(6749,'2024-09-18 11:58:39',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用  \n(字串長度:45)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->(字串長度:0)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\n\n   We will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\n\n   In the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\n\n    For image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\n\n    Through these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2054)```me_desc---->胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。    胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:869)---->*10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\n*Vice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\n*Mentor at NCKU Phoenix Startup Platform.\n*Lecturer on Generative AI at ITRI College of Industry.\n*Recipient of the AI Award for Best Speaker from the Taiwan AI Academy.\n*Co-founder of the International Institute for Innovative Biomedical Technology.\n*Lecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。    胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1498)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/(字串長度:51)'),(6750,'2024-09-18 12:06:53',50,'desc---->(字串長度:0)---->Generative AI is very popular this year, and major software and hardware companies have invested in it.\r\n\r\nThis trend will quickly extend to mobile or edge devices in the future.\r\n\r\nFor example, PC, mobile phone, AR/VR, home products, etc.\r\n\r\nNot only performs LLM model inference, Vision LLM combined with 4K Vision AI will also become mainstream.\r\n\r\nTo perform Vision LLM model inference on mobile or edge devices, and to reduce the font factor of the end product,\r\n\r\nnot only the known problems of memory configuration, computing power, and memory bandwidth,\r\n\r\nhow to solve the overheating problem with the advanced package of COWOS and WOW stacked up will be a more severe challenge.\r\n\r\nThe Inventec Magna™ NPU IP series provides ultra-low power consumption solutions with the highest computing density for Vision LLM, which can systematically solve the above problems.(字串長度:876)'),(6751,'2024-09-18 12:06:54',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用  \n(字串長度:45)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->(字串長度:0)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\n\n   We will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\n\n   In the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\n\n    For image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\n\n    Through these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2054)```me_desc---->胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。    胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:869)---->*10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\n*Vice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\n*Mentor at NCKU Phoenix Startup Platform.\n*Lecturer on Generative AI at ITRI College of Industry.\n*Recipient of the AI Award for Best Speaker from the Taiwan AI Academy.\n*Co-founder of the International Institute for Innovative Biomedical Technology.\n*Lecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。    胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1498)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/(字串長度:51)'),(6752,'2024-09-18 12:09:50',50,'desc---->Generative AI is very popular this year, and major software and hardware companies have invested in it.\r\n\r\nThis trend will quickly extend to mobile or edge devices in the future.\r\n\r\nFor example, PC, mobile phone, AR/VR, home products, etc.\r\n\r\nNot only performs LLM model inference, Vision LLM combined with 4K Vision AI will also become mainstream.\r\n\r\nTo perform Vision LLM model inference on mobile or edge devices, and to reduce the font factor of the end product,\r\n\r\nnot only the known problems of memory configuration, computing power, and memory bandwidth,\r\n\r\nhow to solve the overheating problem with the advanced package of COWOS and WOW stacked up will be a more severe challenge.\r\n\r\nThe Inventec Magna™ NPU IP series provides ultra-low power consumption solutions with the highest computing density for Vision LLM, which can systematically solve the above problems.(字串長度:876)---->Generative AI is very popular this year, and major software and hardware companies have invested in it.\nThis trend will quickly extend to mobile or edge devices in the future.\nFor example, PC, mobile phone, AR/VR, home products, etc.\nNot only performs LLM model inference, Vision LLM combined with 4K Vision AI will also become mainstream.\nTo perform Vision LLM model inference on mobile or edge devices, and to reduce the font factor of the end product,not only the known problems of memory configuration, computing power, and memory bandwidth,how to solve the overheating problem with the advanced package of COWOS and WOW stacked up will be a more severe challenge.\n\nThe Inventec Magna™ NPU IP series provides ultra-low power consumption solutions with the highest computing density for Vision LLM, which can systematically solve the above problems.(字串長度:854)'),(6753,'2024-09-18 12:09:50',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用  \n(字串長度:45)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->(字串長度:0)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\n\n   We will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\n\n   In the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\n\n    For image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\n\n    Through these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2054)```me_desc---->胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。    胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:869)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。    胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1491)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/(字串長度:51)'),(6754,'2024-09-18 12:12:55',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用  \n(字串長度:45)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->(字串長度:0)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\n\n   We will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\n\n   In the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\n\n    For image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\n\n    Through these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2054)```me_desc---->胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。    胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:869)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/(字串長度:51)'),(6755,'2024-09-18 12:14:59',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->(字串長度:0)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\n\n   We will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\n\n   In the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\n\n    For image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\n\n    Through these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2054)```me_desc---->胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。    胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:869)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/(字串長度:51)'),(6756,'2024-09-18 12:16:57',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->(字串長度:0)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\n\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\n\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\n\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\n\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2040)```me_desc---->胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。    胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:869)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74(字串長度:50)'),(6757,'2024-09-18 12:19:54',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\n\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\n\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\n\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\n\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2040)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74(字串長度:50)'),(6758,'2024-09-18 12:23:05',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74(字串長度:50)'),(6759,'2024-09-18 12:35:26',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74(字串長度:50)'),(6760,'2024-09-18 12:36:09',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74(字串長度:50)'),(6761,'2024-09-18 12:41:34',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74(字串長度:50)'),(6762,'2024-09-18 12:42:47',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74(字串長度:50)'),(6763,'2024-09-18 12:45:01',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74(字串長度:50)'),(6764,'2024-09-18 12:50:04',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/   ;   https://www.gvm.com.tw/article/110282(字串長度:95)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/(字串長度:51)'),(6765,'2024-09-18 12:50:58',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)'),(6766,'2024-09-18 12:51:38',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)'),(6767,'2024-09-18 12:51:55',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->(字串長度:0)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/(字串長度:51)'),(6768,'2024-09-18 12:53:08',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->(字串長度:0)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/(字串長度:51)');
INSERT INTO `speakers_changelog` VALUES (6769,'2024-09-18 12:54:20',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1484)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->(字串長度:0)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/(字串長度:51)'),(6770,'2024-09-18 12:58:02',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->(字串長度:0)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->(字串長度:0)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->(字串長度:0)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/(字串長度:51)'),(6771,'2024-09-18 12:58:37',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->(字串長度:0)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1484)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->(字串長度:0)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/(字串長度:51)'),(6772,'2024-09-18 12:59:40',79,'name2---->Siang-Wei Hu(字串長度:12)---->Hsiang-Wei Hu(字串長度:13)```anchor---->siang-wei-hu(字串長度:12)---->hsiang-wei-hu(字串長度:13)```talk_title---->(字串長度:0)---->Advancing Medical Innovation with Multimodal Edge Computing Technologies(字串長度:72)```talk_title2---->多模態邊緣運算於醫療創新應用  \r\n(字串長度:46)---->多模態邊緣運算於醫療創新應用(字串長度:42)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->工研院資通所   (字串長度:21)---->Industrial Technology Research Institute(字串長度:40)```position---->智慧運算組 博士(字串長度:22)---->(字串長度:0)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\n\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\n\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\n\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\n\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2048)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1484)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->(字串長度:0)---->https://www.linkedin.com/in/hsiang-wei-hu-19b23b74/(字串長度:51)'),(6773,'2024-09-18 13:03:56',25,'me_desc---->(字串長度:0)---->Prof. Yuh-Jye Lee is a computer scientist who holds a PhD in Computer Sciences from UW-Madison (2001). He has held positions at esteemed universities, including National Chun Cheng University, Taiwan Tech, and National Yang Ming Chiao Tung University.\n\nProf. Lee also had the opportunity to serve the government from November 2020 to January 2023 as the Deputy Executive Secretary of the Office of Science and Technology Policy, National Science and Technology Council. This office is responsible for Taiwan\'s science and technology policies, budget allocation, and the review of national-level science and technology projects.  He serves as the Chief Executive Officer of the Taiwan Information Security Center at the Research Center for Information Technology Innovation (CITI), Academia Sinica in the period of Aug. 2023 to Feb. 2024 and Sept. 2018 to Feb. 2021. He is a research fellow at CITI since Aug. 2023.\n\nProf. Lee\'s research primarily focuses on AI, data science, machine learning, federated learning, and information security. In essence, his research is centered on AI in security and security in AI. He is also in charge of the TAIDE project, which stands for Trustworthy AI Dialogue Engine.\n(字串長度:1207)'),(6774,'2024-09-18 13:03:57',79,'position---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Divi(字串長度:100)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2040)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1491)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)'),(6775,'2024-09-18 13:05:18',79,'position---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Divi(字串長度:100)---->Information and Communications Research Laboratories(ICL), Computational Intelligent Technology Division, Engineer (字串長度:115)```desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2040)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1491)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)'),(6776,'2024-09-18 16:39:27',8,'talk_title---->(字串長度:0)---->Intelligent Computing X Edge Al in Driving Innovation in the Al Era(字串長度:67)```can_playback---->(字串長度:0)---->V(字串長度:1)```position---->President, Taiwan & SEA\r\nAustralia, New Zealand\r\nVice President(字串長度:63)---->Vice President of Qualcomm Technologies, Inc. and the President of Qualcomm Taiwan and South East Asia, Australia, New Zealand(字串長度:126)```desc---->(字串長度:0)---->Generative AI applications are quickly transforming into indispensable tools for individuals and businesses alike, with an estimated annual economic impact of $2.6T–4.4T. With more than 15 years of investment in AI research and development, Qualcomm stands at the forefront of on-device AI innovation. In this session, we will explore how AI is being enabled across various segments and showcase how Qualcomm empowers developers to drive the next generation of AI-driven advancements.(字串長度:486)```me_desc---->劉思泰先生是高通副總裁暨台灣、東南亞與紐澳區總裁。出生於馬來西亞，他曾在馬來西亞、新加坡和英國接受教育。\n劉思泰負責高通在此區域的所有業務和營運。在此之前，他擔任高通公司副總裁暨台灣區總裁。\n劉思泰在通訊產業擁有三十年以上領導業務和研發的資歷，曾任宏碁股份有限公司的智慧產品事業總裁，並在瑞士和台灣居住期間，負責針對全球市場、非PC產品線的研發和銷售。\n加入宏碁之前，劉思泰在摩托羅拉（Motorola）公司任職超過十八年，帶領區域研發團隊，後來也負責全球產品規劃和業務團隊，產品涵蓋行動無線電、呼叫器、數據終端設備到蜂巢式無線通訊產品。任職期間，他曾居住在中國、韓國、美國和印度。\n劉思泰擁有新加坡國立大學工商管理碩士學位，及英國里茲大學電機及電子工程學位。(字串長度:962)---->ST Liew is a Vice President of Qualcomm Technologies, Inc. and the President of Qualcomm Taiwan and South East Asia, Australia, New Zealand. ST was born in Malaysia and was educated in Malaysia, Singapore and the UK. His permanent home is Singapore.\r\n\r\nIn this role, ST is responsible for leading all business and operational functions for Qualcomm in the region. Prior to this role, ST served as the Vice President and President of Qualcomm Taiwan. \r\n\r\nST has more than 30 years of experience leading businesses and R&D in the telecommunication industry. Most recently ST was the President of Acer’s new Business Group SPBG that focused on R&D and Sales of non PC lines of product for the global market while living in Switzerland and later in Taiwan. \r\n\r\nBefore joining Acer, ST was in Motorola for over 18 years leading Regional R&D Teams and later in Product planning, business Teams across the world in products ranging from Mobile radios, Pagers, Data terminals to Cellular phones. ST has lived in China, Korea, USA and India during his tenure. \r\n\r\nLiew received his MBA from the National University of Singapore, and holds a BSc. in Electrical and Electronic Engineering from the University of Leeds, UK.(字串長度:1213)```talkfile_link---->(字串長度:0)---->X(字串長度:1)'),(6777,'2024-09-18 16:39:27',10,'talk_title---->(字串長度:0)---->資通訊業AI 2.0之布局與實踐(字串長度:36)```talk_title2---->(字串長度:0)---->資通訊業AI 2.0之布局與實踐(字串長度:36)'),(6778,'2024-09-18 16:39:28',53,'talk_title---->(字串長度:0)---->From Silicon Design to Software Stack: AMD AI Strategy(字串長度:54)```desc---->(字串長度:0)---->AMD not only has the broadest AI product portfolio but also leads in silicon design and packaging technologies. AMD pioneers chiplet modular design and 3.5D packaging in AI Accelerator. Together with Open and Proven software/hardware stack, AMD aims to advance the AI development with the ecosystem through deep collaboration.(字串長度:326)'),(6779,'2024-09-18 16:39:28',79,'desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\r\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\r\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\r\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\r\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2040)---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)```me_desc---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\r\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\r\nMentor at NCKU Phoenix Startup Platform.\r\nLecturer on Generative AI at ITRI College of Industry.\r\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\r\nCo-founder of the International Institute for Innovative Biomedical Technology.\r\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\r\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1494)---->10 years of AI research and commercialization experience, specializing in time series forecasting, multimodal language models, 2D/3D image generation and segmentation, and knowledge graphs.\nVice Chair and Supervisor of the Continuing Education and Exchange Committee, Taiwan AI Academy.\nMentor at NCKU Phoenix Startup Platform.\nLecturer on Generative AI at ITRI College of Industry.\nRecipient of the AI Award for Best Speaker from the Taiwan AI Academy.\nCo-founder of the International Institute for Innovative Biomedical Technology.\nLecturer at New York University, Taipei Medical University, and Mackay Medical College.\n胡翔崴博士為 H.I.T. for Asia、翔安生醫共同創辦人及成大鳳凰新創導師，致力於AI 技術研究10年，曾擔任多間新創技術顧問，擁有多項發明專利、發表超過20篇國際研討會與期刊論文。   現任職於工研院資通所，專注生成式AI技術研發，文字、語音、3D影像、及化合物生成等多模態大型語言模型開發，主要應用於醫療及金融領域，目前已有十多項產品專案與多家機構合作，研發成果協助工研院資通所智慧運算組榮獲台灣人工智慧協會2024 AI Award Best Solutions 特優賞，個人演講能力獲得 Best Speaker 優秀賞。胡翔崴博士以AI技術落地應用共創產業價值為願景，所領導的HIT社群以共學模式每年培育100位生成式 AI 人才為目標，期許能為台灣各領域提升國際競爭力。(字串長度:1487)'),(6780,'2024-09-18 16:39:28',94,'talk_title2---->(字串長度:0)---->討論: 一起建立好的資料基礎建設(字串長度:44)'),(6781,'2024-09-19 09:11:53',50,'talk_title---->Water-Cooling-free.  How to fulfill ultra low energy consumption NPU IP for ptivate Large Language Model(LLM)\r\n(字串長度:111)---->Water-Cooling-free.  How to fulfill ultra low energy consumption NPU IP for ptivate Large Language Model(LLM)\n(字串長度:110)```desc---->Generative AI is very popular this year, and major software and hardware companies have invested in it.\r\nThis trend will quickly extend to mobile or edge devices in the future.\r\nFor example, PC, mobile phone, AR/VR, home products, etc.\r\nNot only performs LLM model inference, Vision LLM combined with 4K Vision AI will also become mainstream.\r\nTo perform Vision LLM model inference on mobile or edge devices, and to reduce the font factor of the end product,not only the known problems of memory configuration, computing power, and memory bandwidth,how to solve the overheating problem with the advanced package of COWOS and WOW stacked up will be a more severe challenge.\r\n\r\nThe Inventec Magna™ NPU IP series provides ultra-low power consumption solutions with the highest computing density for Vision LLM, which can systematically solve the above problems.(字串長度:860)---->Generative AI is very popular this year, and major software and hardware companies have invested in it.\nThis trend will quickly extend to mobile or edge devices in the future.\nFor example, PC, mobile phone, AR/VR, home products, etc.\nNot only performs LLM model inference, Vision LLM combined with 4K Vision AI will also become mainstream.\nTo perform Vision LLM model inference on mobile or edge devices, and to reduce the font factor of the end product,not only the known problems of memory configuration, computing power, and memory bandwidth,how to solve the overheating problem with the advanced package of COWOS and WOW stacked up will be a more severe challenge.\n\nThe Inventec Magna™ NPU IP series provides ultra-low power consumption solutions with the highest computing density for Vision LLM, which can systematically solve the above problems.(字串長度:854)```me_desc---->Graduated from the Institute of Electrical Engineering of National Chiao Tung University. His research topic is the neural network application of Cerebellar Model Articulation Controller (CMAC) in robot arm control. He is currently the head of the AI ​​NPU IP design team of Inventec AI Research Center. His main expertise is in digital signal processing IP, image processing IP, neural network processor IP, etc., and he has in-depth research on ultra-low energy consumption design of large language models (LLM).\r\n畢業於國立交通大學電機工程研究所，研究題目為人類小腦模型類神經網路(CMAC) 於機器手臂控制的應用。 現為英業達 AI 研究中心 AI NPU IP晶片設計團隊負責人，主要專長在數位信號處理 IP、影像處理 IP、類神經網路處理器 IP 等方面，並對大型語言模型(LLM)超低能耗設計有深入研究。(字串長度:893)---->Graduated from the Institute of Electrical Engineering of National Chiao Tung University. His research topic is the neural network application of Cerebellar Model Articulation Controller (CMAC) in robot arm control. He is currently the head of the AI ​​NPU IP design team of Inventec AI Research Center. His main expertise is in digital signal processing IP, image processing IP, neural network processor IP, etc., and he has in-depth research on ultra-low energy consumption design of large language models (LLM).\n畢業於國立交通大學電機工程研究所，研究題目為人類小腦模型類神經網路(CMAC) 於機器手臂控制的應用。 現為英業達 AI 研究中心 AI NPU IP晶片設計團隊負責人，主要專長在數位信號處理 IP、影像處理 IP、類神經網路處理器 IP 等方面，並對大型語言模型(LLM)超低能耗設計有深入研究。(字串長度:892)'),(6782,'2024-09-19 09:11:53',92,'can_playback---->(字串長度:0)---->V(字串長度:1)'),(6783,'2024-09-19 09:11:53',94,'can_playback---->(字串長度:0)---->V(字串長度:1)'),(6784,'2024-09-19 09:37:20',27,'name2---->(字串長度:0)---->Wen-hsi  Chang(字串長度:14)```anchor---->speaker-27(字串長度:10)---->wen-hsi--chang(字串長度:14)```talk_title---->(字串長度:0)---->Sharing the Experience of Business Transformation Using Artificial Intelligence in the Ministry of Finance.\r(字串長度:108)```talk_title2---->(字串長度:0)---->財政部使用人工智慧進行業務轉型經驗分享(字串長度:57)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->財政部財政資訊中心(字串長度:27)---->Fiscal Information Agency, Ministry of Finance(字串長度:46)```position---->主任(字串長度:6)---->Director-general(字串長度:16)```me_desc---->(字串長度:0)---->(中文)財政部財政資訊中心主任張文熙博士，長期積極研究人工智慧、導入稅務機器人流程自動化、稅務資訊系統優化和規劃次世代稅務行政資訊系統。張主任創設結人工智慧機器新創實驗室，全力投入AI應用研究擴大服務量能，結合學習和大數據等技術進行工作流程分析，建立納稅人異常行為偵測模型。張主任具備資訊系統博士，並同時擁有工業工程學和資訊管理碩士學位。其專長和研究興趣以機器學習應用、數據分析、大型資訊系統規劃等。\n(英文)Dr. Wen-Hsi Chang is the Director-General, Fiscal Information Agency, Ministry of Finance, Taiwan and has been actively involved in artificial intelligence and robotic process automation on taxation process, tax information system optimization and planning a next-generation tax administration information system. Dr. Chang launched an innovation laboratory that integrates machine learning and big data analytics into existing workflows such as detection models on identifying taxpayers’ abnormal behavior. He received his Ph.D. degree in management science on information system management. He also obtained a MS degree in industrial engineering and a MBA degree in information management. His professional expertise and research interests include machine learning application, data analysis, and large-scale information systems planning.\n(字串長度:1444)```talkfile_link---->(字串長度:0)---->V(字串長度:1)'),(6785,'2024-09-19 09:37:20',71,'company---->(字串長度:0)---->台灣人工智慧學校(字串長度:24)```position---->(字串長度:0)---->產學長(字串長度:9)'),(6786,'2024-09-19 09:58:29',5,'can_playback---->(字串長度:0)---->V(字串長度:1)```company---->中華民國對外貿易發展協會(字串長度:36)---->James C. F. Huang(字串長度:17)```position---->董事長(字串長度:9)---->Taiwan External Trade Development Council (TAITRA)(字串長度:50)```company2---->(字串長度:0)---->Chairman(字串長度:8)```me_desc---->(字串長度:0)---->James C. F. Huang is the incumbent chairman of the Taiwan External Trade Development Council (TAITRA), an apex body of business and trade promotion organization in Taiwan.Chairman Huang is a dynamic, charismatic and vibrant leader, who is also a distinguished entrepreneur and former diplomat dedicated to promoting Taiwan\'s economic development and international relations.\nPrior to joining TAITRA, Chairman Huang held various senior positions in the government including Minister of Foreign Affairs. He also held several senior consultant and management positions at Taiwan\'s leading technology companies. He has served in the public and private sectors for more than 30 years, accumulated extensive experience in formulating strategy and outstanding negotiation skills.\nBeginning his tenure in TAITRA in 2017, Chairman Huang has initiated several reforms within the organization, including flattening organizational structure, encourage people-to-people interactions and digital transformation. He is also actively involved in helping Taiwan’s business community building international networks by leading high profile delegations to participate in major trade, business and investment events around the world.(字串長度:1215)```talkfile_link---->(字串長度:0)---->X(字串長度:1)'),(6787,'2024-09-19 09:58:29',27,'name2---->Wen-hsi  Chang(字串長度:14)---->Wen-hsi Chang(字串長度:13)```anchor---->wen-hsi--chang(字串長度:14)---->wen-hsi-chang(字串長度:13)'),(6788,'2024-09-19 10:03:34',103,'me_desc---->(字串長度:0)---->律師，專注於創新科技、開放資料、開放政府等數位治理議題及相關法律規範。2012 年以來持續參與 g0v 公民科技社群，從中習得「揪團打怪」技能：多方協作推動社會創新。\r\n\r\n2017 年協助設立台灣人工智慧學校基金會，2021 年起擔任秘書長，推動課程轉型及開源 AI 平台「風火輪計畫」。\r\n\r\n2020-2022 年擔任立法院開放國會委員會民間委員，推動完成台灣立法院首份「開放國會行動方案」。近來與台灣公民社群夥伴協作推動「數位公民素養」行動計畫作為台灣新世代社會基礎建設。(字串長度:636)'),(6789,'2024-09-19 10:03:34',15,'me_desc---->(字串長度:0)---->律師，專注於創新科技、開放資料、開放政府等數位治理議題及相關法律規範。2012 年以來持續參與 g0v 公民科技社群，從中習得「揪團打怪」技能：多方協作推動社會創新。\r\n\r\n2017 年協助設立台灣人工智慧學校基金會，2021 年起擔任秘書長，推動課程轉型及開源 AI 平台「風火輪計畫」。\r\n\r\n2020-2022 年擔任立法院開放國會委員會民間委員，推動完成台灣立法院首份「開放國會行動方案」。近來與台灣公民社群夥伴協作推動「數位公民素養」行動計畫作為台灣新世代社會基礎建設。(字串長度:636)'),(6790,'2024-09-19 10:07:18',31,'me_desc---->(字串長度:0)---->Richie Tsai is the provost of Taiwan AI Academy and takes overall responsibility for the academy\'s AI talent traini 資深協理 ng, strategy on developing consulting services, startup coaching, technology promotion, community management, industry-university bridging, and career development. Prior to joining Taiwan AI Academy, Richie was Senior Director at SAP Greater China, ever worked for Oracle and Teradata where he helped leading enterprises to navigate their digital transformation. He graduated from Fu Jen Catholic University in 1996 and received his MBA degree from San Diego State Univeristy in 2003. He is pursuitng PhD degree in National Tsing Huwa University.\r\n\r\n學歷：清華大學跨院國際博士班 - 智慧製造與數位決策\r\n\r\n現職：台灣人工智慧學校 校務長 / 國科會 TAIWAN AICoE 策略委員會委員 / 台灣氣候聯盟 - 綠色與數位科技委員會召集人\r\n\r\n專長：AI, Big Data, 數位轉型, SaaS\r\n\r\n經歷：20 多年經驗在資訊工程、產品管理、營運、市場 行銷、生態系統培育\r\n\r\nSAP 德商思愛普 大中華區資深副總經理\r\nOracle 美商甲骨文 應用軟體部副總經理\r\nTeradata 美商天睿資訊系統 資深協理(字串長度:1211)'),(6791,'2024-09-24 08:46:12',4,'desc---->(字串長度:0)---->簡報通常都最後一刻才產出(字串長度:36)'),(6792,'2024-09-24 08:46:12',5,'name2---->(字串長度:0)---->James C. F. Huang(字串長度:17)```anchor---->speaker-5(字串長度:9)---->james-c-f-huang(字串長度:15)```talk_title---->(字串長度:0)---->AI and the Changes of the Century(字串長度:33)```talk_title2---->(字串長度:0)---->全球變局與人工智慧(字串長度:27)```company---->James C. F. Huang(字串長度:17)---->Taiwan External Trade Development Council (TAITRA)(字串長度:50)```position---->Taiwan External Trade Development Council (TAITRA)(字串長度:50)---->Chairman(字串長度:8)```company2---->Chairman(字串長度:8)---->(字串長度:0)'),(6793,'2024-09-24 08:46:12',8,'position_for_agenda---->副總裁台灣、東南亞與紐澳區總裁(字串長度:45)---->副總裁暨台灣、東南亞與紐澳區總裁(字串長度:48)'),(6794,'2024-09-24 08:46:12',13,'name2---->(字串長度:0)---->Wen-Yuh Jywe(字串長度:12)```anchor---->speaker-13(字串長度:10)---->wen-yuh-jywe(字串長度:12)```talk_title---->(字串長度:0)---->The Deepening of AI Applications and Talent Cultivation in the Manufacturing Industry(字串長度:85)```talk_title2---->(字串長度:0)---->製造產業的AI應用與人才培育深化(字串長度:44)```can_playback---->(字串長度:0)---->X(字串長度:1)```company---->國立臺灣大學機械工程學系(字串長度:36)---->National Taiwan University(字串長度:26)```position---->終身特聘教授(字串長度:18)---->Department of Mechanical Engineering/Lifetime Distinguished Professor(字串長度:69)```desc---->(字串長度:0)---->In Taiwan, the integration of AI in the manufacturing industry is rapidly advancing, driven by the need for increased efficiency, automation, and competitiveness in the global market. Companies are leveraging AI technologies such as predictive maintenance, quality control, and intelligent automation to optimize production processes. However, the demand for skilled talent capable of developing and applying AI solutions remains a challenge. Taiwan is addressing this by enhancing talent cultivation programs, particularly in universities and technical institutes, to meet the industry’s needs. This includes fostering interdisciplinary skills that combine AI expertise with manufacturing knowledge, ensuring that Taiwan remains at the forefront of smart manufacturing.\r\n\r\n在台灣，製造業中人工智慧的整合正在迅速推進，主要是由提高效率、自動化和全球市場競爭力的需求所驅動。企業正在利用AI技術，如預測性維護、質量控制和智能自動化來優化生產流程。然而，能夠開發和應用AI解決方案的技能人才需求仍然是一大挑戰。台灣正通過加強人才培育計劃，特別是在大學和技術學院，來應對這一挑戰。這包括培養結合AI專業知識與製造業知識的跨學科技能，確保台灣在智慧製造領域保持領先地位。(字串長度:1346)```me_desc---->(字串長度:0)---->The Lifetime Distinguished Professor of National Taiwan University (NTU), Dr. Wen-Yuh Jywe, is a specialist in optical precision measurement, machine tool calibration measurement, precise positioning stage design, and implementation of IIOT. During his doctoral study in University of Manchester Institute of Science and Technology (UMIST), he developed a measurement system for CNC machine tools based on Ball-Bar technology, which has now marketed worldwide. He has served in NFU as the President, the dean of Engineering College, the dean of Research and Development and, Secretary-general, etc. He organized a research team in which members were composed of over 20 professors and 40 full-time engineers, and is running many intelligent machine plans founded by Taiwan Government in recent years. The research results have now affected the domestic and international industries. He is author and co-author of more than 250 publications as journal and conference proceeding papers. (字串長度:985)```talkfile_link---->(字串長度:0)---->X(字串長度:1)```website---->(字串長度:0)---->http://www.me.ntu.edu.tw/main.php?mod=adv_custom_page&func=show_page&site_id=0&page_id=299(字串長度:90)'),(6795,'2024-09-24 08:46:12',14,'talk_title---->(字串長度:0)---->Boost Productivity & Innovation – MediaTek DaVinci GenAI Platform(字串長度:67)```talk_title2---->(字串長度:0)---->從個人生產力到企業競爭力 - MediaTek DaVinci GenAI Platform(字串長度:70)```desc---->(字串長度:0)---->MediaTek DaVinci, based on MediaTek\'s Generative AI Service Framework (GAISF), was initially developed as a data-secure, productivity-enhancing generative AI tool for the MediaTek group\'s internal use. It has been widely used by various departments, and has evolved into a generative AI service platform that can be imported by external enterprises, and a rich set of ecosystem.\r\n\r\nDozens of companies from high-tech, financial, telecom, legal, manufacturing, sales, service, system integration, and cloud services industries, as well as educational institutions and start-ups, have already joined the MediaTek Davinci ecosystem.\r\n\r\nMediaTek Davinci provides a highly integrated and scalable open platform that allows developers to create customized generative AI extension plug-ins and assistants for various industries, while enterprises can make good use of the platform\'s rich tools to improve productivity and competitiveness in a data-secure environment.\r\n\r(字串長度:963)'),(6796,'2024-09-24 08:46:12',103,'talk_title---->(字串長度:0)---->以人為本的 AI 治理 -- 目標、現實與想像 2024(字串長度:58)'),(6797,'2024-09-24 08:46:12',22,'name---->吳誠文(字串長度:9)---->簡立峰(字串長度:9)```name2---->(字串長度:0)---->Lee-Feng Chien(字串長度:14)```anchor---->speaker-22(字串長度:10)---->lee-feng-chien(字串長度:14)```can_playback---->(字串長度:0)---->V(字串長度:1)```company---->國家科學及技術委員會(字串長度:30)---->Appier(字串長度:6)```position---->主任委員(字串長度:12)---->Independent Director (字串長度:21)```me_desc---->(字串長度:0)---->Dr. Lee-Feng Chien has long been focusing on the development of software technology and innovation. From 2006 to 2020, he served as the first Managing Director of Google Taiwan and successfully transformed it into the largest R&D center in Asia. Post-retirement in 2020, Dr. Chien has been actively engaged in Taiwan\'s entrepreneurial  ecosystem, serving on the boards of AI startups such as Appier and iKala. Notably, Appier was successfully listed in Japan and became Taiwan’s first AI unicorn. He also co-founded an angel fund, and mentors numerous startups, as well as serving as a member of the Executive Yuan\'s Economic Development Committee. Dr. Chien is also deeply involved in academia. As a pioneer in Chinese natural language processing and AI, he obtained his Ph.D. from NTU CSIE in 1991 for his research on Chinese language models. Between 1993 and 2006, he was a research fellow at the Institute of Information Science, Academia Sinica, while concurrently teaching at the Department of Information Management at National Taiwan University from 2001 to 2006. (字串長度:1074)```talkfile_link---->(字串長度:0)---->V(字串長度:1)'),(6798,'2024-09-24 08:46:13',27,'talk_title---->Sharing the Experience of Business Transformation Using Artificial Intelligence in the Ministry of Finance.\r(字串長度:108)---->Sharing the Experience of Business Transformation Using Artificial Intelligence in the Ministry of Finance.\n(字串長度:108)'),(6799,'2024-09-24 08:46:13',71,'talk_title---->Close Talk(字串長度:10)---->用AI強大台灣、均衡台灣: 讓人才成為擴展與加深影響力的引擎(字串長度:82)'),(6800,'2024-09-24 08:46:13',39,'desc---->(字串長度:0)---->AI for all applications: value chain sustains supply chain\r\n\r\nAIoT, AI + IoT, not AI + Internet, for Taiwan penetrating global market\r\n\r\nBig data for AI, open standards for big data\r\n\r\nAIoT Digital Transformation enables trillion $ IoT market\r\n\r\nKiller AI application + Free Downloads: Booming IoT Ecosystems\r\n\r\n (字串長度:313)'),(6801,'2024-09-24 08:46:13',43,'position_for_agenda---->產品顧問\n(字串長度:13)---->產品顧問(字串長度:12)'),(6802,'2024-09-24 08:46:13',45,'desc---->For individual users, as the leader in high-performance Gaming PCs, MSI is integrating AI technology into its innovations, such as a self-developed small language model preloaded in MSI AI PCs. This model supports offline generative AI capabilities, addressing user needs for data privacy protection and real-time responsiveness.(字串長度:329)---->AI is leading a revolution in music streaming, reshaping user experiences and industry dynamics. This session will examine how AI innovations drive personalized recommendations, influence content distribution, and open new paths for engagement. Discover how AI is setting the stage for the future of music streaming.\n\nAI 正在引領音樂串流的革命，重塑用戶體驗和產業動態。本次演講將探討 AI 創新如何推動個性化推薦、影響內容分發並開創新的互動方式。了解 AI 如何為音樂串流的未來鋪路。(字串長度:551)'),(6803,'2024-09-24 08:46:13',52,'talk_title---->(字串長度:0)---->Building the AI Compute Platform of the Future(字串長度:46)```talk_title2---->(字串長度:0)---->建構未來的人工智慧運算平台(字串長度:39)```desc---->(字串長度:0)---->The demand for AI has been growing rapidly across various sectors, driven by its potential to revolutionize industries and enhance efficiency, decision-making, and innovation. Computing is a foundational requirement for AI as it provides the hardware and software infrastructure necessary for process data, perform calculations, and run complex algorithms.  Arm’s energy efficient foundations and pervasive footprint from cloud to edge, make us the AI platform the world can rely on, both today and in the future. In this presentation, we will share Arm’s vision and technologies building for AI world.(字串長度:606)```me_desc---->(字串長度:0)---->David Hsu  \r\nFAE Director, Arm Taiwan \r\nDavid Hsu is FAE Director of Arm, leading FAE team of Taiwan for closer \r\ncollaboration and communications internally and externally to accomplish \r\npartners and customers solutions. He has over 20 years of professional \r\nexperience in the semiconductor industry. \r\nDavid Hsu joined Arm in 2011 as a CPU FAE as well as responsible for product \r\nmarketing and sales project management and then left in 2017. After then, he \r\njoined Intelligo, a start-up invested by MediaTek, leading the business \r\ndevelopment team to explore opportunities in Japan, Korea, and US. In 2019 \r\nDavid was recruited by Arm again as FAE director to drive Arm’s IP solutions \r\nadoption.  \r\nIn addition to Arm, David worked at Faraday, Alpha Imaging Technology \r\n(MediaTek subsidiary) and BenQ, focusing on IC design work ranging from \r\narchitecture definition, RTL coding, front-end/back-end implementation to IC \r\nverification and testing. He has rich experience in pre-sales strategy set-up \r\nand sales enablement.  \r\nDavid received a master’s degree in Computer Science and Engineering at \r\nNational Chiao Tung University.  \r\n徐達勇   \r\n  \r\nArm 應用工程總監 \r\n徐達勇目前為Arm應用工程總監，帶導台灣FAE團隊與內部跨部門和外部更\r\n密切地合作與溝通，以完成合作夥伴與客戶所需的解決方案。 他在半導體產\r\n業擁有超過20年的專業經驗。 \r\n徐達勇於2011年加入Arm並擔任CPU應用工程師，同時也負責產品行銷和銷\r\n售專案管理。於2017年離開Arm之後，他加入聯發科轉投資的新創公司\r\nIntelligo，帶領業務團隊成功開發日本、韓國與美國市場。2019年他再次被邀\r\n請回到Arm擔任應用工程總監，致力推動並協助客戶採用Arm IP解決方案。 \r\n除了Arm之外，他也曾經任職於Faraday、聯發科分公司Alpha Imaging \r\nTechnology 和 BenQ，專注從事在IC設計相關工作，從架構的定義、RTL編\r\n碼、前端/後端執行，以及到IC驗證與測試。在售前策略擬定和推動銷售擁有\r\n豐富的經驗。 \r\n徐達勇擁有國立交通大學電腦科學工程學系碩士學位。(字串長度:2175)'),(6804,'2024-09-24 08:46:13',57,'position_for_agenda---->(字串長度:0)---->醫療長(字串長度:9)'),(6805,'2024-09-24 09:45:31',3,'name---->卓榮泰(字串長度:9)---->蕭美琴(字串長度:9)```name2---->(字串長度:0)---->Bi-khim Hsiao(字串長度:13)```anchor---->speaker-3(字串長度:9)---->bi-khim-hsiao(字串長度:13)```company---->行政院(字串長度:9)---->Taiwan (R.O.C.)(字串長度:15)```position---->院長(字串長度:6)---->Vice President(字串長度:14)```company_for_agenda---->行政院(字串長度:9)---->R.O.C.(字串長度:6)```position_for_agenda---->院長(字串長度:6)---->副總統(字串長度:9)'),(6806,'2024-09-24 09:45:31',4,'desc---->簡報通常都最後一刻才產出(字串長度:36)---->(字串長度:0)'),(6807,'2024-09-24 09:45:31',5,'me_desc---->James C. F. Huang is the incumbent chairman of the Taiwan External Trade Development Council (TAITRA), an apex body of business and trade promotion organization in Taiwan.Chairman Huang is a dynamic, charismatic and vibrant leader, who is also a distinguished entrepreneur and former diplomat dedicated to promoting Taiwan\'s economic development and international relations.\r\nPrior to joining TAITRA, Chairman Huang held various senior positions in the government including Minister of Foreign Affairs. He also held several senior consultant and management positions at Taiwan\'s leading technology companies. He has served in the public and private sectors for more than 30 years, accumulated extensive experience in formulating strategy and outstanding negotiation skills.\r\nBeginning his tenure in TAITRA in 2017, Chairman Huang has initiated several reforms within the organization, including flattening organizational structure, encourage people-to-people interactions and digital transformation. He is also actively involved in helping Taiwan’s business community building international networks by leading high profile delegations to participate in major trade, business and investment events around the world.(字串長度:1217)---->James C. F. Huang is the incumbent chairman of the Taiwan External Trade Development Council (TAITRA), an apex body of business and trade promotion organization in Taiwan.Chairman Huang is a dynamic, charismatic and vibrant leader, who is also a distinguished entrepreneur and former diplomat dedicated to promoting Taiwan\'s economic development and international relations.\nPrior to joining TAITRA, Chairman Huang held various senior positions in the government including Minister of Foreign Affairs. He also held several senior consultant and management positions at Taiwan\'s leading technology companies. He has served in the public and private sectors for more than 30 years, accumulated extensive experience in formulating strategy and outstanding negotiation skills.\nBeginning his tenure in TAITRA in 2017, Chairman Huang has initiated several reforms within the organization, including flattening organizational structure, encourage people-to-people interactions and digital transformation. He is also actively involved in helping Taiwan’s business community building international networks by leading high profile delegations to participate in major trade, business and investment events around the world.(字串長度:1215)'),(6808,'2024-09-24 09:45:31',13,'desc---->In Taiwan, the integration of AI in the manufacturing industry is rapidly advancing, driven by the need for increased efficiency, automation, and competitiveness in the global market. Companies are leveraging AI technologies such as predictive maintenance, quality control, and intelligent automation to optimize production processes. However, the demand for skilled talent capable of developing and applying AI solutions remains a challenge. Taiwan is addressing this by enhancing talent cultivation programs, particularly in universities and technical institutes, to meet the industry’s needs. This includes fostering interdisciplinary skills that combine AI expertise with manufacturing knowledge, ensuring that Taiwan remains at the forefront of smart manufacturing.\r\n\r\n在台灣，製造業中人工智慧的整合正在迅速推進，主要是由提高效率、自動化和全球市場競爭力的需求所驅動。企業正在利用AI技術，如預測性維護、質量控制和智能自動化來優化生產流程。然而，能夠開發和應用AI解決方案的技能人才需求仍然是一大挑戰。台灣正通過加強人才培育計劃，特別是在大學和技術學院，來應對這一挑戰。這包括培養結合AI專業知識與製造業知識的跨學科技能，確保台灣在智慧製造領域保持領先地位。(字串長度:1346)---->In Taiwan, the integration of AI in the manufacturing industry is rapidly advancing, driven by the need for increased efficiency, automation, and competitiveness in the global market. Companies are leveraging AI technologies such as predictive maintenance, quality control, and intelligent automation to optimize production processes. However, the demand for skilled talent capable of developing and applying AI solutions remains a challenge. Taiwan is addressing this by enhancing talent cultivation programs, particularly in universities and technical institutes, to meet the industry’s needs. This includes fostering interdisciplinary skills that combine AI expertise with manufacturing knowledge, ensuring that Taiwan remains at the forefront of smart manufacturing.\n\n在台灣，製造業中人工智慧的整合正在迅速推進，主要是由提高效率、自動化和全球市場競爭力的需求所驅動。企業正在利用AI技術，如預測性維護、質量控制和智能自動化來優化生產流程。然而，能夠開發和應用AI解決方案的技能人才需求仍然是一大挑戰。台灣正通過加強人才培育計劃，特別是在大學和技術學院，來應對這一挑戰。這包括培養結合AI專業知識與製造業知識的跨學科技能，確保台灣在智慧製造領域保持領先地位。(字串長度:1344)'),(6809,'2024-09-24 09:45:31',14,'desc---->MediaTek DaVinci, based on MediaTek\'s Generative AI Service Framework (GAISF), was initially developed as a data-secure, productivity-enhancing generative AI tool for the MediaTek group\'s internal use. It has been widely used by various departments, and has evolved into a generative AI service platform that can be imported by external enterprises, and a rich set of ecosystem.\r\n\r\nDozens of companies from high-tech, financial, telecom, legal, manufacturing, sales, service, system integration, and cloud services industries, as well as educational institutions and start-ups, have already joined the MediaTek Davinci ecosystem.\r\n\r\nMediaTek Davinci provides a highly integrated and scalable open platform that allows developers to create customized generative AI extension plug-ins and assistants for various industries, while enterprises can make good use of the platform\'s rich tools to improve productivity and competitiveness in a data-secure environment.\r\n\r\n(字串長度:964)---->MediaTek DaVinci, based on MediaTek\'s Generative AI Service Framework (GAISF), was initially developed as a data-secure, productivity-enhancing generative AI tool for the MediaTek group\'s internal use. It has been widely used by various departments, and has evolved into a generative AI service platform that can be imported by external enterprises, and a rich set of ecosystem.\n\nDozens of companies from high-tech, financial, telecom, legal, manufacturing, sales, service, system integration, and cloud services industries, as well as educational institutions and start-ups, have already joined the MediaTek Davinci ecosystem.\n\nMediaTek Davinci provides a highly integrated and scalable open platform that allows developers to create customized generative AI extension plug-ins and assistants for various industries, while enterprises can make good use of the platform\'s rich tools to improve productivity and competitiveness in a data-secure environment.\n\n(字串長度:958)```me_desc---->Jason Yeh is the head of AIDE (AI & Data Engineering) at MediaTek. AIDE\'s mission is to empower BU/FU to adopt AI to boost productivity.\r\nHe has 27 years of experience in semiconductor IT, big data and AI. Prior to joining MediaTek, he worked for TSMC. He received his MBA degree in Management Information System from National Chengchi University, Taiwan.\r\n\r\n葉家順是聯發科技人工智慧暨數據工程主管，負責協助各單位導入AI以提升生產力。\r\n他在半導體產業的IT、大數據、人工智慧等領域有27年的工作經驗。在加入聯發科技之前，他在台積電工作。他畢業於台灣政治大學資訊管理研究所碩士。\r\n\r\n(字串長度:677)---->Jason Yeh is the head of AIDE (AI & Data Engineering) at MediaTek. AIDE\'s mission is to empower BU/FU to adopt AI to boost productivity.\nHe has 27 years of experience in semiconductor IT, big data and AI. Prior to joining MediaTek, he worked for TSMC. He received his MBA degree in Management Information System from National Chengchi University, Taiwan.\n\n葉家順是聯發科技人工智慧暨數據工程主管，負責協助各單位導入AI以提升生產力。\n他在半導體產業的IT、大數據、人工智慧等領域有27年的工作經驗。在加入聯發科技之前，他在台積電工作。他畢業於台灣政治大學資訊管理研究所碩士。\n\n(字串長度:671)'),(6810,'2024-09-24 09:45:32',27,'talk_title---->Sharing the Experience of Business Transformation Using Artificial Intelligence in the Ministry of Finance.\r\n(字串長度:109)---->Sharing the Experience of Business Transformation Using Artificial Intelligence in the Ministry of Finance.\n(字串長度:108)```me_desc---->(中文)財政部財政資訊中心主任張文熙博士，長期積極研究人工智慧、導入稅務機器人流程自動化、稅務資訊系統優化和規劃次世代稅務行政資訊系統。張主任創設結人工智慧機器新創實驗室，全力投入AI應用研究擴大服務量能，結合學習和大數據等技術進行工作流程分析，建立納稅人異常行為偵測模型。張主任具備資訊系統博士，並同時擁有工業工程學和資訊管理碩士學位。其專長和研究興趣以機器學習應用、數據分析、大型資訊系統規劃等。\r\n(英文)Dr. Wen-Hsi Chang is the Director-General, Fiscal Information Agency, Ministry of Finance, Taiwan and has been actively involved in artificial intelligence and robotic process automation on taxation process, tax information system optimization and planning a next-generation tax administration information system. Dr. Chang launched an innovation laboratory that integrates machine learning and big data analytics into existing workflows such as detection models on identifying taxpayers’ abnormal behavior. He received his Ph.D. degree in management science on information system management. He also obtained a MS degree in industrial engineering and a MBA degree in information management. His professional expertise and research interests include machine learning application, data analysis, and large-scale information systems planning.\r\n(字串長度:1446)---->財政部財政資訊中心主任張文熙博士，長期積極研究人工智慧、導入稅務機器人流程自動化、稅務資訊系統優化和規劃次世代稅務行政資訊系統。張主任創設結人工智慧機器新創實驗室，全力投入AI應用研究擴大服務量能，結合學習和大數據等技術進行工作流程分析，建立納稅人異常行為偵測模型。張主任具備資訊系統博士，並同時擁有工業工程學和資訊管理碩士學位。其專長和研究興趣以機器學習應用、數據分析、大型資訊系統規劃等。\nDr. Wen-Hsi Chang is the Director-General, Fiscal Information Agency, Ministry of Finance, Taiwan and has been actively involved in artificial intelligence and robotic process automation on taxation process, tax information system optimization and planning a next-generation tax administration information system. Dr. Chang launched an innovation laboratory that integrates machine learning and big data analytics into existing workflows such as detection models on identifying taxpayers’ abnormal behavior. He received his Ph.D. degree in management science on information system management. He also obtained a MS degree in industrial engineering and a MBA degree in information management. His professional expertise and research interests include machine learning application, data analysis, and large-scale information systems planning.\n(字串長度:1428)'),(6811,'2024-09-24 09:45:32',52,'me_desc---->David Hsu  \r\nFAE Director, Arm Taiwan \r\nDavid Hsu is FAE Director of Arm, leading FAE team of Taiwan for closer \r\ncollaboration and communications internally and externally to accomplish \r\npartners and customers solutions. He has over 20 years of professional \r\nexperience in the semiconductor industry. \r\nDavid Hsu joined Arm in 2011 as a CPU FAE as well as responsible for product \r\nmarketing and sales project management and then left in 2017. After then, he \r\njoined Intelligo, a start-up invested by MediaTek, leading the business \r\ndevelopment team to explore opportunities in Japan, Korea, and US. In 2019 \r\nDavid was recruited by Arm again as FAE director to drive Arm’s IP solutions \r\nadoption.  \r\nIn addition to Arm, David worked at Faraday, Alpha Imaging Technology \r\n(MediaTek subsidiary) and BenQ, focusing on IC design work ranging from \r\narchitecture definition, RTL coding, front-end/back-end implementation to IC \r\nverification and testing. He has rich experience in pre-sales strategy set-up \r\nand sales enablement.  \r\nDavid received a master’s degree in Computer Science and Engineering at \r\nNational Chiao Tung University.  \r\n徐達勇   \r\n  \r\nArm 應用工程總監 \r\n徐達勇目前為Arm應用工程總監，帶導台灣FAE團隊與內部跨部門和外部更\r\n密切地合作與溝通，以完成合作夥伴與客戶所需的解決方案。 他在半導體產\r\n業擁有超過20年的專業經驗。 \r\n徐達勇於2011年加入Arm並擔任CPU應用工程師，同時也負責產品行銷和銷\r\n售專案管理。於2017年離開Arm之後，他加入聯發科轉投資的新創公司\r\nIntelligo，帶領業務團隊成功開發日本、韓國與美國市場。2019年他再次被邀\r\n請回到Arm擔任應用工程總監，致力推動並協助客戶採用Arm IP解決方案。 \r\n除了Arm之外，他也曾經任職於Faraday、聯發科分公司Alpha Imaging \r\nTechnology 和 BenQ，專注從事在IC設計相關工作，從架構的定義、RTL編\r\n碼、前端/後端執行，以及到IC驗證與測試。在售前策略擬定和推動銷售擁有\r\n豐富的經驗。 \r\n徐達勇擁有國立交通大學電腦科學工程學系碩士學位。(字串長度:2175)---->David Hsu is FAE Director of Arm, leading FAE team of Taiwan for closer collaboration and communications internally and externally to accomplish partners and customers solutions. He has nearly 20 years of professional experience in the semiconductor industry.David Hsu joined Arm in 2011 as a CPU FAE as well as responsible for product marketing and sales project management and then left in 2017. After then, he joined Intelligo, a start-up invested by MediaTek, leading the business development team to explore opportunities in Japan, Korea and US. In 2019 he was recruited by Arm again as FAE director to drive Arm’s IP solutions adoption. In addition to Arm, David worked at Faraday, Alpha Imaging Technology (MediaTek subsidiary) and BenQ, focusing on IC design work ranging from architecture definition, RTL coding, front-end/back-end implementation to IC verification and testing. He has rich experience in pre-sales strategy set-up and sales enablement. David received a master’s degree in Computer Science and Engineering at National Chiao Tung University.(字串長度:1069)'),(6812,'2024-09-24 10:45:19',23,'name---->鄭振牟(字串長度:9)---->許永真(字串長度:9)```hide---->1(字串長度:1)---->0(字串長度:1)```talk_title---->(字串長度:0)---->AI 的平衡木：技術突破、倫理邊界與國家安全(字串長度:60)```company---->長庚大學人工智慧學系(字串長度:30)---->長庚大學智慧運算學院(字串長度:30)```position---->教授(字串長度:6)---->副院長(字串長度:9)```me_desc---->(字串長度:0)---->許永真教授現任於臺大資訊工程學系暨網路與多媒體研究所，曾任臺大資訊系主任。許教授致力於人工智慧研究領域逾三十年，研究主題含智慧型多代理人、資料分析、知識建構、常識運算、感知服務、及智慧型物聯網技術。 自2012年起，許教授擔任Intel台大創新研究中心主任，積極進行跨國、跨校、跨領域、跨產學合作，研發物聯網於智慧農業、智慧運輸、與智慧家庭之相關技術。2016年在台大、科技部、英特爾實驗室、台達電、與研華共同支持下，開始進行擴充人機互動於未來智慧製造之研究。以「臺大智慧聯網創新研究中心」的新標幟，成長發展為物聯網、人工智慧、與人機互動相關研究的重鎮。\r\n許教授活躍於國際學術領域，曾擔任台灣人工智慧學會理事長，TAAI、IEEE iThings、AAAI SocialNLP Workshop等國際會議的議程主席; 以及Intelligent Data Analysis, Service-Oriented Computing and Applications, JISE等國際期刊之編輯委員。許教授目前於資訊系開授人工智慧相關課程，並合授網媒所「物聯網產品設計與創新」、創新設計學院「智齡設計—老人科技福祉專題」，透過跨領域合作來激發學生創新思考與前瞻技術實現。(字串長度:1336)'),(6813,'2024-09-24 10:45:20',88,'hide---->1(字串長度:1)---->0(字串長度:1)```me_desc---->Mu-Yueh Lee / Generative AI Community Curator\r\nMu-Yueh Lee focuses on promoting \"coding with AI\" (blog.gaiconf.com), dedicated to helping non-engineers use ChatGPT to write automation scripts, allowing office workers to complete tasks more efficiently.\r\nAs the curator of the Generative AI community, Mu-Yueh organizes the annual Generative AI Conference (gaiconf.com) and monthly meetups.\r\nIn addition, Mu-Yueh is the founder of Mu-Yueh Co., Ltd. (muyueh.com), a company specializing in customized information systems and AI integration services. Clients include Chunghwa Telecom, FarEasTone Telecom, E.SUN Bank, and the World Health Organization.\r\nMu-Yueh graduated from National Taiwan University with a degree in Business Administration and is skilled in data analysis and data visualization.\r\n\r\n(字串長度:800)---->Mu-Yueh Lee / Generative AI Community Curator\nMu-Yueh Lee focuses on promoting \"coding with AI\" (blog.gaiconf.com), dedicated to helping non-engineers use ChatGPT to write automation scripts, allowing office workers to complete tasks more efficiently.\nAs the curator of the Generative AI community, Mu-Yueh organizes the annual Generative AI Conference (gaiconf.com) and monthly meetups.\nIn addition, Mu-Yueh is the founder of Mu-Yueh Co., Ltd. (muyueh.com), a company specializing in customized information systems and AI integration services. Clients include Chunghwa Telecom, FarEasTone Telecom, E.SUN Bank, and the World Health Organization.\nMu-Yueh graduated from National Taiwan University with a degree in Business Administration and is skilled in data analysis and data visualization.\n\n(字串長度:794)'),(6814,'2024-09-24 11:01:04',23,'website---->(字串長度:0)---->http://agents.csie.ntu.edu.tw/~yjhsu/(字串長度:37)'),(6815,'2024-09-24 11:14:29',74,'hide---->0(字串長度:1)---->1(字串長度:1)'),(6816,'2024-09-24 15:26:40',12,'desc---->(字串長度:0)---->This presentation examines the pivotal role of Artificial Intelligence (AI) in driving the digital transformation of the steel industry, with a particular focus on the exemplary case of China Steel Corporation (CSC). It showcases CSC\'s pioneering AI initiatives, including:\n\n  ‧A 5G-enabled, AI-powered remote crane operation system, augmented by digital twin technology.\n  ‧The strategic deployment of digital twin technology for production process optimization.\n  ‧The integration of generative AI applications, such as intelligent ERP assistants and maintenance expert systems.\n\nThe presentation elucidates how these AI-driven strategies effectively address industry-specific challenges, enhance operational efficiency, and foster knowledge dissemination within the steel sector.(字串長度:788)'),(6817,'2024-09-24 15:26:40',29,'desc---->(字串長度:0)---->This presentation primarily explores how generative AI is revolutionizing knowledge management. It begins by elucidating the necessity of knowledge management and the limitations of traditional approaches. The presentation then introduces how generative AI optimizes knowledge management processes through its comprehension capabilities, retrieval-augmented generation techniques, and integration with external tools. Several real-world case studies are presented, demonstrating the application of generative AI across various domains, including disaster prevention and relief, financial information, and construction. The benefits and risks of implementing generative AI in knowledge management are discussed, including considerations for information security. The presentation concludes by emphasizing the future directions of generative AI in knowledge management, anticipating its emergence as an essential tool for knowledge workers.\n\n這份簡報主要探討生成式 AI 如何革新知識管理。首先說明知識管理的必要性，以及傳統知識管理的局限性，接著介紹生成式 AI 如何透過理解能力、檢索增強生成技術、以及與外部工具整合，來優化知識管理流程。簡報還舉出多個實際案例，展示生成式 AI 在不同領域的應用，例如災害防救、金融資訊、建築營造等，並討論導入生成式 AI 知識管理的效益和風險，包括在資訊安全上的考量。簡報最後強調生成式 AI 知識管理的未來發展方向，預期將成為知識工作者的重要工具。(字串長度:1569)'),(6818,'2024-09-24 15:26:40',64,'talk_title---->Multimodal RAG Framework (字串長度:25)---->Implementing RAG in Multimodal Architectures for Image Generation(字串長度:65)```desc---->(字串長度:0)---->RAG (Retrieval-Augmented Generation) has become a mainstream method for enhancing responses from large language models. In recent years, multimodal LLM has been developing increasingly well, and similar approaches in multimodal LLM application scenarios and implementation methods are worth exploring. This presentation will discuss the implementation of multimodal LLM in the RAG process, and share practical applications using image generation as a case study.(字串長度:462)'),(6819,'2024-09-24 15:43:06',12,'desc---->This presentation examines the pivotal role of Artificial Intelligence (AI) in driving the digital transformation of the steel industry, with a particular focus on the exemplary case of China Steel Corporation (CSC). It showcases CSC\'s pioneering AI initiatives, including:\r\n\r\n  ‧A 5G-enabled, AI-powered remote crane operation system, augmented by digital twin technology.\r\n  ‧The strategic deployment of digital twin technology for production process optimization.\r\n  ‧The integration of generative AI applications, such as intelligent ERP assistants and maintenance expert systems.\r\n\r\nThe presentation elucidates how these AI-driven strategies effectively address industry-specific challenges, enhance operational efficiency, and foster knowledge dissemination within the steel sector.(字串長度:794)---->This presentation examines the pivotal role of Artificial Intelligence (AI) in driving the digital transformation of the steel industry, with a particular focus on the exemplary case of China Steel Corporation (CSC). It showcases CSC\'s pioneering AI initiatives, including:\n\n  ‧A 5G-enabled, AI-powered remote crane operation system, augmented by digital twin technology.\n  ‧The strategic deployment of digital twin technology for production process optimization.\n  ‧The integration of generative AI applications, such as intelligent ERP assistants and maintenance expert systems.\n\nThe presentation elucidates how these AI-driven strategies effectively address industry-specific challenges, enhance operational efficiency, and foster knowledge dissemination within the steel sector.(字串長度:788)'),(6820,'2024-09-24 15:43:06',29,'desc---->This presentation primarily explores how generative AI is revolutionizing knowledge management. It begins by elucidating the necessity of knowledge management and the limitations of traditional approaches. The presentation then introduces how generative AI optimizes knowledge management processes through its comprehension capabilities, retrieval-augmented generation techniques, and integration with external tools. Several real-world case studies are presented, demonstrating the application of generative AI across various domains, including disaster prevention and relief, financial information, and construction. The benefits and risks of implementing generative AI in knowledge management are discussed, including considerations for information security. The presentation concludes by emphasizing the future directions of generative AI in knowledge management, anticipating its emergence as an essential tool for knowledge workers.\r\n\r\n這份簡報主要探討生成式 AI 如何革新知識管理。首先說明知識管理的必要性，以及傳統知識管理的局限性，接著介紹生成式 AI 如何透過理解能力、檢索增強生成技術、以及與外部工具整合，來優化知識管理流程。簡報還舉出多個實際案例，展示生成式 AI 在不同領域的應用，例如災害防救、金融資訊、建築營造等，並討論導入生成式 AI 知識管理的效益和風險，包括在資訊安全上的考量。簡報最後強調生成式 AI 知識管理的未來發展方向，預期將成為知識工作者的重要工具。(字串長度:1571)---->This presentation primarily explores how generative AI is revolutionizing knowledge management. It begins by elucidating the necessity of knowledge management and the limitations of traditional approaches. The presentation then introduces how generative AI optimizes knowledge management processes through its comprehension capabilities, retrieval-augmented generation techniques, and integration with external tools. Several real-world case studies are presented, demonstrating the application of generative AI across various domains, including disaster prevention and relief, financial information, and construction. The benefits and risks of implementing generative AI in knowledge management are discussed, including considerations for information security. The presentation concludes by emphasizing the future directions of generative AI in knowledge management, anticipating its emergence as an essential tool for knowledge workers.\n\n這份簡報主要探討生成式 AI 如何革新知識管理。首先說明知識管理的必要性，以及傳統知識管理的局限性，接著介紹生成式 AI 如何透過理解能力、檢索增強生成技術、以及與外部工具整合，來優化知識管理流程。簡報還舉出多個實際案例，展示生成式 AI 在不同領域的應用，例如災害防救、金融資訊、建築營造等，並討論導入生成式 AI 知識管理的效益和風險，包括在資訊安全上的考量。簡報最後強調生成式 AI 知識管理的未來發展方向，預期將成為知識工作者的重要工具。(字串長度:1569)```me_desc---->Dr. Willie Yang is the founder of eLAND (意藍), the #1 public listed AI+Data company in Taiwan (TPEX:6925). eLAND is a leading SaaS (Software-as-a-Service) company specializing in the research and development of cutting-edge software technologies, including search engines, natural language processing, and AI applications for enterprises. eLAND\'s flagship product OpView, the largest social listening platform in Taiwan, scans tens of billions words every day, With its advanced AI technologies and infrastructure, OpView offers comprehensive functions to monitor and analyze online conversations, helping companies understand public opinions and market trends.\r\n\r\nDr. Willie Yang\'s research focuses on information retrieval, natural language processing, and social media analytics. He devoted himself to the development of large-scale full-text search engine and has been providing leading technology solutions for enterprise since 2000. He holds more than 20 patents for natural language processing and web mining in Greater China. \r\n\r\nDr. Willie Yang also serves as Assistant Professor in National Taiwan University, and the member of National Standards Technical Committee on Information and Communication in Taiwan\r\n\r\n楊立偉博士為現任台灣大學資訊管理學系及工商管理學系兼任助理教授、資訊及通信國家標準技術委員、意藍資訊公司總經理。任教於台灣大學超過18年，為台大教學傑出教師，曾榮獲國家雲端創新獎、數位時代「創業之星」首獎、100MVP最有價值經理人，以及數位時代雜誌選為代表台灣軟體的封面人物之一。研究專長領域為資料庫管理、搜尋引擎、機器學習及語意分析、大數據及商業分析、資訊管理及數位行銷等，擁有近30項數據及語意分析專利。楊立偉博士所創辦之意藍資訊(eLAND)為首家掛牌的AI智能數據公司(股票代號 6925)，提供搜尋引擎、輿情分析、電商分析、企業知識管理等雲端軟體服務及AI數據應用，主要客戶包括高科技、金融、零售等產業之龍頭企業，擁有極高之市場覆蓋率。(字串長度:2142)---->Dr. Willie Yang is the founder of eLAND (意藍), the #1 public listed AI+Data company in Taiwan (TPEX:6925). eLAND is a leading SaaS (Software-as-a-Service) company specializing in the research and development of cutting-edge software technologies, including search engines, natural language processing, and AI applications for enterprises. eLAND\'s flagship product OpView, the largest social listening platform in Taiwan, scans tens of billions words every day, With its advanced AI technologies and infrastructure, OpView offers comprehensive functions to monitor and analyze online conversations, helping companies understand public opinions and market trends.\n\nDr. Willie Yang\'s research focuses on information retrieval, natural language processing, and social media analytics. He devoted himself to the development of large-scale full-text search engine and has been providing leading technology solutions for enterprise since 2000. He holds more than 20 patents for natural language processing and web mining in Greater China. \n\nDr. Willie Yang also serves as Assistant Professor in National Taiwan University, and the member of National Standards Technical Committee on Information and Communication in Taiwan\n\n楊立偉博士為現任台灣大學資訊管理學系及工商管理學系兼任助理教授、資訊及通信國家標準技術委員、意藍資訊公司總經理。任教於台灣大學超過18年，為台大教學傑出教師，曾榮獲國家雲端創新獎、數位時代「創業之星」首獎、100MVP最有價值經理人，以及數位時代雜誌選為代表台灣軟體的封面人物之一。研究專長領域為資料庫管理、搜尋引擎、機器學習及語意分析、大數據及商業分析、資訊管理及數位行銷等，擁有近30項數據及語意分析專利。楊立偉博士所創辦之意藍資訊(eLAND)為首家掛牌的AI智能數據公司(股票代號 6925)，提供搜尋引擎、輿情分析、電商分析、企業知識管理等雲端軟體服務及AI數據應用，主要客戶包括高科技、金融、零售等產業之龍頭企業，擁有極高之市場覆蓋率。(字串長度:2136)'),(6821,'2024-09-24 17:01:36',10,'talk_title---->資通訊業AI 2.0之布局與實踐(字串長度:36)---->The strategy and development of AI 2.0 in the ICT Industry(字串長度:58)```desc---->(字串長度:0)---->It outlines the evolution from AI 1.0 to AI 2.0. Especially in innovative applications such as speech, image and text recognition. We share Chunghwa Telecom’s various applications of generative AI, empowering various industries to develop AI applications in their own fields, and also explore the practical applications and challenges of artificial intelligence in various industries.(字串長度:386)'),(6822,'2024-09-25 08:52:44',37,'position_for_agenda---->資訊暨數位轉型科技群執行副總(字串長度:42)---->資訊長暨執行副總(字串長度:24)'),(6823,'2024-09-25 08:52:45',96,'name2---->(字串長度:0)---->Ronny Wang(字串長度:10)```anchor---->speaker-96(字串長度:10)---->ronny-wang(字串長度:10)```company---->歐噴有限公司(字串長度:18)---->OpenFun Ltd.(字串長度:12)```position---->創辦人(字串長度:9)---->Founder(字串長度:7)```me_desc---->(字串長度:0)---->Ronny Wang, the founder of OpenFun Ltd., has been involved in the open government and open parliament movements for many years. He enjoys writing web scraping programs to collect government data and convert it into open data or open APIs for public use. Within the g0v.tw community, he has initiated numerous civic technology projects. For example, he launched the Open Campaign Finance project, which uses crowdsourcing to convert political donation data, only available in paper form by the government, into digital data and advocates for the government to publish this data online. He also started the Job Helper project, a browser extension that shows whether a company has a history of labor law violations while you are browsing job websites. Additionally, he initiated the Taiwan Parliament API project, which transforms various unstructured Word and PDF documents from the Legislative Yuan into structured data, aiming to help more people understand the workings of the parliament. For more related projects and works, visit https://ronny.tw/data or https://openfun.tw/.(字串長度:1078)```talkfile_link---->(字串長度:0)---->V(字串長度:1)```website---->(字串長度:0)---->https://openfun.tw/(字串長度:19)'),(6824,'2024-09-25 11:16:13',92,'talkfile_link---->V(字串長度:1)---->X(字串長度:1)'),(6825,'2024-09-25 11:16:13',95,'position---->副研究員(字串長度:12)---->研究員(字串長度:9)```me_desc---->(字串長度:0)---->學經歷\r\n美國維吉尼亞大學法學博士 (S.J.D., 2004)\r\n美國賓西法尼亞大學法學碩士 (LL.M., 1999)\r\n國立台灣大學法律學研究所碩士 (LL.M., 1998)\r\n國立台灣大學法律學系學士 (LL.B., 1994)\r\n\r\n研究領域\r\n資訊法、食品法、憲法隱私權、科學與法律、科技社會研究與法律(字串長度:332)```position_for_agenda---->副研究員(字串長度:12)---->研究員(字串長度:9)'),(6826,'2024-09-25 11:38:14',34,'desc---->(字串長度:0)---->In this talk, we will present E.SUN\'s experiences in the application and development of generative AI. Besides the commonly seen conversational AI platforms, we will also discuss methods for rapidly embedding generative AI into existing systems, as well as cases of generating news summaries and meeting minutes through prompt engineering. Lastly, we will share some other interesting cognitive computing cases that can be done using generative AI.(字串長度:448)'),(6827,'2024-09-25 15:40:56',30,'desc---->(字串長度:0)---->Artificial intelligence (AI) stands as humanity\'s most pivotal invention, possessing the potential to reshape society profoundly, akin to the transformative power of fire and electricity. While earlier innovations such as the wheel and the printing press laid the groundwork for societal development, AI introduces unprecedented complexities, particularly concerning trustworthiness and fairness. As AI systems increasingly govern decision-making, it is crucial that these technologies operate transparently to prevent biases that could exacerbate existing inequalities. In this talk, we will present our recent work on addressing these issues, including data collection, privacy, and robotics, while reflecting on our choices regarding AI technology as we navigate the future.(字串長度:777)'),(6828,'2024-09-26 08:35:13',2,'name---->黃彥男(字串長度:9)---->周美吟(字串長度:9)```company---->數位發展部(字串長度:15)---->中央研究院(字串長度:15)```position---->部長(字串長度:6)---->副院長(字串長度:9)```company_for_agenda---->數位發展部(字串長度:15)---->(字串長度:0)```position_for_agenda---->部長(字串長度:6)---->(字串長度:0)'),(6829,'2024-09-26 08:35:13',50,'talk_title---->Water-Cooling-free.  How to fulfill ultra low energy consumption NPU IP for ptivate Large Language Model(LLM)\n(字串長度:110)---->Water-Cooling-free.  How to fulfill ultra low energy consumption NPU IP for Large Language Model(LLM)\n,(字串長度:103)'),(6830,'2024-09-26 09:39:48',44,'desc---->(字串長度:0)---->91APP has been dedicated to serving the retail industry for the past decade, assisting over 1,000 medium to large retail brands in their digital transformation. In recent years, the company has further advanced by promoting AI transformation. The aim of introducing new technologies is to solve industry-specific problems. This topic will begin with the fundamental issues of the retail industry, discussing the role of AI in retail, how 91APP develops AI models for retail, and predicting the future landscape of the retail sector.\r\n91APP 專注於服務零售產業，10年來協助1,000多家中大型的零售品牌數位轉型，近年來更近一步推動 AI 轉型。導入新科技，目標都是要解決產業問題。本講題將從從零售業的基本問題出發，討論 AI 在零售業內的角色，91APP 如何發展零售的 AI 模型，並預測未來零售業的面貌。(字串長度:892)```me_desc---->CPO of 91APP,  Entrepreneur(字串長度:27)---->The speaker is the Chief Product Officer of 91APP and the owner of the blog \"The Science of Retail\" (https://happylee.blog/). As the head of product development at 91APP, the speaker must stay at the forefront of all new technologies, identifying critical entry points and rapidly developing industry applications. With unique insights into the retail industry and the application of technology, these perspectives are shared on the \"The Science of Retail\" blog, where most articles have been widely circulated on social media. With the advent of the AI era, the speaker actively participates in various AI communities and is committed to promoting AI applications within enterprises.\r\n91APP 產品長，「零售的科學」（https://happylee.blog/）的站長。\r\n身為 91APP 的產品發展的總舵手，對於所有的新科技都必須第一手直接掌握，並找出關鍵的切入點，快速發展出產業應用，對零售業與科技應用有其獨到的見解，並發表這些見解在「零售的科學」部落格上，多數文章在社群上被大量轉發。隨著 AI 時代的來臨，積極參與各種 AI 社群，積極推動 AI 在企業內的應用。(字串長度:1179)'),(6831,'2024-09-26 15:07:28',27,'desc---->(字串長度:0)---->Briefly introduce the Ministry of Finance\'s artificial intelligence technology strategy and goals since 2020, sharing concrete practical experiences including the difficulties encountered during implementation, corresponding measures taken, key success factors, current performance, and directions for continued development.\n\n財政部自2020年起運用人工智慧技術策略及目標，推動過程的困難、因應措施、成功關鍵因素、目前績效以及持續發展方向等之具體實務經驗分享。\n\n\n(字串長度:522)'),(6832,'2024-09-26 18:28:32',5,'talk_title---->AI and the Changes of the Century(字串長度:33)---->AI and the Changes of a Century(字串長度:31)'),(6833,'2024-09-26 18:51:55',44,'me_desc---->The speaker is the Chief Product Officer of 91APP and the owner of the blog \"The Science of Retail\" (https://happylee.blog/). As the head of product development at 91APP, the speaker must stay at the forefront of all new technologies, identifying critical entry points and rapidly developing industry applications. With unique insights into the retail industry and the application of technology, these perspectives are shared on the \"The Science of Retail\" blog, where most articles have been widely circulated on social media. With the advent of the AI era, the speaker actively participates in various AI communities and is committed to promoting AI applications within enterprises.\r\n91APP 產品長，「零售的科學」（https://happylee.blog/）的站長。\r\n身為 91APP 的產品發展的總舵手，對於所有的新科技都必須第一手直接掌握，並找出關鍵的切入點，快速發展出產業應用，對零售業與科技應用有其獨到的見解，並發表這些見解在「零售的科學」部落格上，多數文章在社群上被大量轉發。隨著 AI 時代的來臨，積極參與各種 AI 社群，積極推動 AI 在企業內的應用。(字串長度:1179)---->The speaker is the Chief Product Officer of 91APP and the owner of the blog \\\"The Science of Retail\\\" (https://happylee.blog/). As the head of product development at 91APP, the speaker must stay at the forefront of all new technologies, identifying critical entry points and rapidly developing industry applications. With unique insights into the retail industry and the application of technology, these perspectives are shared on the \"The Science of Retail\" blog, where most articles have been widely circulated on social media. With the advent of the AI era, the speaker actively participates in various AI communities and is committed to promoting AI applications within enterprises.\n91APP 產品長，「零售的科學」（https://happylee.blog/）的站長。\n身為 91APP 的產品發展的總舵手，對於所有的新科技都必須第一手直接掌握，並找出關鍵的切入點，快速發展出產業應用，對零售業與科技應用有其獨到的見解，並發表這些見解在「零售的科學」部落格上，多數文章在社群上被大量轉發。隨著 AI 時代的來臨，積極參與各種 AI 社群，積極推動 AI 在企業內的應用。(字串長度:1179)'),(6834,'2024-09-27 13:49:29',44,'me_desc---->The speaker is the Chief Product Officer of 91APP and the owner of the blog The Science of Retail ( https://happylee.blog /). As the head of product development at 91APP, the speaker must stay at the forefront of all new technologies, identifying critical entry points and rapidly developing industry applications. With unique insights into the retail industry and the application of technology, these perspectives are shared on the \"The Science of Retail\" blog, where most articles have been widely circulated on social media. With the advent of the AI era, the speaker actively participates in various AI communities and is committed to promoting AI applications within enterprises.\r\n91APP 產品長，「零售的科學」（https://happylee.blog/）的站長。\r\n身為 91APP 的產品發展的總舵手，對於所有的新科技都必須第一手直接掌握，並找出關鍵的切入點，快速發展出產業應用，對零售業與科技應用有其獨到的見解，並發表這些見解在「零售的科學」部落格上，多數文章在社群上被大量轉發。隨著 AI 時代的來臨，積極參與各種 AI 社群，積極推動 AI 在企業內的應用。(字串長度:1179)---->The speaker is the Chief Product Officer of 91APP and the owner of the blog \\\"The Science of Retail\\\" (https://happylee.blog/). As the head of product development at 91APP, the speaker must stay at the forefront of all new technologies, identifying critical entry points and rapidly developing industry applications. With unique insights into the retail industry and the application of technology, these perspectives are shared on the \"The Science of Retail\" blog, where most articles have been widely circulated on social media. With the advent of the AI era, the speaker actively participates in various AI communities and is committed to promoting AI applications within enterprises.\n91APP 產品長，「零售的科學」（https://happylee.blog/）的站長。\n身為 91APP 的產品發展的總舵手，對於所有的新科技都必須第一手直接掌握，並找出關鍵的切入點，快速發展出產業應用，對零售業與科技應用有其獨到的見解，並發表這些見解在「零售的科學」部落格上，多數文章在社群上被大量轉發。隨著 AI 時代的來臨，積極參與各種 AI 社群，積極推動 AI 在企業內的應用。(字串長度:1179)'),(6835,'2024-09-27 13:49:29',68,'desc---->(字串長度:0)---->GenAI has numerous applications in technology, but what about cybersecurity? If GenAI is utilized for offensive purposes, how significant could the impact on the world be? Much like artists, the red team in cybersecurity requires immense creativity and imagination. How has GenAI assisted them? This session will explore the applications of GenAI in the offensive landscape, from attack trends to red team implementations.(字串長度:422)'),(6836,'2024-09-27 13:49:29',79,'desc---->This presentation will focus on the principles of multimodal language model technology and its applications in the healthcare field, demonstrating its extensive use through six different medical scenarios. Each scenario will be accompanied by a 30-second demo video, designed to give the audience a quick and immersive experience of these technologies\' powerful capabilities in healthcare.\nWe will begin by showcasing a chronic disease management Q&A system for dementia knowledge. This system is presented in a website format and provides accurate, real-time medical information through a language model, helping users better understand disease-related information. Following this, we will introduce a Line Chatbot for remote diabetes care, which facilitates intelligent Q&A between patients and healthcare providers, offering immediate medical support to diabetes patients.\nIn the area of emotional companionship, we will share a TVBS interview video demonstrating how an emotional companion QA robot uses language models for question-answering, offering psychological support and enhancing patients\' emotional well-being. Additionally, we will demonstrate a digital interaction display for standardized dental patient interactions, where AI automates assessments to enhance the interactivity and effectiveness of dental education.\nFor image processing applications, we will introduce an X-ray dental image report generation system that assists doctors in automatically generating dental imaging reports, significantly improving medical efficiency. Lastly, we will present a text-to-dental 3D image generation technology that converts text input into 3D images, applied in the development of VR imaging for surgical planning, showcasing cutting-edge technologies in dental surgery.\nThrough these demo videos, we will guide the audience to a deeper understanding of how multimodal language model technology plays a key role in various healthcare scenarios, highlighting its significance in the future development of medical technology.(字串長度:2036)---->The presentation will focus on the principles of multimodal language model edge Computing technology and its applications in healthcare, demonstrating its extensive use in six different medical scenarios. \n1. Chronic disease management Q&A system for dementia knowledge  :\n    web-based platform with real-time, accurate medical information\n2. Line Chatbot for remote diabetes care :\n     facilitates intelligent Q&A between patients and healthcare providers.\n3. Emotional companion QA robot :\n     TVBS interview video demonstrating psychological support via language models.\n4. Digital interaction display for standardized dental-patient interactions :\n     AI automates assessments for effective dental education.\n5. X-ray dental image report generation system :\n    automatically generates dental imaging reports to improve medical efficiency.\n6. Text-to-dental 3D image generation technology :\n    converts text into 3D images for VR imaging and surgical planning in dental surgery.(字串長度:987)'),(6837,'2025-03-04 11:47:19',9,'name---->TBD(字串長度:3)---->TBD(字串長度:3)```anchor---->speaker-9(字串長度:9)---->speaker-9(字串長度:9)```talk_title2---->開源模型論壇(字串長度:18)---->開源模型論壇(字串長度:18)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```lecture_type---->開源模型論壇(字串長度:18)---->開源模型論壇(字串長度:18)```post_id---->10784(字串長度:5)---->10784(字串長度:5)```conf---->PANEL(字串長度:5)---->PANEL(字串長度:5)'),(6838,'2025-03-04 11:47:20',10,'name---->TBD(字串長度:3)---->TBD(字串長度:3)```anchor---->speaker-10(字串長度:10)---->speaker-10(字串長度:10)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```post_id---->10785(字串長度:5)---->10785(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6839,'2025-03-04 11:47:20',11,'name---->TBD(字串長度:3)---->TBD(字串長度:3)```anchor---->speaker-11(字串長度:10)---->speaker-11(字串長度:10)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```post_id---->10786(字串長度:5)---->10786(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6840,'2025-03-04 11:47:20',12,'name---->TBD(字串長度:3)---->TBD(字串長度:3)```anchor---->speaker-12(字串長度:10)---->speaker-12(字串長度:10)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```post_id---->10787(字串長度:5)---->10787(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6841,'2025-03-04 11:47:20',13,'name---->梁柏嵩(字串長度:9)---->梁柏嵩(字串長度:9)```name2---->Bor-Sung Liang(字串長度:14)---->Bor-Sung Liang(字串長度:14)```anchor---->bor-sung-liang(字串長度:14)---->bor-sung-liang(字串長度:14)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```company_for_agenda----> 聯發科前瞻技術平台(字串長度:28)----> 聯發科前瞻技術平台(字串長度:28)```position_for_agenda---->資深處長(字串長度:12)---->資深處長(字串長度:12)```post_id---->10788(字串長度:5)---->10788(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6842,'2025-03-04 11:47:20',14,'name---->林旅強(字串長度:9)---->林旅強(字串長度:9)```anchor---->speaker-14(字串長度:10)---->speaker-14(字串長度:10)```talk_title2---->中國的 AI 開源生態系現狀(字串長度:34)---->中國的 AI 開源生態系現狀(字串長度:34)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```company_for_agenda---->零一萬物開源(字串長度:18)---->零一萬物開源(字串長度:18)```position_for_agenda---->前負責人(字串長度:12)---->前負責人(字串長度:12)```post_id---->10789(字串長度:5)---->10789(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6843,'2025-03-04 11:47:20',15,'name---->蔡政霖(字串長度:9)---->蔡政霖(字串長度:9)```anchor---->speaker-15(字串長度:10)---->speaker-15(字串長度:10)```talk_title2---->生成式 AI 的開源工具應用(字串長度:34)---->生成式 AI 的開源工具應用(字串長度:34)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```company_for_agenda---->台灣人工智慧學校(字串長度:24)---->台灣人工智慧學校(字串長度:24)```position_for_agenda---->技術處副組長(字串長度:18)---->技術處副組長(字串長度:18)```post_id---->10790(字串長度:5)---->10790(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6844,'2025-03-04 11:47:20',16,'name---->邵逸文(字串長度:9)---->邵逸文(字串長度:9)```anchor---->speaker-16(字串長度:10)---->speaker-16(字串長度:10)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```company_for_agenda---->台灣電通(字串長度:12)---->台灣電通(字串長度:12)```position_for_agenda---->策略創新長(字串長度:15)---->策略創新長(字串長度:15)```post_id---->10791(字串長度:5)---->10791(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6845,'2025-03-04 11:47:36',9,'name---->TBD(字串長度:3)---->TBD(字串長度:3)```anchor---->speaker-9(字串長度:9)---->speaker-9(字串長度:9)```talk_title2---->開源模型論壇(字串長度:18)---->開源模型論壇(字串長度:18)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```lecture_type---->開源模型論壇(字串長度:18)---->開源模型論壇(字串長度:18)```post_id---->10784(字串長度:5)---->10784(字串長度:5)```conf---->PANEL(字串長度:5)---->PANEL(字串長度:5)'),(6846,'2025-03-04 11:47:36',10,'name---->TBD(字串長度:3)---->TBD(字串長度:3)```anchor---->speaker-10(字串長度:10)---->speaker-10(字串長度:10)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```post_id---->10785(字串長度:5)---->10785(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6847,'2025-03-04 11:47:36',11,'name---->TBD(字串長度:3)---->TBD(字串長度:3)```anchor---->speaker-11(字串長度:10)---->speaker-11(字串長度:10)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```post_id---->10786(字串長度:5)---->10786(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6848,'2025-03-04 11:47:36',12,'name---->TBD(字串長度:3)---->TBD(字串長度:3)```anchor---->speaker-12(字串長度:10)---->speaker-12(字串長度:10)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```post_id---->10787(字串長度:5)---->10787(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6849,'2025-03-04 11:47:36',13,'name---->梁柏嵩(字串長度:9)---->梁柏嵩(字串長度:9)```name2---->Bor-Sung Liang(字串長度:14)---->Bor-Sung Liang(字串長度:14)```anchor---->bor-sung-liang(字串長度:14)---->bor-sung-liang(字串長度:14)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```company_for_agenda----> 聯發科前瞻技術平台(字串長度:28)----> 聯發科前瞻技術平台(字串長度:28)```position_for_agenda---->資深處長(字串長度:12)---->資深處長(字串長度:12)```post_id---->10788(字串長度:5)---->10788(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6850,'2025-03-04 11:47:36',14,'name---->林旅強(字串長度:9)---->林旅強(字串長度:9)```anchor---->speaker-14(字串長度:10)---->speaker-14(字串長度:10)```talk_title2---->中國的 AI 開源生態系現狀(字串長度:34)---->中國的 AI 開源生態系現狀(字串長度:34)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```company_for_agenda---->零一萬物開源(字串長度:18)---->零一萬物開源(字串長度:18)```position_for_agenda---->前負責人(字串長度:12)---->前負責人(字串長度:12)```post_id---->10789(字串長度:5)---->10789(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6851,'2025-03-04 11:47:36',15,'name---->蔡政霖(字串長度:9)---->蔡政霖(字串長度:9)```anchor---->speaker-15(字串長度:10)---->speaker-15(字串長度:10)```talk_title2---->生成式 AI 的開源工具應用(字串長度:34)---->生成式 AI 的開源工具應用(字串長度:34)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```company_for_agenda---->台灣人工智慧學校(字串長度:24)---->台灣人工智慧學校(字串長度:24)```position_for_agenda---->技術處副組長(字串長度:18)---->技術處副組長(字串長度:18)```post_id---->10790(字串長度:5)---->10790(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6852,'2025-03-04 11:47:36',16,'name---->邵逸文(字串長度:9)---->邵逸文(字串長度:9)```anchor---->speaker-16(字串長度:10)---->speaker-16(字串長度:10)```talk_lang---->中文(字串長度:6)---->中文(字串長度:6)```company_for_agenda---->台灣電通(字串長度:12)---->台灣電通(字串長度:12)```position_for_agenda---->策略創新長(字串長度:15)---->策略創新長(字串長度:15)```post_id---->10791(字串長度:5)---->10791(字串長度:5)```conf---->AI(字串長度:2)---->AI(字串長度:2)'),(6853,'2025-03-05 09:00:35',2,'hide---->0(字串長度:1)---->1(字串長度:1)'),(6854,'2025-03-05 09:00:35',3,'hide---->0(字串長度:1)---->1(字串長度:1)'),(6855,'2025-03-05 09:00:35',4,'hide---->0(字串長度:1)---->1(字串長度:1)'),(6856,'2025-03-05 09:00:35',13,'name---->梁柏嵩(字串長度:9)---->梁伯嵩(字串長度:9)```name2---->Bor-Sung Liang(字串長度:14)---->(字串長度:0)```anchor---->bor-sung-liang(字串長度:14)---->speaker-13(字串長度:10)```conf---->AI(字串長度:2)---->AIK(字串長度:3)'),(6857,'2025-03-05 09:00:35',10,'hide---->0(字串長度:1)---->1(字串長度:1)'),(6858,'2025-03-05 09:00:35',11,'hide---->0(字串長度:1)---->1(字串長度:1)'),(6859,'2025-03-05 09:00:35',12,'hide---->0(字串長度:1)---->1(字串長度:1)'),(6860,'2025-03-05 09:00:35',5,'name---->TBD(字串長度:3)---->黃瀚萱(字串長度:9)```name2---->(字串長度:0)---->Bor-Sung Liang(字串長度:14)```anchor---->speaker-5(字串長度:9)---->bor-sung-liang(字串長度:14)```company_for_agenda---->(字串長度:0)---->中研院資訊科學研究所(字串長度:30)```position_for_agenda---->(字串長度:0)---->副研究員(字串長度:12)```conf---->AIK(字串長度:3)---->AI(字串長度:2)'),(6861,'2025-03-05 09:00:35',16,'name---->邵逸文(字串長度:9)---->邵懿文(字串長度:9)'),(6862,'2025-03-05 09:36:03',4,'lecture_type---->KEYNOTE (字串長度:8)---->(字串長度:0)'),(6863,'2025-03-05 09:36:03',13,'talk_title2---->(字串長度:0)---->AI 運算的衝擊及展望(字串長度:27)```lecture_type---->(字串長度:0)---->KEYNOTE(字串長度:7)'),(6864,'2025-03-05 10:09:09',13,'company_for_agenda----> 聯發科前瞻技術平台(字串長度:28)---->聯發科技 企業策略與前瞻技術(字串長度:40)'),(6865,'2025-03-05 10:09:09',9,'name---->TBD(字串長度:3)---->許永真(字串長度:9)```company_for_agenda---->(字串長度:0)---->Taiwan AICoE 策略委員會(字串長度:28)```position_for_agenda---->(字串長度:0)---->副總召(字串長度:9)'),(6866,'2025-03-05 10:30:11',9,'company_for_agenda---->Taiwan AICoE 策略委員會(字串長度:28)---->長庚大學智慧運算學院(字串長度:30)```position_for_agenda---->副總召(字串長度:9)---->院長(字串長度:6)'),(6867,'2025-03-05 19:19:59',1,'name---->蔡明順(字串長度:9)---->郭秉宸(字串長度:9)```name2---->Richie Tsai(字串長度:11)---->Benjamin Kuo(字串長度:12)```anchor---->richie-tsai(字串長度:11)---->benjamin-kuo(字串長度:12)'),(6868,'2025-03-05 19:19:59',6,'name---->蔡明順(字串長度:9)---->侯宜秀(字串長度:9)```anchor---->richie-tsai-2(字串長度:13)---->richie-tsai(字串長度:11)```conf---->PANEL(字串長度:5)---->AI(字串長度:2)'),(6869,'2025-03-05 19:37:09',6,'name2---->Richie Tsai(字串長度:11)---->Isabel Hou(字串長度:10)```anchor---->richie-tsai(字串長度:11)---->isabel-hou(字串長度:10)```company_for_agenda---->台灣人工智慧學校(字串長度:24)---->財團法人台灣人工智慧學校基金會(字串長度:45)```position_for_agenda---->校務長(字串長度:9)---->秘書長(字串長度:9)'),(6870,'2025-03-05 19:37:09',17,'name2---->(字串長度:0)---->Isabel Hou(字串長度:10)```anchor---->speaker-17(字串長度:10)---->isabel-hou-2(字串長度:12)```talk_title2---->(字串長度:0)---->開源與 AI 治理： 負責任的 AI 開發與應用(字串長度:54)```company_for_agenda---->台灣人工智慧學校 (字串長度:25)---->財團法人台灣人工智慧學校基金會(字串長度:45)'),(6871,'2025-03-06 20:23:42',6,'conf---->AI(字串長度:2)---->PANEL(字串長度:5)'),(6872,'2025-03-06 20:23:43',9,'hide---->0(字串長度:1)---->1(字串長度:1)'),(6873,'2025-03-06 20:23:45',17,'conf---->(字串長度:0)---->AI(字串長度:2)'),(6874,'2025-03-06 20:23:45',5,'talk_title2---->(字串長度:0)---->開源 LLM 版圖導航：如何為應用選擇合適的模型(字串長度:62)'),(6875,'2025-03-06 20:23:47',16,'name2---->(字串長度:0)---->Shao, Yi-Wen(字串長度:12)```anchor---->speaker-16(字串長度:10)---->shao-yi-wen(字串長度:11)```talk_title2---->(字串長度:0)---->行銷人的 GenAI 必修課(字串長度:28)'),(6876,'2025-03-06 20:23:47',18,'talk_title2---->(字串長度:0)---->開源趨勢下的 AI 人才認證計畫(字串長度:40)'),(6877,'2025-03-18 08:32:04',8,'name---->許大山(字串長度:9)---->蔡祈岩(字串長度:9)```name2---->Da-shan Shiu(字串長度:12)---->Rock TSAI(字串長度:9)```anchor---->da-shan-shiu(字串長度:12)---->rock-tsai(字串長度:9)```company_for_agenda---->聯發創新基地(字串長度:18)---->台灣大哥大 (字串長度:16)```position_for_agenda---->總監(字串長度:6)---->資訊長(字串長度:9)'),(6878,'2025-04-02 16:47:11',2,'me_desc---->(字串長度:0)---->經歷：\n資訊服務處處長（2018/01/01 迄今）\n資訊服務處代理處長（2017/10/01-2017/12/31）\n研究員，資訊科學研究所 （2017/12 迄今）\n副研究員，資訊科學研究所 （2011/01–2017/12）\n助研究員，資訊科學研究所 （2005/09–2011/01）\n\n學歷：\n博士，資訊科學系，加州大學洛杉磯分校，美國 （2003/01–2005/06）\n碩士，資訊科學系，加州大學洛杉磯分校，美國 （2001/09–2002/12）\n大學，資訊教育系，國立臺灣師範大學，中華民國 （1993/09–1998/06）(字串長度:564)'),(6879,'2025-04-02 16:47:12',13,'company---->(字串長度:0)---->聯發科技 企業策略與前瞻技術(字串長度:40)```position---->(字串長度:0)---->資深處長(字串長度:12)```desc---->(字串長度:0)---->AI 發展迅速，大型語言模型（LLM） 的演進，展現傑出的內容生成能力。但AI 計算量龐大，讓全世界競相建置運算資源，而讓世界半導體的需求高速成長。在此，我們討論大型語言模型的演進。尤其 AI 運算規模擴大規則 (Scaling Law)範圍，已由訓練時期運算 (Pre-Training & Post-Training)，進展到推理時期運算 (Test-Time Compute)。蒸餾模型 (Distilled Model) 可顯著降低運算又保持相當的AI性能，搭配開源模型的趨勢，對邊緣設備的AI推理運算有相當的助益。在未來AI 技術成長主軸，將會討論 Agentic AI 、Physical AI的發展，以及對AI產業發展的展望。(字串長度:700)```me_desc---->(字串長度:0)---->梁伯嵩博士現為 聯發科技 企業策略與前瞻技術 資深處長，與聯發科技教育基金會董事。兼任 國立臺灣大學 電機資訊學院 資訊工程系 與 重點科技研究學院 合聘之 客座教授，以及 國立陽明交通大學 電機學院 客座教授、產學創新研究學院 教授級專業技術人員。並擔任 國際電機電子工程學會 中華民國分會  (IEEE Taipei section) 理事。\n\n梁博士於 國立交通大學 電子研究所 獲得博士學位，並於 國立臺灣大學 管理學院 EMBA 商學組 畢業。曾獲得 中華民國十大傑出青年 (科技發展類)、三度獲得 經濟部智慧財產局 國家發明創作獎 (發明獎一金二銀) 、經濟部技術處 產業科技發展獎-傑出青年創新獎、資訊月傑出資訊人才獎、李國鼎青年研究獎等重要獎項。(字串長度:861)'),(6880,'2025-04-02 16:47:12',6,'company---->(字串長度:0)---->財團法人台灣人工智慧學校基金會(字串長度:45)```position---->(字串長度:0)---->秘書長(字串長度:9)```me_desc---->(字串長度:0)---->律師，專注於創新科技、開放資料、開放政府等數位治理議題及相關法律規範。2012 年以來持續參與 g0v 公民科技社群，從中習得「揪團打怪」技能：多方協作推動社會創新。\r\n2017 年協助設立台灣人工智慧學校基金會，2021 年起擔任秘書長，推動課程轉型及開源 AI 平台「風火輪計畫」。\r\n2020-2022 年擔任立法院開放國會委員會民間委員，推動完成台灣立法院首份「開放國會行動方案」。近來與台灣公民社群夥伴協作推動「數位公民素養」行動計畫作為台灣新世代社會基礎建設。(字串長度:632)'),(6881,'2025-04-02 16:47:13',7,'company---->(字串長度:0)---->臺大資工系(字串長度:15)```position---->(字串長度:0)---->教授(字串長度:6)```me_desc---->(字串長度:0)---->臺灣大學資訊工程學系教授\n研究專長: 電腦應用與架構相互最佳化、計算機架構與平行處理、計算機效能評估與最佳化、嵌入式系統設計\n\n(字串長度:182)'),(6882,'2025-04-02 16:47:13',8,'company---->(字串長度:0)---->台灣大哥大 (字串長度:16)```position---->(字串長度:0)---->資訊長(字串長度:9)```me_desc---->(字串長度:0)---->Dr. Rock Tsai, holding a Ph.D. in Management Science and Engineering from Central South University, China, currently serves as Chief Information Officer at Taiwan Mobile and Chairman of the Information Management Association (IMA). He started programming in elementary school and took on freelance projects in junior high. In the 1990s, as a freshman at National Yang Ming Chiao Tung University’s Department of Computer Science, he developed a game called “Face to Face,” which took Asia by storm, selling close to 100,000 copies. \nDr. Tsai has previously served as Senior VP at HSBC Bank (China), Group Chief Information Officer at Sinyi Realty, and Head of Technology at DBS Bank (Taiwan). He received the awards of “Most Valuable CIO in China” in 2011, “Distinguished CIO in China” in 2012, “Outstanding CIO in Taiwan” in 2017, and IMA’s “Outstanding Information Director” in 2022.  \nHe joined Taiwan Mobile in February 2022, taking on the significant responsibility of managing the group’s IT transformation. (字串長度:1039)'),(6883,'2025-04-02 16:47:14',9,'company---->(字串長度:0)---->長庚大學智慧運算學院(字串長度:30)```position---->(字串長度:0)---->院長(字串長度:6)```me_desc---->(字串長度:0)---->許永真教授現任於臺大資訊工程學系暨網路與多媒體研究所，曾任臺大資訊系主任。許教授致力於人工智慧研究領域逾三十年，研究主題含智慧型多代理人、資料分析、知識建構、常識運算、感知服務、及智慧型物聯網技術。 自2012年起，許教授擔任Intel台大創新研究中心主任，積極進行跨國、跨校、跨領域、跨產學合作，研發物聯網於智慧農業、智慧運輸、與智慧家庭之相關技術。2016年在台大、科技部、英特爾實驗室、台達電、與研華共同支持下，開始進行擴充人機互動於未來智慧製造之研究。以「臺大智慧聯網創新研究中心」的新標幟，成長發展為物聯網、人工智慧、與人機互動相關研究的重鎮。\n許教授活躍於國際學術領域，曾擔任台灣人工智慧學會理事長，TAAI、IEEE iThings、AAAI SocialNLP Workshop等國際會議的議程主席; 以及Intelligent Data Analysis, Service-Oriented Computing and Applications, JISE等國際期刊之編輯委員。許教授目前於資訊系開授人工智慧相關課程，並合授網媒所「物聯網產品設計與創新」、創新設計學院「智齡設計—老人科技福祉專題」，透過跨領域合作來激發學生創新思考與前瞻技術實現。(字串長度:1335)'),(6884,'2025-04-02 16:47:16',5,'me_desc---->(字串長度:0)---->Dr. Huang\'\'s research focuses on the investigation of computational approaches for modeling the interplay between figurative language processing and human knowledge representation, leading the pioneering work in Chinese discourse analysis. His works have been published in SCI/SSCI journals and top-tier conferences in the venues of artificial intelligence, computational linguistics, natural language processing, and information retrieval. Prior to joining the Institute of Information Science, Academia Sinica, Dr. Huang was an assistant professor in the Department of Computer Science at the National Chengchi University. His award and honors include the Honorable Mention of Doctoral Dissertation Award of ACLCLP in 2014 and the Honorable Mention of Master Thesis Award of ACLCLP in 2008. He served as the registration chair of TAAI 2017, the publication chair of ROCLING 2020, and as PC members of representative conferences in computational linguistics including ACL, COLING, EMNLP, and NAACL. He was one of organizers of FinNum Task at NTCIR-2014 and FinNLP Workshop at IJCAI 2019.(字串長度:1088)'),(6885,'2025-04-02 16:47:17',14,'me_desc---->(字串長度:0)---->林旅強｜以開源佈道為志業，以開放、自由、共享、創新為信仰。現負責華為雲 AI 開發者生態發展；曾任華為開發者社區運營總監。曾於中央研究院自由軟體鑄造場 (OSSF) 投入開源授權研究和校園社群推廣工作。(字串長度:279)'),(6886,'2025-04-02 16:47:17',15,'me_desc---->(字串長度:0)---->現任財團法人台灣人工智慧學校技術發展處副組長，是一位經驗豐富的 AI 工程師，專精於生成式人工智慧領域的應用。他的專業涵蓋了大型語言模型 (LLM) 以及提升效率的自動化平台。生成式 AI 的實際應用方面擁有深厚的經驗，曾多次應邀至學術界與產業夥伴分享其專業知識，例如在 Google、電通台灣、廣達電腦、統一超商、中華電信電信學院等知名機構進行交流與合作。他也積極在各類研討會與工作坊分享，涵蓋了生成式 AI 工具的應用、AI 技術的整合運用。致力於推廣生成式 AI 技術的普及與發展，並關注開源技術應用。憑藉其專業知識與豐富經驗，在推動生成式 AI 技術的創新與應用上扮演著重要的角色。(字串長度:811)'),(6887,'2025-04-02 16:47:17',16,'me_desc---->(字串長度:0)---->工作經歷\n\n目前任職於電通安吉斯集團，成立顧問事業-商業加速中心，協助客戶數位轉型與品牌轉型的顧顧問服務。 曾服務於安索帕數位行銷公司 (電通安吉斯集團旗下品牌公司)、意識形態廣告公司、傳立媒體、 奧美廣告、與超級電視臺。25年以上跨媒體、網路、社群行銷, 數據與技術整合行銷傳播經驗。 2000年加入安索帕，正式投入網路行銷的世界， 2004年起擔任執行副總負責臺灣區公司的公司營運，於2007年升任臺灣區董事總經理職務。 2013年8月，升任集團職務擔擔任數位長，負責電通安吉斯集團臺灣數位策略發展及布局。 2019年起擔任天下創新學院數位轉型導師。\n\n個人獎項肯定\n\n榮獲《經理人月刊》主辦之「第五屆100 MVP經理人-數位策略Digital Strategy」、2013年度傑出廣告專業經理人獎、2012年動腦雜誌和臺灣廣告主協會主辦之「2012行銷傳播傑出貢獻獎」以及獲頒2012「年度傑出網路行銷公司經營者」。(字串長度:1074)'),(6888,'2025-04-02 16:48:53',1,'company---->(字串長度:0)---->台灣人工智慧學校(字串長度:24)```position---->(字串長度:0)---->校務長(字串長度:9)'),(6889,'2025-04-02 16:48:53',2,'company---->(字串長度:0)---->台灣人工智慧學校(字串長度:24)```position---->(字串長度:0)---->執行長(字串長度:9)'),(6890,'2025-04-02 16:48:58',17,'company---->(字串長度:0)---->財團法人台灣人工智慧學校基金會(字串長度:45)```position---->(字串長度:0)---->秘書長(字串長度:9)'),(6891,'2025-04-02 16:48:58',5,'company---->(字串長度:0)---->中研院資訊科學研究所(字串長度:30)```position---->(字串長度:0)---->副研究員(字串長度:12)'),(6892,'2025-04-02 16:48:58',14,'company---->(字串長度:0)---->零一萬物開源(字串長度:18)```position---->(字串長度:0)---->前負責人(字串長度:12)'),(6893,'2025-04-02 16:48:59',15,'company---->(字串長度:0)---->台灣人工智慧學校(字串長度:24)```position---->(字串長度:0)---->技術處副組長(字串長度:18)'),(6894,'2025-04-02 16:48:59',16,'company---->(字串長度:0)---->台灣電通(字串長度:12)```position---->(字串長度:0)---->策略創新長(字串長度:15)'),(6895,'2025-04-02 16:49:00',18,'company---->(字串長度:0)---->台灣人工智慧學校(字串長度:24)```position---->(字串長度:0)---->校務長(字串長度:9)'),(6896,'2025-04-02 16:52:09',1,'position---->校務長(字串長度:9)---->產學長(字串長度:9)'),(6897,'2025-04-02 16:52:13',17,'me_desc---->(字串長度:0)---->律師，專注於創新科技、開放資料、開放政府等數位治理議題及相關法律規範。2012 年以來持續參與 g0v 公民科技社群，從中習得「揪團打怪」技能：多方協作推動社會創新。\n\n2017 年協助設立台灣人工智慧學校基金會，2021 年起擔任秘書長，推動課程轉型及開源 AI 平台「風火輪計畫」。\n\n2020-2022 年擔任立法院開放國會委員會民間委員，推動完成台灣立法院首份「開放國會行動方案」。近來與台灣公民社群夥伴協作推動「數位公民素養」行動計畫作為台灣新世代社會基礎建設。(字串長度:632)'),(6898,'2025-04-02 16:52:15',18,'me_desc---->(字串長度:0)---->Richie Tsai is the Provost of the Taiwan AI Academy, Taiwan\'s leading institution in AI education. He oversees the Academy\'s operations, consulting services, and research development, transforming it into a dynamic platform for collaboration across AI disciplines and sectors. Taiwan AI Academy educates over 11,000 AI talents from 2,200 organizations in 15 industries. \nBefore joining Taiwan AI Academy, Tsai served as Senior Director for SAP Greater China, overseeing market strategy, technical services, and business operations. His previous roles include senior roles at Oracle and  at Teradata. With over 25 years of experience in emerging information technology fields such as artificial intelligence, data science, cloud services, and digital transformation, Richie is an AI evangelist with deep expertise in information technology and ecosystem development.\n\n現職：\n- 台灣人工智慧學校校務長\n- 國科會Taiwan AI 卓越中心策略委員\n- 中華民國人工智慧學會理事\n\n經歷:\n- 台灣人工智慧學校營運長/ 代理執行長\n- SAP 德商思愛普 大中華區資深副總經理\n- Oracle 美商甲骨文 應用軟體部副總經理\n- Teradata 美商天睿資訊系統 資深協理\n\n學歷:\n國立清華大學工學博士候選人(字串長度:1263)'),(6899,'2025-04-02 16:53:04',1,'hide---->0(字串長度:1)---->1(字串長度:1)'),(6900,'2025-04-02 16:55:54',9,'hide---->1(字串長度:1)---->0(字串長度:1)'),(6901,'2025-04-02 17:06:13',2,'hide---->1(字串長度:1)---->0(字串長度:1)'),(6902,'2025-04-02 17:19:01',2,'me_desc---->經歷：\r\n資訊服務處處長（2018/01/01 迄今）\r\n資訊服務處代理處長（2017/10/01-2017/12/31）\r\n研究員，資訊科學研究所 （2017/12 迄今）\r\n副研究員，資訊科學研究所 （2011/01–2017/12）\r\n助研究員，資訊科學研究所 （2005/09–2011/01）\r\n\r\n學歷：\r\n博士，資訊科學系，加州大學洛杉磯分校，美國 （2003/01–2005/06）\r\n碩士，資訊科學系，加州大學洛杉磯分校，美國 （2001/09–2002/12）\r\n大學，資訊教育系，國立臺灣師範大學，中華民國 （1993/09–1998/06）(字串長度:574)---->經歷：\n資訊服務處處長（2018/01/01 迄今）\n資訊服務處代理處長（2017/10/01-2017/12/31）\n研究員，資訊科學研究所 （2017/12 迄今）\n副研究員，資訊科學研究所 （2011/01–2017/12）\n助研究員，資訊科學研究所 （2005/09–2011/01）\n\n學歷：\n博士，資訊科學系，加州大學洛杉磯分校，美國 （2003/01–2005/06）\n碩士，資訊科學系，加州大學洛杉磯分校，美國 （2001/09–2002/12）\n大學，資訊教育系，國立臺灣師範大學，中華民國 （1993/09–1998/06）(字串長度:564)'),(6903,'2025-04-02 17:19:02',3,'me_desc---->國家科學及技術委員會副主任委員\r\n主要研究領域\r\n• 人機互動\r\n• 電腦圖學\r\n• 視訊及影像處理\r\n• 創新創業教育(字串長度:152)---->國家科學及技術委員會副主任委員\n主要研究領域\n• 人機互動\n• 電腦圖學\n• 視訊及影像處理\n• 創新創業教育(字串長度:147)'),(6904,'2025-04-02 17:19:02',4,'me_desc---->學歷\r\n1995 美國布朗大學電腦科學博士\r\n1987 臺灣大學資訊工程系學士\r\n經歷\r\n2002-2024 艾爾科技創辦人兼執行長\r\n2021-2023 印刻文學生活誌專欄作家\r\n1999-2001 趨勢科技新產品研發部協理\r\n1996-1999 華通電腦資訊工程部副理\r\n1995-1996 美國 IBM 華生研究中心博士後研究員(字串長度:339)---->學歷\n1995 美國布朗大學電腦科學博士\n1987 臺灣大學資訊工程系學士\n經歷\n2002-2024 艾爾科技創辦人兼執行長\n2021-2023 印刻文學生活誌專欄作家\n1999-2001 趨勢科技新產品研發部協理\n1996-1999 華通電腦資訊工程部副理\n1995-1996 美國 IBM 華生研究中心博士後研究員(字串長度:331)'),(6905,'2025-04-02 17:19:02',13,'me_desc---->梁伯嵩博士現為 聯發科技 企業策略與前瞻技術 資深處長，與聯發科技教育基金會董事。兼任 國立臺灣大學 電機資訊學院 資訊工程系 與 重點科技研究學院 合聘之 客座教授，以及 國立陽明交通大學 電機學院 客座教授、產學創新研究學院 教授級專業技術人員。並擔任 國際電機電子工程學會 中華民國分會  (IEEE Taipei section) 理事。\r\n\r\n梁博士於 國立交通大學 電子研究所 獲得博士學位，並於 國立臺灣大學 管理學院 EMBA 商學組 畢業。曾獲得 中華民國十大傑出青年 (科技發展類)、三度獲得 經濟部智慧財產局 國家發明創作獎 (發明獎一金二銀) 、經濟部技術處 產業科技發展獎-傑出青年創新獎、資訊月傑出資訊人才獎、李國鼎青年研究獎等重要獎項。(字串長度:863)---->梁伯嵩博士現為 聯發科技 企業策略與前瞻技術 資深處長，與聯發科技教育基金會董事。兼任 國立臺灣大學 電機資訊學院 資訊工程系 與 重點科技研究學院 合聘之 客座教授，以及 國立陽明交通大學 電機學院 客座教授、產學創新研究學院 教授級專業技術人員。並擔任 國際電機電子工程學會 中華民國分會  (IEEE Taipei section) 理事。\n\n梁博士於 國立交通大學 電子研究所 獲得博士學位，並於 國立臺灣大學 管理學院 EMBA 商學組 畢業。曾獲得 中華民國十大傑出青年 (科技發展類)、三度獲得 經濟部智慧財產局 國家發明創作獎 (發明獎一金二銀) 、經濟部技術處 產業科技發展獎-傑出青年創新獎、資訊月傑出資訊人才獎、李國鼎青年研究獎等重要獎項。(字串長度:861)'),(6906,'2025-04-02 17:19:03',7,'me_desc---->臺灣大學資訊工程學系教授\r\n研究專長: 電腦應用與架構相互最佳化、計算機架構與平行處理、計算機效能評估與最佳化、嵌入式系統設計\r\n\r\n(字串長度:185)---->臺灣大學資訊工程學系教授\n研究專長: 電腦應用與架構相互最佳化、計算機架構與平行處理、計算機效能評估與最佳化、嵌入式系統設計\n\n(字串長度:182)'),(6907,'2025-04-02 17:19:04',8,'me_desc---->Dr. Rock Tsai, holding a Ph.D. in Management Science and Engineering from Central South University, China, currently serves as Chief Information Officer at Taiwan Mobile and Chairman of the Information Management Association (IMA). He started programming in elementary school and took on freelance projects in junior high. In the 1990s, as a freshman at National Yang Ming Chiao Tung University’s Department of Computer Science, he developed a game called “Face to Face,” which took Asia by storm, selling close to 100,000 copies. \r\nDr. Tsai has previously served as Senior VP at HSBC Bank (China), Group Chief Information Officer at Sinyi Realty, and Head of Technology at DBS Bank (Taiwan). He received the awards of “Most Valuable CIO in China” in 2011, “Distinguished CIO in China” in 2012, “Outstanding CIO in Taiwan” in 2017, and IMA’s “Outstanding Information Director” in 2022.  \r\nHe joined Taiwan Mobile in February 2022, taking on the significant responsibility of managing the group’s IT transformation. (字串長度:1041)---->Dr. Rock Tsai, holding a Ph.D. in Management Science and Engineering from Central South University, China, currently serves as Chief Information Officer at Taiwan Mobile and Chairman of the Information Management Association (IMA). He started programming in elementary school and took on freelance projects in junior high. In the 1990s, as a freshman at National Yang Ming Chiao Tung University’s Department of Computer Science, he developed a game called “Face to Face,” which took Asia by storm, selling close to 100,000 copies. \nDr. Tsai has previously served as Senior VP at HSBC Bank (China), Group Chief Information Officer at Sinyi Realty, and Head of Technology at DBS Bank (Taiwan). He received the awards of “Most Valuable CIO in China” in 2011, “Distinguished CIO in China” in 2012, “Outstanding CIO in Taiwan” in 2017, and IMA’s “Outstanding Information Director” in 2022.  \nHe joined Taiwan Mobile in February 2022, taking on the significant responsibility of managing the group’s IT transformation. (字串長度:1039)'),(6908,'2025-04-02 17:19:04',9,'me_desc---->許永真教授現任於臺大資訊工程學系暨網路與多媒體研究所，曾任臺大資訊系主任。許教授致力於人工智慧研究領域逾三十年，研究主題含智慧型多代理人、資料分析、知識建構、常識運算、感知服務、及智慧型物聯網技術。 自2012年起，許教授擔任Intel台大創新研究中心主任，積極進行跨國、跨校、跨領域、跨產學合作，研發物聯網於智慧農業、智慧運輸、與智慧家庭之相關技術。2016年在台大、科技部、英特爾實驗室、台達電、與研華共同支持下，開始進行擴充人機互動於未來智慧製造之研究。以「臺大智慧聯網創新研究中心」的新標幟，成長發展為物聯網、人工智慧、與人機互動相關研究的重鎮。\r\n許教授活躍於國際學術領域，曾擔任台灣人工智慧學會理事長，TAAI、IEEE iThings、AAAI SocialNLP Workshop等國際會議的議程主席; 以及Intelligent Data Analysis, Service-Oriented Computing and Applications, JISE等國際期刊之編輯委員。許教授目前於資訊系開授人工智慧相關課程，並合授網媒所「物聯網產品設計與創新」、創新設計學院「智齡設計—老人科技福祉專題」，透過跨領域合作來激發學生創新思考與前瞻技術實現。(字串長度:1336)---->許永真教授現任於臺大資訊工程學系暨網路與多媒體研究所，曾任臺大資訊系主任。許教授致力於人工智慧研究領域逾三十年，研究主題含智慧型多代理人、資料分析、知識建構、常識運算、感知服務、及智慧型物聯網技術。 自2012年起，許教授擔任Intel台大創新研究中心主任，積極進行跨國、跨校、跨領域、跨產學合作，研發物聯網於智慧農業、智慧運輸、與智慧家庭之相關技術。2016年在台大、科技部、英特爾實驗室、台達電、與研華共同支持下，開始進行擴充人機互動於未來智慧製造之研究。以「臺大智慧聯網創新研究中心」的新標幟，成長發展為物聯網、人工智慧、與人機互動相關研究的重鎮。\n許教授活躍於國際學術領域，曾擔任台灣人工智慧學會理事長，TAAI、IEEE iThings、AAAI SocialNLP Workshop等國際會議的議程主席; 以及Intelligent Data Analysis, Service-Oriented Computing and Applications, JISE等國際期刊之編輯委員。許教授目前於資訊系開授人工智慧相關課程，並合授網媒所「物聯網產品設計與創新」、創新設計學院「智齡設計—老人科技福祉專題」，透過跨領域合作來激發學生創新思考與前瞻技術實現。(字串長度:1335)'),(6909,'2025-04-02 17:19:06',17,'hide---->0(字串長度:1)---->1(字串長度:1)'),(6910,'2025-04-02 17:19:07',16,'me_desc---->工作經歷\r\n\r\n目前任職於電通安吉斯集團，成立顧問事業-商業加速中心，協助客戶數位轉型與品牌轉型的顧顧問服務。 曾服務於安索帕數位行銷公司 (電通安吉斯集團旗下品牌公司)、意識形態廣告公司、傳立媒體、 奧美廣告、與超級電視臺。25年以上跨媒體、網路、社群行銷, 數據與技術整合行銷傳播經驗。 2000年加入安索帕，正式投入網路行銷的世界， 2004年起擔任執行副總負責臺灣區公司的公司營運，於2007年升任臺灣區董事總經理職務。 2013年8月，升任集團職務擔擔任數位長，負責電通安吉斯集團臺灣數位策略發展及布局。 2019年起擔任天下創新學院數位轉型導師。\r\n\r\n個人獎項肯定\r\n\r\n榮獲《經理人月刊》主辦之「第五屆100 MVP經理人-數位策略Digital Strategy」、2013年度傑出廣告專業經理人獎、2012年動腦雜誌和臺灣廣告主協會主辦之「2012行銷傳播傑出貢獻獎」以及獲頒2012「年度傑出網路行銷公司經營者」。(字串長度:1080)---->工作經歷\n\n目前任職於電通安吉斯集團，成立顧問事業-商業加速中心，協助客戶數位轉型與品牌轉型的顧顧問服務。 曾服務於安索帕數位行銷公司 (電通安吉斯集團旗下品牌公司)、意識形態廣告公司、傳立媒體、 奧美廣告、與超級電視臺。25年以上跨媒體、網路、社群行銷, 數據與技術整合行銷傳播經驗。 2000年加入安索帕，正式投入網路行銷的世界， 2004年起擔任執行副總負責臺灣區公司的公司營運，於2007年升任臺灣區董事總經理職務。 2013年8月，升任集團職務擔擔任數位長，負責電通安吉斯集團臺灣數位策略發展及布局。 2019年起擔任天下創新學院數位轉型導師。\n\n個人獎項肯定\n\n榮獲《經理人月刊》主辦之「第五屆100 MVP經理人-數位策略Digital Strategy」、2013年度傑出廣告專業經理人獎、2012年動腦雜誌和臺灣廣告主協會主辦之「2012行銷傳播傑出貢獻獎」以及獲頒2012「年度傑出網路行銷公司經營者」。(字串長度:1074)'),(6911,'2025-04-02 21:09:02',6,'hide---->0(字串長度:1)---->1(字串長度:1)'),(6912,'2025-04-02 21:09:05',17,'hide---->1(字串長度:1)---->0(字串長度:1)'),(6913,'2025-04-02 21:09:07',18,'me_desc---->Richie Tsai is the Provost of the Taiwan AI Academy, Taiwan\'s leading institution in AI education. He oversees the Academy\'s operations, consulting services, and research development, transforming it into a dynamic platform for collaboration across AI disciplines and sectors. Taiwan AI Academy educates over 11,000 AI talents from 2,200 organizations in 15 industries. \r\nBefore joining Taiwan AI Academy, Tsai served as Senior Director for SAP Greater China, overseeing market strategy, technical services, and business operations. His previous roles include senior roles at Oracle and  at Teradata. With over 25 years of experience in emerging information technology fields such as artificial intelligence, data science, cloud services, and digital transformation, Richie is an AI evangelist with deep expertise in information technology and ecosystem development.\r\n\r\n現職：\r\n- 台灣人工智慧學校校務長\r\n- 國科會Taiwan AI 卓越中心策略委員\r\n- 中華民國人工智慧學會理事\r\n\r\n經歷:\r\n- 台灣人工智慧學校營運長/ 代理執行長\r\n- SAP 德商思愛普 大中華區資深副總經理\r\n- Oracle 美商甲骨文 應用軟體部副總經理\r\n- Teradata 美商天睿資訊系統 資深協理\r\n\r\n學歷:\r\n國立清華大學工學博士候選人(字串長度:1278)---->Richie Tsai is the Provost of the Taiwan AI Academy, Taiwan\'s leading institution in AI education. He oversees the Academy\'s operations, consulting services, and research development, transforming it into a dynamic platform for collaboration across AI disciplines and sectors. Taiwan AI Academy educates over 11,000 AI talents from 2,200 organizations in 15 industries. \nBefore joining Taiwan AI Academy, Tsai served as Senior Director for SAP Greater China, overseeing market strategy, technical services, and business operations. His previous roles include senior roles at Oracle and  at Teradata. With over 25 years of experience in emerging information technology fields such as artificial intelligence, data science, cloud services, and digital transformation, Richie is an AI evangelist with deep expertise in information technology and ecosystem development.\n\n現職：\n- 台灣人工智慧學校校務長\n- 國科會Taiwan AI 卓越中心策略委員\n- 中華民國人工智慧學會理事\n\n經歷:\n- 台灣人工智慧學校營運長/ 代理執行長\n- SAP 德商思愛普 大中華區資深副總經理\n- Oracle 美商甲骨文 應用軟體部副總經理\n- Teradata 美商天睿資訊系統 資深協理\n\n學歷:\n國立清華大學工學博士候選人(字串長度:1263)');

--
-- Table structure for table `speakers_changelog20200922`
--

CREATE TABLE `speakers_changelog20200922` (
  `log_id` int unsigned NOT NULL AUTO_INCREMENT,
  `changetime` datetime NOT NULL,
  `speaker_id` smallint unsigned NOT NULL,
  `changelog` longtext CHARACTER SET utf8mb3 COLLATE utf8mb3_unicode_ci NOT NULL,
  PRIMARY KEY (`log_id`)
) ENGINE=InnoDB AUTO_INCREMENT=473 DEFAULT CHARSET=latin1;

--
-- Dumping data for table `speakers_changelog20200922`
--

INSERT INTO `speakers_changelog20200922` VALUES (1,'2017-08-22 00:00:00',1,'name---->馬TEST(字串長度:7)---->馬匡六(字串長度:9)'),(2,'2017-08-22 08:45:30',1,'name---->馬匡六(字串長度:9)---->馬11匡六(字串長度:11)'),(3,'2017-08-22 09:09:05',1,'name---->馬11匡六(字串長度:11)---->馬匡六(字串長度:9)```photo---->(字串長度:0)---->https://drive.google.com/open?id=0B8aPckZapD1sR0JQV2NVTHpocDA(字串長度:61)'),(4,'2017-08-22 09:09:05',2,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0Bw_OtFTydVCySU94dXlYZGFYOHc(字串長度:61)'),(5,'2017-08-22 09:09:05',3,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0B6_uKxs0uwmubnhwZDdIRkN2eWc(字串長度:61)'),(6,'2017-08-22 09:09:05',4,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0ByJgUdTb1N2CeE1TYmhhdTFOVzg(字串長度:61)'),(7,'2017-08-22 09:09:05',5,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0B1-7HeusmFbPTWk0cXdiLUpUc1E(字串長度:61)'),(8,'2017-08-22 09:09:05',6,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0B59IzUnV5CLjcXNmRHFrVkE3NjQ(字串長度:61)'),(9,'2017-08-22 09:09:05',7,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0B9mHtmglofDCU214NVpjbkRlVk0(字串長度:61)'),(10,'2017-08-22 09:09:05',8,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0Bx2Wt2CYnDW2NkdtVFNhTDRETUU(字串長度:61)'),(11,'2017-08-22 09:13:18',1,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0B8aPckZapD1sR0JQV2NVTHpocDA(字串長度:61)'),(12,'2017-08-22 09:13:18',2,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0Bw_OtFTydVCySU94dXlYZGFYOHc(字串長度:61)'),(13,'2017-08-22 09:13:18',3,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0B6_uKxs0uwmubnhwZDdIRkN2eWc(字串長度:61)'),(14,'2017-08-22 09:13:18',4,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0ByJgUdTb1N2CeE1TYmhhdTFOVzg(字串長度:61)'),(15,'2017-08-22 09:13:18',5,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0B1-7HeusmFbPTWk0cXdiLUpUc1E(字串長度:61)'),(16,'2017-08-22 09:13:19',6,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0B59IzUnV5CLjcXNmRHFrVkE3NjQ(字串長度:61)'),(17,'2017-08-22 09:13:19',7,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0B9mHtmglofDCU214NVpjbkRlVk0(字串長度:61)'),(18,'2017-08-22 09:13:19',8,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0Bx2Wt2CYnDW2NkdtVFNhTDRETUU(字串長度:61)'),(19,'2017-08-22 09:13:19',9,'photo---->(字串長度:0)---->https://drive.google.com/open?id=0ByqkbzbmgT9kWkJEMTBmWVAyalU(字串長度:61)'),(20,'2017-08-22 09:22:16',1,'website---->http://(字串長度:7)---->http://ma@cs.ucdavis.edu(字串長度:24)'),(21,'2017-08-22 09:22:16',2,'website---->http://(字串長度:7)---->https://www.iis.sinica.edu.tw/pages/changyc(字串長度:43)'),(22,'2017-08-22 09:22:24',3,'website---->http://(字串長度:7)---->https://about.me/jazzwang(字串長度:25)'),(23,'2017-08-22 09:22:24',4,'website---->http://(字串長度:7)---->http://aliensunmin.github.io(字串長度:28)'),(24,'2017-08-22 09:22:24',7,'website---->http://(字串長度:7)---->http://scchen.com(字串長度:17)'),(25,'2017-08-22 09:22:25',8,'website---->http://(字串長度:7)---->http://www.atgenomix.com(字串長度:24)'),(26,'2017-08-24 01:43:14',1,'photo---->/img/speaker/00e0feac.png(字串長度:25)---->https://drive.google.com/open?id=0B8aPckZapD1sR0JQV2NVTHpocDA(字串長度:61)'),(27,'2017-08-25 09:24:41',1,'conf---->(字串長度:0)---->DSK(字串長度:3)```photo---->/img/speaker/35d7ecbe.png(字串長度:25)---->https://drive.google.com/open?id=0B8aPckZapD1sR0JQV2NVTHpocDA(字串長度:61)'),(28,'2017-08-25 09:24:41',2,'conf---->(字串長度:0)---->DS(字串長度:2)'),(29,'2017-08-25 09:24:41',3,'conf---->(字串長度:0)---->DS(字串長度:2)'),(30,'2017-08-25 09:24:42',4,'conf---->(字串長度:0)---->AI(字串長度:2)'),(31,'2017-08-25 09:24:42',5,'conf---->(字串長度:0)---->DS(字串長度:2)'),(32,'2017-08-25 09:24:42',6,'conf---->(字串長度:0)---->DS(字串長度:2)'),(33,'2017-08-25 09:24:42',7,'conf---->(字串長度:0)---->DS(字串長度:2)'),(34,'2017-08-25 09:24:42',8,'conf---->(字串長度:0)---->DS(字串長度:2)'),(35,'2017-08-25 09:24:42',9,'conf---->(字串長度:0)---->AI(字串長度:2)'),(36,'2017-08-26 08:49:02',1,'conf---->(字串長度:0)---->DSK(字串長度:3)```photo---->/img/speaker/35d7ecbe.png(字串長度:25)---->https://drive.google.com/open?id=0B8aPckZapD1sR0JQV2NVTHpocDA(字串長度:61)'),(37,'2017-08-26 08:49:02',2,'photo---->/img/speaker/2d4d1967.jpg(字串長度:25)---->https://drive.google.com/open?id=0Bw_OtFTydVCySU94dXlYZGFYOHc(字串長度:61)'),(38,'2017-08-26 08:49:02',3,'photo---->/img/speaker/ca5c6bd8.png(字串長度:25)---->https://drive.google.com/open?id=0B6_uKxs0uwmubnhwZDdIRkN2eWc(字串長度:61)'),(39,'2017-08-26 08:49:02',4,'photo---->/img/speaker/ea31451e.jpg(字串長度:25)---->https://drive.google.com/open?id=0ByJgUdTb1N2CeE1TYmhhdTFOVzg(字串長度:61)'),(40,'2017-08-26 08:49:02',5,'conf---->DS(字串長度:2)---->(字串長度:0)```photo---->/img/speaker/f9521fcf.jpg(字串長度:25)---->https://drive.google.com/open?id=0B1-7HeusmFbPTWk0cXdiLUpUc1E(字串長度:61)'),(41,'2017-08-26 08:49:02',11,'website---->http://homepage.ntu.edu.tw/~schhuang/Welcome.html(字串長度:49)---->http://homepage.ntu.edu.tw/~schhuang/Welcome.html , http://web.ntnu.edu.tw/~hlshao/(字串長度:83)'),(42,'2017-08-28 01:56:59',5,'conf---->(字串長度:0)---->DS(字串長度:2)'),(43,'2017-08-28 01:56:59',11,'website---->http://homepage.ntu.edu.tw/~schhuang/Welcome.html , http://web.ntnu.edu.tw/~hlshao/(字串長度:83)---->http://homepage.ntu.edu.tw/~schhuang/Welcome.html(字串長度:49)'),(44,'2017-08-28 01:59:12',11,'website---->http://homepage.ntu.edu.tw/~schhuang/Welcome.html(字串長度:49)---->http://homepage.ntu.edu.tw/~schhuang/Welcome.html , http://web.ntnu.edu.tw/~hlshao/(字串長度:83)'),(45,'2017-08-28 02:33:08',16,'name2---->N/A(字串長度:3)---->Meeyoung Cha(字串長度:12)```company---->N/A(字串長度:3)---->KAIST / School of Computing (字串長度:28)```position---->N/A(字串長度:3)---->associate professor(字串長度:19)'),(46,'2017-08-28 03:47:14',16,'desc---->Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:790)---->Social platforms are an ideal place for spreading rumors and fake news. As more people seek information and read news online, automatically debunking such false claims has become an urgent problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand their propagation processes. In detecting rumors and fake news in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early detection. I will also highlight the latest detection studies with deep learning techniques.<br><br> Short Bio Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:1560)'),(47,'2017-08-28 08:55:23',1,'name---->馬匡六(字串長度:9)---->男(字串長度:3)```name2---->Kwan-Liu Ma(字串長度:11)---->University of California at Davis(字串長度:33)```front_photo---->https://drive.google.com/open?id=0B8aPckZapD1sR0JQV2NVTHpocDA(字串長度:61)---->是(字串長度:3)```talk_title---->Big Data Visualization(字串長度:22)---->Advanced computing and imaging technologies enable scientists to study natural phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Internet and mobile device users is expected to be even greater.  To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis methods. Visualization transforms large quantities of, often multiple-dimensional, data into graphical representations that exploit the high-bandwidth channel of the human visual system, leveraging the brain\'s remarkable ability to detect patterns and draw inferences.  It has been shown very effective in understanding large, complex data, and thus become an indispensable tool for many areas of research and practice. I will present several use cases of visualization based on new concepts and techniques that my group at UC Davis has introduced to further advance the visualization technology as a powerful discovery and communication tool.(字串長度:1113)```company---->University of California at Davis(字串長度:33)---->Professor(字串長度:9)```position---->Professor(字串長度:9)---->是(字串長度:3)```desc---->Advanced computing and imaging technologies enable scientists to study natural phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Internet and mobile device users is expected to be even greater.<br><br>To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis methods. Visualization transforms large quantities of, often multiple-dimensional, data into graphical representations that exploit the high-bandwidth channel of the human visual system, leveraging the brain\'s remarkable ability to detect patterns and draw inferences.<br><br>It has been shown very effective in understanding large, complex data, and thus become an indispensable tool for many areas of research and practice. I will present several use cases of visualization based on new concepts and techniques that my group at UC Davis has introduced to further advance the visualization technology as a powerful discovery and communication tool.(字串長度:1125)---->Kwan-Liu Ma (馬匡六) is a professor of computer science and the chair of the Graduate Group in Computer Science (GGCS) at the University of California-Davis, where he directs VIDI Labs and UC Davis Center of Excellence for Visualization. He is also a visiting professor of the National Taiwan University and a chair professor of the National Chiao Tung University. His research spans the fields of visualization, computer graphics, high-performance computing, and user interface design. Professor Ma received his PhD in computer science from the University of Utah in 1993. During 1993-1999, he was with ICASE/NASA Langley Research Center as a research scientist. He joined UC Davis in 1999.<br><br>Professor Ma is presently leading a team of 25 researchers pursuing projects driven by a wide range of real-world applications.<br><br>He received the NSF Presidential Early-Career Research Award (PECASE) in 2000, was elected an IEEE Fellow in 2012, and received the 2013 IEEE VGTC Visualization Technical Achievement Award for his outstanding research work. Professor Ma has been actively serving the research community by playing leading roles in many<br><br>professional activities in the area of visualization. He presently serves as the Associate Editor-in-Chief of the<br><br>IEEE Computer Graphics and Applications (CG&amp;A), and on the editorial board of the Journal of Visualization, the Journal of Visual Informatics, and the Journal of Computational Visual Media.(字串長度:1477)```conf---->DSK(字串長度:3)---->馬匡六(字串長度:9)```website---->http://ma@cs.ucdavis.edu(字串長度:24)---->http://Kwan-Liu Ma(字串長度:18)'),(48,'2017-08-28 08:55:23',2,'name---->張詠淳(字串長度:9)---->男(字串長度:3)```name2---->張詠淳(字串長度:9)---->台北醫學大學大數據科技與管理研究所(字串長度:51)```front_photo---->https://drive.google.com/open?id=0Bw_OtFTydVCySU94dXlYZGFYOHc(字串長度:61)---->是(字串長度:3)```talk_title---->一窺文字探勘之奧妙(字串長度:27)---->在這個網路普及的時代，社群多媒體已經成為人們分享與取得知識的重要來源，讓我們一同探索文字探勘技術用於社群多媒體分析的奧妙吧！！(字串長度:189)```company---->台北醫學大學大數據科技與管理研究所(字串長度:51)---->博士(字串長度:6)```position---->博士(字串長度:6)---->是(字串長度:3)```desc---->在這個網路普及的時代，社群多媒體已經成為人們分享與取得知識的重要來源，讓我們一同探索文字探勘技術用於社群多媒體分析的奧妙吧！！(字串長度:189)---->張詠淳博士專精於文字探勘與檢索、對談式問答機器人，以及相關的機器學習與語言辨識技術。張博士自2008年就在中央研究院服國防役，其間參與了自然輸入法開發，於2016年擔任博士後研究員，並榮獲中研院博士後傑出研究獎。張博士近年來發表多篇國內外期刊論文以及會議論文，其中包含了頂尖期刊TKDE以及頂尖國際會議論文ACL等。近期的研究重點之一在於結合句構知識表達與機器學習，打造一個具深度理解的智慧型對談機器人。另一個研究重點則是基於過去在文字分類上相關的研究成果，研發一個全自動的文字分類架構，此應用非常廣泛，例如：情感分析、主題偵測、甚至是問答系統等。(字串長度:792)```conf---->DS(字串長度:2)---->張詠淳(字串長度:9)```website---->https://www.iis.sinica.edu.tw/pages/changyc(字串長度:43)---->http://張詠淳(字串長度:16)'),(49,'2017-08-28 08:55:23',3,'name---->王耀聰(字串長度:9)---->男(字串長度:3)```name2---->Jazz Wang(字串長度:9)---->TenMax(字串長度:6)```front_photo---->https://drive.google.com/open?id=0B6_uKxs0uwmubnhwZDdIRkN2eWc(字串長度:61)---->否(字串長度:3)```talk_title---->Data Pipeline Matters -- 以 Tracking Pixel 為例(字串長度:50)---->中小企業與新創團隊看過來～本分享將以訪客追蹤像素（Tracking Pixel）為例，說明如何蒐集網站訪客的線上行為，並比較不同的資料匯流（Data Pipeline）設計的成本結構，藉此說明近期雲服務 Serverless 與微服務 (Microservices) 的趨勢如何幫您省錢。(字串長度:320)```company---->TenMax(字串長度:6)---->Data Architect(字串長度:14)```position---->Data Architect(字串長度:14)---->是(字串長度:3)```desc---->中小企業與新創團隊看過來～本分享將以訪客追蹤像素（Tracking Pixel）為例，說明如何蒐集網站訪客的線上行為，並比較不同的資料匯流（Data Pipeline）設計的成本結構，藉此說明近期雲服務 Serverless 與微服務 (Microservices) 的趨勢如何幫您省錢。(字串長度:320)---->- 台灣資料工程協會發起人 - 台灣 Hadoop 使用者社群共同創辦人/傳教士 - Hadoop : The Definitive Guide、Hadoop Operations 兩書譯者(字串長度:157)```conf---->DS(字串長度:2)---->王耀聰(字串長度:9)```website---->https://about.me/jazzwang(字串長度:25)---->http://Jazz Wang(字串長度:16)'),(50,'2017-08-28 08:55:23',4,'name---->孫民(字串長度:6)---->男(字串長度:3)```name2---->孫民(字串長度:6)---->國立清華大學(字串長度:18)```front_photo---->https://drive.google.com/open?id=0ByJgUdTb1N2CeE1TYmhhdTFOVzg(字串長度:61)---->是(字串長度:3)```talk_title---->Training a Deep Agent to See and Interact(字串長度:41)---->An intelligent agent should have the abilities to see and interact with the world in many different ways. In this talk, I summarize our recent work on seeing and interacting using language (e.g., video captioning), interacting by taking actions in specific applications (e.g., viewing angle selection in 360 videos), and interacting by attacking other agents. Ultimately, we long for an embodied intelligent agent to assist us in our daily life. Hence, we also propose a system to anticipate human intention in order to proactively provide assistance.(字串長度:551)```company---->國立清華大學(字串長度:18)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->是(字串長度:3)```desc---->An intelligent agent should have the abilities to see and interact with the world in many different ways. In this talk, I summarize our recent work on seeing and interacting using language (e.g., video captioning), interacting by taking actions in specific applications (e.g., viewing angle selection in 360 videos), and interacting by attacking other agents. Ultimately, we long for an embodied intelligent agent to assist us in our daily life. Hence, we also propose a system to anticipate human intention in order to proactively provide assistance.(字串長度:551)---->Min Sun is an assistant professor at National Tsing Hua University in Taiwan. Before that, he was a postdoctoral researcher at Washington University in Seattle and he graduated from the University of Michigan with a Ph.D. degree in EE: System. He also won the best paper award of 3dRR in 2007, paper award of CVGIP in 2015, 2016, and 2017, and the winner of Thor challenge in CVPR 2017.(字串長度:386)```conf---->DS(字串長度:2)---->孫民(字串長度:6)```website---->http://aliensunmin.github.io(字串長度:28)---->http://孫民(字串長度:13)'),(51,'2017-08-28 08:55:23',5,'name---->胡筱薇(字串長度:9)---->女(字串長度:3)```name2---->Camille Hu(字串長度:10)---->School of BigData Management(字串長度:28)```front_photo---->https://drive.google.com/open?id=0B1-7HeusmFbPTWk0cXdiLUpUc1E(字串長度:61)---->否(字串長度:3)```company---->School of BigData Management(字串長度:28)---->Assistant Professor(字串長度:19)```position---->Assistant Professor(字串長度:19)---->是(字串長度:3)```desc---->TBA(字串長度:3)---->喜愛新鮮的事物，對於資訊科技充滿熱情，外表看起來很活潑，但骨子裡是個阿宅，沒事喜歡窩在家裡觀察其他阿宅都在做什麼，愛家、愛耶穌，目前擔任東吳大學巨量資料管理學院助理教授，勉強算是個Data Watcher，也勉強算是個Data Player，近年來致力於巨量資料探勘以及社群網路分析應用之研究，同時也成立了資料實驗室(Data Lab)，並透過實際的專案項目，培養巨量資料分析人才。(字串長度:507)```conf---->DS(字串長度:2)---->胡筱薇(字串長度:9)```website---->(字串長度:0)---->http://Camille Hu(字串長度:17)'),(52,'2017-08-28 08:55:23',6,'name---->曾俊雄(字串長度:9)---->男(字串長度:3)```name2---->曾俊雄(字串長度:9)---->元智大學通訊工程系(字串長度:27)```front_photo---->https://drive.google.com/open?id=0B59IzUnV5CLjcXNmRHFrVkE3NjQ(字串長度:61)---->否(字串長度:3)```talk_title---->課堂資料學：到底有沒有在上課(字串長度:42)---->身為教職人員，看著講臺下的學子們，不免有一些期許，但是，我經常不由得問自己，小朋友們到底有沒有在聽課呢? 因緣際會，接觸了情感學習這個領域，帶著實驗團隊找了一些從情感反映來窺探小朋友想法的小研究，在本次演講中，會試著將這些研究結果進行說明及整理。(字串長度:365)```company---->元智大學通訊工程系(字串長度:27)---->助理教授/軟體工坊主持人(字串長度:34)```position---->助理教授/軟體工坊主持人(字串長度:34)---->是(字串長度:3)```desc---->身為教職人員，看著講臺下的學子們，不免有一些期許，但是，我經常不由得問自己，小朋友們到底有沒有在聽課呢? 因緣際會，接觸了情感學習這個領域，帶著實驗團隊找了一些從情感反映來窺探小朋友想法的小研究，在本次演講中，會試著將這些研究結果進行說明及整理。(字串長度:365)---->我常覺得我們處於一個很有趣的時代，有機會親身體會到應用領域轉變的好幾個過程：首先是從一般單機型應用轉變成伺服端的架構，另外這幾年慢慢從伺服端架構又轉變成web-based的架構，大家討論的議題又從分散式計算轉成grid computing、web service，big data，以及 cloud computing，這幾年人工智慧又重新受到關注。面對這樣的轉變，我覺得過去我們談論的許多模型，不論是程式開發的模型，或是分散式計算的處理，其實有機會作一些調整，悠遊於新的時代，有很多新東西可以玩，在這樣的時間點做一個研究人員，是很幸福的。(字串長度:694)```conf---->DS(字串長度:2)---->曾俊雄(字串長度:9)```website---->(字串長度:0)---->http://曾俊雄(字串長度:16)'),(53,'2017-08-28 08:55:23',7,'name---->陳紹慶(字串長度:9)---->男(字串長度:3)```name2---->陳紹慶(字串長度:9)---->慈濟大學(字串長度:12)```front_photo---->https://drive.google.com/open?id=0B9mHtmglofDCU214NVpjbkRlVk0(字串長度:61)---->是(字串長度:3)```talk_title---->從資料發掘理論？用資料確認理論？淺談如何兼容資料科學與開放科學(字串長度:93)----> 從資料發掘新奇實用理論的機器學習，是近幾年台灣社會及學術界許多領域的顯學。近期學術界造假事件層出不窮，我認為這時候值得了解開放科學的宗旨：累積可重製的資料，才能建構經得起考驗的理論。  我以2011年心理學再現危機開始爆發的指標事件，與近兩年引發議論的「權力姿勢」為例，說明以新奇理論包裝的劣質研究能被發表，並獲得關注的關鍵。現在的開放科學採用那些措施扭轉這些問題。這些措施與資料科學的建模有沒有抵觸？或是相輔相成的可能？  最後以curate science project為例，說明如何運用累積數據，引導一般科學研究人員提昇研究操作品質，建構以可再現結果為基礎的實用理論。(字串長度:805)```company---->慈濟大學(字串長度:12)---->開放科學中心大使(字串長度:24)```position---->開放科學中心大使(字串長度:24)---->錄音不錄影(字串長度:15)```desc----> 從資料發掘新奇實用理論的機器學習，是近幾年台灣社會及學術界許多領域的顯學。近期學術界造假事件層出不窮，我認為這時候值得了解開放科學的宗旨：累積可重製的資料，才能建構經得起考驗的理論。<br><br>我以2011年心理學再現危機開始爆發的指標事件，與近兩年引發議論的「權力姿勢」為例，說明以新奇理論包裝的劣質研究能被發表，並獲得關注的關鍵。現在的開放科學採用那些措施扭轉這些問題。這些措施與資料科學的建模有沒有抵觸？或是相輔相成的可能？<br><br>最後以curate science project為例，說明如何運用累積數據，引導一般科學研究人員提昇研究操作品質，建構以可再現結果為基礎的實用理論。(字串長度:817)---->慈濟大人類發展與心理學系專任助理教授 開放科學中心大使(Center for Open Science Ambassador)<br><br>在台灣教了幾年書，2014年受到開放科學的知識論感召，親身到荷蘭體會身體力行之路。希望向華文大眾與學術界傳播分辨好壞科學的心法。(字串長度:307)```conf---->DS(字串長度:2)---->陳紹慶(字串長度:9)```website---->http://scchen.com(字串長度:17)---->http://陳紹慶(字串長度:16)'),(54,'2017-08-28 08:55:24',8,'name---->蘇中才(字串長度:9)---->男(字串長度:3)```name2---->蘇中才(字串長度:9)---->亞大基因(字串長度:12)```front_photo---->https://drive.google.com/open?id=0Bx2Wt2CYnDW2NkdtVFNhTDRETUU(字串長度:61)---->是(字串長度:3)```talk_title---->靠寫程式來翻轉醫療－從基因體大數據出發(字串長度:57)---->第一個人類基因體草稿於西元2000年被公布後，正式進入後基因體時代，但因基因體定序價錢居高不下而無法落實到臨床醫療。近年來，由於次世代定序技術的進步，人類全基因體定序的價錢下降速度比莫爾定律還快，市場化到臨床醫療的時代即將到來。藉由人工智慧與大數據的技術演進，大家可以靠寫程式來讓基因密碼一一解密，將從傳統醫療邁入精準醫療 (Precision Medicine)的世代，但大家是否準備好面對它呢？試問，以後大家去醫院看病，不是帶著健保卡，而是帶著你的基因序列去給醫生看？或者是給 AlphaGo 看呢？在台灣，多元成家議題一直爭議不休，試問，目前性別是依照性染色體來判斷，但這是否正確？針對這些具爭議性的議題用數據來進行探討，除了帶領各位資料科學家們一窺精準醫療的領域，更希望帶給大家以更多元的角度來面對自己生活週遭的每一個數據。(字串長度:1030)```company---->亞大基因(字串長度:12)---->科學長(字串長度:9)```position---->科學長(字串長度:9)---->是(字串長度:3)```desc---->第一個人類基因體草稿於西元2000年被公布後，正式進入後基因體時代，但因基因體定序價錢居高不下而無法落實到臨床醫療。近年來，由於次世代定序技術的進步，人類全基因體定序的價錢下降速度比莫爾定律還快，市場化到臨床醫療的時代即將到來。藉由人工智慧與大數據的技術演進，大家可以靠寫程式來讓基因密碼一一解密，將從傳統醫療邁入精準醫療 (Precision Medicine)的世代，但大家是否準備好面對它呢？試問，以後大家去醫院看病，不是帶著健保卡，而是帶著你的基因序列去給醫生看？或者是給 AlphaGo 看呢？在台灣，多元成家議題一直爭議不休，試問，目前性別是依照性染色體來判斷，但這是否正確？針對這些具爭議性的議題用數據來進行探討，除了帶領各位資料科學家們一窺精準醫療的領域，更希望帶給大家以更多元的角度來面對自己生活週遭的每一個數據。(字串長度:1030)---->2007年畢業於台灣大學資訊工程研究所，博士論文題目為「蛋白質非穩定區段之預測與特性研究」。之後加入趨勢科技從事雲端平台的建置與相關演算法開發，利用資料探勘與機器學習等技術來主動偵測垃圾郵件、色情網站及惡意軟體。將處理人類病毒相關演算法運用到處理電腦病毒上，跨領域活用演算法來有效處理資料。2015年成立亞大基因，建立一個擁有生物資訊和大數據分析技術雙重優勢的軟體研發團隊，專注致力於發展創新的人類基因序列數據挖掘（Data Mining）技術及分析軟體平台（Analytics Platform），進而幫助臨床醫師有效利用基因體資料提供病人精準的醫療。(字串長度:757)```conf---->DS(字串長度:2)---->蘇中才(字串長度:9)```website---->http://www.atgenomix.com(字串長度:24)---->http://蘇中才(字串長度:16)'),(55,'2017-08-28 08:55:24',9,'name---->蔡炎龍(字串長度:9)---->男(字串長度:3)```name2---->蔡炎龍(字串長度:9)---->政治大學應用數學系(字串長度:27)```front_photo---->https://drive.google.com/open?id=0ByqkbzbmgT9kWkJEMTBmWVAyalU(字串長度:61)---->是(字串長度:3)```talk_title---->深度學習實作上的各種卡關(字串長度:36)---->學習過基本深度學習的概念, 也會一些建構神經網路方便的工具如 Keras 之後,  我們總覺得馬上就可以用深度學習的方法做點東西出來, 但往往不如我們預想中的順利。這個演講中我們會先複習基本神經網路的架構, 接著分享在實務應用上經歷過的種種卡關, 以及克服這些障礙的方式。(字串長度:372)```company---->政治大學應用數學系(字串長度:27)---->副教授(字串長度:9)```position---->副教授(字串長度:9)---->是(字串長度:3)```desc---->學習過基本深度學習的概念, 也會一些建構神經網路方便的工具如 Keras 之後,<br><br>我們總覺得馬上就可以用深度學習的方法做點東西出來, 但往往不如我們預想中的順利。這個演講中我們會先複習基本神經網路的架構, 接著分享在實務應用上經歷過的種種卡關, 以及克服這些障礙的方式。(字串長度:378)---->美國加州大學爾灣校區 (UC Irvine) 數學博士, 專攻純數學「代數幾何」領域, 但多年來看上 Python 「簡單、強大、可專注在問題而非程式語言本身」, 所以一直致力推廣 Python 程式語言。碩士班時期就研究神經網路, 直到約一年半前才重回深度學習的領域。已在多個場合介紹過深度學習的基本理論和實作方式, 也試著用深度學習的方法解決不同領域的問題。(字串長度:465)```conf---->DS(字串長度:2)---->蔡炎龍(字串長度:9)```website---->(字串長度:0)---->http://蔡炎龍(字串長度:16)'),(56,'2017-08-28 08:55:24',10,'name---->吳牧恩(字串長度:9)---->男(字串長度:3)```name2---->吳牧恩(字串長度:9)---->台北科技大學(字串長度:18)```front_photo---->https://drive.google.com/open?id=0B0W2fuFPpPC9ZlZJMnhjejJvZDg(字串長度:61)---->是(字串長度:3)```talk_title---->資料科學時代下的智能交易初體驗(字串長度:45)---->隨著雲端技術與電腦運算的成長，金融交易模式也跟著大幅改變。過去人工方式選股，近年來程式交易興起，設計交易演算法由電腦自動下單逐漸蔚為風潮，許多人都想自行撰寫程式建立自己的演算法交易事業。而近年來交易環境的改變，利用傳統價量進行研發已不敷使用，而機器學習似乎為我們帶來一片希望? 究竟機器學習機制該如何用於交易策略研發? 深度學習技術能否帶來交易的AlphaGO?演算法交易該用什麼樣的工具? 本演講將針對這些問題進行探討。(字串長度:605)```company---->台北科技大學(字串長度:18)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->是(字串長度:3)```desc---->隨著雲端技術與電腦運算的成長，金融交易模式也跟著大幅改變。過去人工方式選股，近年來程式交易興起，設計交易演算法由電腦自動下單逐漸蔚為風潮，許多人都想自行撰寫程式建立自己的演算法交易事業。而近年來交易環境的改變，利用傳統價量進行研發已不敷使用，而機器學習似乎為我們帶來一片希望? 究竟機器學習機制該如何用於交易策略研發? 深度學習技術能否帶來交易的AlphaGO?演算法交易該用什麼樣的工具? 本演講將針對這些問題進行探討。(字串長度:605)---->吳牧恩教授為數理背景出身、資工博士畢業，其研究領域包括資訊理論與金融資料分析。現為台北科技大學資訊與財經管理系助理教授，致力於推廣資金管理的數學與使用 R 語言研發金融交易策略，期望破除市場上大眾追求穩賺聖盃的迷思。其亦為知名財金部落格幣圖誌 (Bituzi) 專欄作家 -- 牧清華。(字串長度:395)```conf---->DS(字串長度:2)---->吳牧恩(字串長度:9)```website---->(字串長度:0)---->http://吳牧恩(字串長度:16)'),(57,'2017-08-28 08:55:24',11,'name---->邵軒磊(字串長度:9)---->男(字串長度:3)```name2---->邵軒磊(字串長度:9)---->台師大(字串長度:9)```front_photo---->https://drive.google.com/open?id=0B0d16GGNlXlQWTRlUl9oNEpGMWs(字串長度:61)---->是(字串長度:3)```talk_title---->AI、大數據、法資訊學--法律與判決中的資料科學(字串長度:64)---->傳統上，法律與判決研究中多以價值判斷與理念分析為主，而在大數據與人工智慧（AI）的趨勢之下，逐漸有學者思考結合新技術開展「法資訊學研究」。本文作為階段性的成果，旨在分析「離婚後小孩歸誰？」（法學中稱為酌定親權人）之運作情形，而得到有效的計算結果。闡釋這類之分析結果，有助於當事人能對與自身訴訟優劣勢上有初步認識，也能對法官對自身判決與其他判決之「共通性」上有所預期（避免恐龍法官），有助於弭平「法院判決與社會民意」之巨大差異。隨著科技發達，法資訊學研究能夠做到研究大範圍時間段與多數案例重複出現之現象，而較少涉及非常特殊之個案，而受到影響。期望在法資訊學之開展中，能提供一個論證與分析的有效知識基礎，讓不同意見的研究者，有據以對話討論的平臺和起點；也藉此機會，歡迎資訊學科的同好一起合作。(字串長度:1028)```company---->台師大(字串長度:9)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->否(字串長度:3)```desc---->傳統上，法律與判決研究中多以價值判斷與理念分析為主，而在大數據與人工智慧（AI）的趨勢之下，逐漸有學者思考結合新技術開展「法資訊學研究」。本文作為階段性的成果，旨在分析「離婚後小孩歸誰？」（法學中稱為酌定親權人）之運作情形，而得到有效的計算結果。闡釋這類之分析結果，有助於當事人能對與自身訴訟優劣勢上有初步認識，也能對法官對自身判決與其他判決之「共通性」上有所預期（避免恐龍法官），有助於弭平「法院判決與社會民意」之巨大差異。隨著科技發達，法資訊學研究能夠做到研究大範圍時間段與多數案例重複出現之現象，而較少涉及非常特殊之個案，而受到影響。期望在法資訊學之開展中，能提供一個論證與分析的有效知識基礎，讓不同意見的研究者，有據以對話討論的平臺和起點；也藉此機會，歡迎資訊學科的同好一起合作。(字串長度:1028)---->黃詩淳，台大法學士、日本北海道大學法學碩士博士。主要研究領域為身分法， 近來關心高齡者人權、財產管理與代際財產移轉議題，包括繼承、遺囑、成年監護、信託等。教授課目有身分法、物權、民法概要、日文法學名著選讀等課程。近年開展法資訊學的研究。著有〈遺產繼承之圖像與原理解析〉、〈涉訟榮民遺囑之特徵與法律問題〉等論文，請參見http://homepage.ntu.edu.tw/~schhuang/Welcome.html 邵軒磊，台大法學士、政大法學碩士博士，專長：國際關係政治學。目前對資料蒐集、模型預測、可視化具有興趣。主要使用R(major)、python(minor)，以及SAS、SPSS、matlab等軟體，詳情請參考：http://web.ntnu.edu.tw/~hlshao/。(字串長度:803)```conf---->DS(字串長度:2)---->邵軒磊(字串長度:9)```website---->http://homepage.ntu.edu.tw/~schhuang/Welcome.html , http://web.ntnu.edu.tw/~hlshao/(字串長度:83)---->http://邵軒磊(字串長度:16)'),(58,'2017-08-28 08:55:24',12,'name---->李祈均(字串長度:9)---->男(字串長度:3)```name2---->李祈均 (Jeremy)(字串長度:18)---->清大電機(字串長度:12)```front_photo---->https://drive.google.com/open?id=0B9qZsKw-A44IR1p4d2ZtaDFEWjg(字串長度:61)---->是(字串長度:3)```talk_title---->機器智能與人類行為: 跨領域決策分析於醫療應用(字串長度:65)---->長年來領域專家透過解析感測器訊號、歸納有用資訊、並進行適切處理以完成各種與人息息相關的決策應用(decision analytics)。在(非)結構化資料快速累積、機器學習演算法高速進展，並配合深度跨領域知識整合下，依機器智開發嶄新的訊息決策分析架構正蓬勃發展中 。在此一演講中，我們會以多個醫療應用為主軸，分享我們近期透過機器智能並以人本運算角度為出發而開發出「高可信度(reliability)」、「可規模化(scalable)」、「客觀且一致(objective)」 的人類訊息行為分析決策於醫療應用(AI-in-Health Behavior Analytics)。實例分享包涵：臨床血癌診斷輔助、未來中風風險評估、可計算式自閉症失調行為、及內在情緒感受辨識等。此一技術發展更仰賴深度跨領域合作，以提供專家現實實務上有效且全新變革式的分析工具。(字串長度:946)```company---->清大電機(字串長度:12)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->是(字串長度:3)```desc---->長年來領域專家透過解析感測器訊號、歸納有用資訊、並進行適切處理以完成各種與人息息相關的決策應用(decision analytics)。在(非)結構化資料快速累積、機器學習演算法高速進展，並配合深度跨領域知識整合下，依機器智開發嶄新的訊息決策分析架構正蓬勃發展中 。在此一演講中，我們會以多個醫療應用為主軸，分享我們近期透過機器智能並以人本運算角度為出發而開發出「高可信度(reliability)」、「可規模化(scalable)」、「客觀且一致(objective)」 的人類訊息行為分析決策於醫療應用(AI-in-Health Behavior Analytics)。實例分享包涵：臨床血癌診斷輔助、未來中風風險評估、可計算式自閉症失調行為、及內在情緒感受辨識等。此一技術發展更仰賴深度跨領域合作，以提供專家現實實務上有效且全新變革式的分析工具。(字串長度:946)---->李祈均目前是國立清華大學電機工程學系助理教授，研究領域橫跨訊號處理與機器學習技術，尤其針對人類行為進行分析、預測及統計建模。他長期著重於運用不同種類的資料檔（文字、聲音、影像等）以研發跨領域，如醫療、教育、心理，相關演算法。他過去擔曾任ID Analytics公司旗下id:a lab的資料科學家，開發分析信用申請函進行金融信用產品的開發。他目前與榮總、國衛院、長庚、台大、國教院等機構合作研究，針對人類行為進行量化建模與客觀辨識。(字串長度:608)```conf---->DS(字串長度:2)---->李祈均(字串長度:9)```website---->http://biic.ee.nthu.edu.tw/(字串長度:27)---->http://李祈均 (Jeremy)(字串長度:25)'),(59,'2017-08-28 08:55:24',13,'name---->黃彥棕(字串長度:9)---->男(字串長度:3)```name2---->黃彥棕(字串長度:9)---->中研院統計所(字串長度:18)```front_photo---->https://drive.google.com/open?id=0B9NGw7J6b8gXaU00VzBmcGRoc1U(字串長度:61)---->是(字串長度:3)```talk_title---->Challenges and opportunities of confounding in data sciences: a case study of smoking and lung cancer mortality 干擾機制對資料科學帶來的挑戰與契機：以抽煙及肺癌存活的因果關係為例(字串長度:211)---->Complexity of the data may provide challenges for making valid causal inference about a scientific hypothesis (smoking vs. lung cancer mortality), particularly in the presence of unknown confounding (underlying lung function). Mendelian randomization (MR) addresses the issue of unknown confounding by using genetic information as an instrumental variable (IV) to estimate the causal effect of an exposure of interest on an outcome. Despite the popularity of IV analyses in fully observable outcomes, methodology is limited for time-to-event survival outcomes with censoring, a common data structure in biomedical sciences. We propose an IV analysis method in the survival context, estimating causal effects on a transformed survival time and the survival probabilities using semiparametric transformation models. We construct unbiased estimating equations to circumvent the difficulty in deriving joint likelihood of the exposure and the outcome, due to the unknown covariance by confounding. Asymptotic properties of the proposed estimators are established. We apply our methods to conduct an MR study for lung cancer survival, which suggests a harmful prognostic effect by smoking intensity (p=0.0067) that would have been missed by the conventional methods. (字串長度:1262)```company---->中研院統計所(字串長度:18)---->副研究員(字串長度:12)```position---->副研究員(字串長度:12)---->是(字串長度:3)```desc---->Complexity of the data may provide challenges for making valid causal inference about a scientific hypothesis (smoking vs. lung cancer mortality), particularly in the presence of unknown confounding (underlying lung function). Mendelian randomization (MR) addresses the issue of unknown confounding by using genetic information as an instrumental variable (IV) to estimate the causal effect of an exposure of interest on an outcome. Despite the popularity of IV analyses in fully observable outcomes, methodology is limited for time-to-event survival outcomes with censoring, a common data structure in biomedical sciences. We propose an IV analysis method in the survival context, estimating causal effects on a transformed survival time and the survival probabilities using semiparametric transformation models. We construct unbiased estimating equations to circumvent the difficulty in deriving joint likelihood of the exposure and the outcome, due to the unknown covariance by confounding. Asymptotic properties of the proposed estimators are established. We apply our methods to conduct an MR study for lung cancer survival, which suggests a harmful prognostic effect by smoking intensity (p=0.0067) that would have been missed by the conventional methods. (字串長度:1262)---->黃博士於2003自台大醫學系畢業，退伍後前往美國哈佛大學先後取得公共衛生碩士（2006, 主修計量方法）、生物統計碩士（2009）、流行病學及生物統計理學博士雙學位（2012）。研究所畢業後於美國布朗大學流行病學系及生物統計系擔任助理教授（2012-2016），於2016年9月返台於中研院統計所任職。黃博士之研究曾獲得國際生統學會東北美地區Research Advisory Board Poster Award（2012）、美國統計學會David P Byar Young Investigator Travel Award（2012）、布朗大學Richard Salomon Faculty Research Award（2013）及布朗大學Junior Research Award in Genetic Studies（2015）。黃博士研究領域包括：生物統計、因果推論、存活分析、流行病學及基因體學。(字串長度:806)```conf---->DS(字串長度:2)---->黃彥棕(字串長度:9)```website---->http://www.stat.sinica.edu.tw/personinfo/?sysid=5017880(字串長度:55)---->http://黃彥棕(字串長度:16)'),(60,'2017-08-28 08:55:24',14,'name---->李家岩(字串長度:9)---->男(字串長度:3)```name2---->李家岩(字串長度:9)---->教育(字串長度:6)```front_photo---->https://drive.google.com/open?id=0BwMeWHVjMsRLZGdZQXRnOEQ0VUE(字串長度:61)---->是(字串長度:3)```talk_title---->製造資料？科學？！(字串長度:27)---->資料科學 (Data Science) 與工業 4.0(Industry 4.0) 是近幾年來廣為討論的主題。在製造業的轉型過程中，如何以資料科學的角度，整合自動化實務和管理經驗，導入方法論以達到智慧製造 (Manufacturing Intelligence) 的理想。本演講藉由實際製造業個案瞭解製造現場解決問題的流程與方法，以實證「現地現物」角度來驗證模型的績效。從瞭解製造現場的特性開始著手，解說智慧型製造系統的概念與運作原理。並根據製造現場的特性，與資料所呈現出的特殊樣型作一比對，例如：平行機台、抽樣檢測、維修保養、換線換模前置作業等，來釐清製造現場與資料之間的迷思，與各位學員一同分享與探討。(字串長度:779)```company---->教育(字串長度:6)---->工作者(字串長度:9)```position---->工作者(字串長度:9)---->否(字串長度:3)```desc---->資料科學 (Data Science) 與工業 4.0(Industry 4.0) 是近幾年來廣為討論的主題。在製造業的轉型過程中，如何以資料科學的角度，整合自動化實務和管理經驗，導入方法論以達到智慧製造 (Manufacturing Intelligence) 的理想。本演講藉由實際製造業個案瞭解製造現場解決問題的流程與方法，以實證「現地現物」角度來驗證模型的績效。從瞭解製造現場的特性開始著手，解說智慧型製造系統的概念與運作原理。並根據製造現場的特性，與資料所呈現出的特殊樣型作一比對，例如：平行機台、抽樣檢測、維修保養、換線換模前置作業等，來釐清製造現場與資料之間的迷思，與各位學員一同分享與探討。(字串長度:779)---->喜歡被稱為「教育工作者」，希望能為教育坦然地做一點事。喜歡以參謀的角度思考與思辨，探究問題的本質。除了喜歡到處遊山玩水趴趴走，也喜歡進製造現場「觀察」，相信「魔鬼都藏在製造現場的細節裡」。他的研究興趣是作業研究、製造資料科學、生產經濟學、多目標決策分析。熱衷於以統計方法與最佳化技術改善製造現場的生產效率與品質，相關應用包含產能規劃、生產排程、錯誤診斷、預測保養、設備健康指標、虛擬物料品質檢測等。(字串長度:597)```conf---->DS(字串長度:2)---->李家岩(字串長度:9)```website---->http://polab.imis.ncku.edu.tw/(字串長度:30)---->http://李家岩(字串長度:16)'),(61,'2017-08-28 08:55:24',15,'name---->邱泊寰(字串長度:9)---->男(字串長度:3)```name2---->邱泊寰(字串長度:9)---->卡米爾股份有限公司(字串長度:27)```front_photo---->https://drive.google.com/open?id=0ByCp5JP3Rf13MXRnaC0xNUxIRk0(字串長度:61)---->否(字串長度:3)```talk_title---->政府數據分析與Google Sprint體驗設計(字串長度:46)---->數據分析最困難的任務之一是找對題目，好題目的關鍵在相同預算創造最大效益ROI。政府單位其實臥虎藏龍，但礙於種種制度，有時難以伸展。透過Google Sprint體驗設計流程，漫天飛舞的便利貼融合共享大腦記憶體，鎖定數據洞見，創造幸福價值。(字串長度:325)```company---->卡米爾股份有限公司(字串長度:27)---->創辦人(字串長度:9)```position---->創辦人(字串長度:9)---->是(字串長度:3)```desc---->數據分析最困難的任務之一是找對題目，好題目的關鍵在相同預算創造最大效益ROI。政府單位其實臥虎藏龍，但礙於種種制度，有時難以伸展。透過Google Sprint體驗設計流程，漫天飛舞的便利貼融合共享大腦記憶體，鎖定數據洞見，創造幸福價值。(字串長度:325)---->喜歡體驗設計、愛好說故事、一星期看七本書(字串長度:60)```conf---->DS(字串長度:2)---->邱泊寰(字串長度:9)```website---->(字串長度:0)---->http://邱泊寰(字串長度:16)'),(62,'2017-08-28 08:55:24',16,'name---->Meeyoung Cha(字串長度:12)---->女(字串長度:3)```name2---->Meeyoung Cha(字串長度:12)---->KAIST / School of Computing (字串長度:28)```front_photo---->https://drive.google.com/open?id=0B7s_TAiINyuFZWlfV1ZsekVUbHc(字串長度:61)---->否(字串長度:3)```talk_title---->Detecting rumors and fake news online(字串長度:37)---->Social platforms are an ideal place for spreading rumors and fake news. As more people seek information and read news online, automatically debunking such false claims has become an urgent problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand their propagation processes. In detecting rumors and fake news in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early detection. I will also highlight the latest detection studies with deep learning techniques.   Short Bio Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:1554)```company---->KAIST / School of Computing (字串長度:28)---->associate professor(字串長度:19)```position---->associate professor(字串長度:19)---->是(字串長度:3)```desc---->Social platforms are an ideal place for spreading rumors and fake news. As more people seek information and read news online, automatically debunking such false claims has become an urgent problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand their propagation processes. In detecting rumors and fake news in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early detection. I will also highlight the latest detection studies with deep learning techniques.<br><br> Short Bio Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:1560)---->Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:790)```conf---->DSK(字串長度:3)---->MEEYOUNG CHA(字串長度:12)```website---->http://ds.kaist.ac.kr/(字串長度:22)---->http://Meeyoung Cha(字串長度:19)'),(63,'2017-08-28 08:55:24',17,'name---->陳榮錡(字串長度:9)---->男(字串長度:3)```name2---->RC(字串長度:2)---->國泰金控(字串長度:12)```front_photo---->https://drive.google.com/open?id=0B9ZFnovPXi90RXdXeWs0R0tpaFU(字串長度:61)---->否(字串長度:3)```talk_title---->資料科學在國泰(字串長度:21)---->資料處理微服務架構設計與實現(字串長度:42)```company---->國泰金控(字串長度:12)---->資料科學家(字串長度:15)```position---->資料科學家(字串長度:15)---->是(字串長度:3)```desc---->資料處理微服務架構設計與實現(字串長度:42)---->曾任YAHOO,Trendmicro,Amazon等軟體公司的工程師，後來不務正業開始了資料科學研究之旅，直到現在。而現今，工作卻像是資料科學工作者的打雜者。(字串長度:191)```conf---->DS(字串長度:2)---->陳榮錡(字串長度:9)'),(64,'2017-08-28 08:55:24',18,'name---->鄭旭詠(字串長度:9)---->女(字串長度:3)```name2---->鄭旭詠(字串長度:9)---->中央大學資訊工程系(字串長度:27)```front_photo---->https://drive.google.com/open?id=0B38yagaBviZYZXBwYzUxbHd1aTA(字串長度:61)---->否(字串長度:3)```talk_title---->智慧型監控系統之廣泛應用(字串長度:36)---->智慧型監控的應用非常廣泛，本次演講將介紹兩種截然不同的應用，首先為應用於智慧型運輸系統之交通參數擷取，第二個是應用於綠色能源之太陽輻射量預測。從這兩個不同類型的應用中，可觀察出智慧型監控需要合適的取相裝置以和不同的應用特性配合，而不同的應用在取相與分析的過程中也會面臨不同的挑戰。從之亦可歸納出智慧型監控系統模組中的共同要件，並希望能夠啟發研究者將智慧型監控技術應用在其他跨領域之研究上。(字串長度:582)```company---->中央大學資訊工程系(字串長度:27)---->教授(字串長度:6)```position---->教授(字串長度:6)---->否(字串長度:3)```desc---->智慧型監控的應用非常廣泛，本次演講將介紹兩種截然不同的應用，首先為應用於智慧型運輸系統之交通參數擷取，第二個是應用於綠色能源之太陽輻射量預測。從這兩個不同類型的應用中，可觀察出智慧型監控需要合適的取相裝置以和不同的應用特性配合，而不同的應用在取相與分析的過程中也會面臨不同的挑戰。從之亦可歸納出智慧型監控系統模組中的共同要件，並希望能夠啟發研究者將智慧型監控技術應用在其他跨領域之研究上。(字串長度:582)---->鄭旭詠博士為美國華盛頓大學電機博士及國立中央大學資訊工程博士。於2008年開始擔任國立中央大學資訊工程系助理教授，並於2013年升等為副教授，2017年升等為教授。研究領域為電腦視覺、視訊分析、影像處理以及智慧型系統等。學術成果豐碩。主持或參與多項相關研究計畫，對學術研究以及產業界皆有具體之貢獻。曾獲多項論文相關獎項。並於2013年獲得中央大學資訊電機學院101學年度新進研究優良教師，2015, 2016, 2017中央大學研究傑出獎，105年度中華民國系統學會學術傑出青年獎，105年度中國電機工程學會優秀青年電機工程師獎，105年度青年獎章等獎項。 (字串長度:747)```conf---->DS(字串長度:2)---->鄭旭詠(字串長度:9)```website---->http://fox1.csie.ncu.edu.tw/~chengsy/index.shtml(字串長度:48)---->http://鄭旭詠(字串長度:16)'),(65,'2017-08-28 08:55:25',19,'name---->李宏毅(字串長度:9)---->男(字串長度:3)```name2---->李宏毅(字串長度:9)---->台大電機(字串長度:12)```front_photo---->https://drive.google.com/open?id=0Bzs7OI1N_nsYRDBsY1dqZE94c2s(字串長度:61)---->否(字串長度:3)```talk_title---->GAN (不是髒話)(字串長度:18)---->為什麼最近在做深度學習的人都一直在 GAN 來 GAN 去的呢？那是因為有一個新的技術叫生成式對抗網路 (Generative Adversarial Network, GAN)，這個技術顯然是深度學習的下一個熱點。在機器學習領域，回歸 (regression) 和分類 (classification) 這兩項任務的解法人們已經不再陌生，但是如何讓機器更進一步創造出有結構的複雜物件 (例如：圖片、文句) 仍是一大挑戰。用生成式對抗網路，機器不只可以畫出以假亂真的人臉，也可以根據一段敘述文字，自己畫出對應的圖案。本課程希望能帶大家認識生成式對抗網路這個深度學習最前沿的技術。(字串長度:699)```company---->台大電機(字串長度:12)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->是(字串長度:3)```desc---->為什麼最近在做深度學習的人都一直在 GAN 來 GAN 去的呢？那是因為有一個新的技術叫生成式對抗網路 (Generative Adversarial Network, GAN)，這個技術顯然是深度學習的下一個熱點。在機器學習領域，回歸 (regression) 和分類 (classification) 這兩項任務的解法人們已經不再陌生，但是如何讓機器更進一步創造出有結構的複雜物件 (例如：圖片、文句) 仍是一大挑戰。用生成式對抗網路，機器不只可以畫出以假亂真的人臉，也可以根據一段敘述文字，自己畫出對應的圖案。本課程希望能帶大家認識生成式對抗網路這個深度學習最前沿的技術。(字串長度:699)---->現為臺大電機系助理教授，他的研究主軸是以機器學習技術讓機器辨識並理解語音訊號的內容。以深度學習技術為基石，他正致力於語音數位內容搜尋、語音數位內容之自動化組織以及從語音數位內容擷取關鍵資訊等前瞻性研究，這些技術有很多的應用，例如：人機互動、問答系統、智慧型線上教學平台等等。他曾在資料科學愛好者協會講授《一日搞懂深度學習》四次，他在臺大開設的機器學習相關課程錄影請見：https://www.youtube.com/channel/UC2ggjtuuWvxrHHHiaDH1dlQ/playlists(字串長度:618)```conf---->DS(字串長度:2)---->李宏毅(字串長度:9)```website---->http://speech.ee.ntu.edu.tw/~tlkagk/(字串長度:36)---->http://李宏毅(字串長度:16)'),(66,'2017-08-28 08:55:25',20,'name---->彭其捷(字串長度:9)---->男(字串長度:3)```name2---->彭其捷(字串長度:9)---->卡米爾股份有限公司(字串長度:27)```front_photo---->https://drive.google.com/open?id=0B04RI8T3EjsKSGp6THdYWDNOYzg(字串長度:61)---->是(字串長度:3)```talk_title---->資料視覺化與使用者經驗設計(字串長度:39)---->許多人都了解資料視覺化的優點，例如可將資料抽象化、讓資訊降維等等，讓數據以更直覺的方式呈現在使用者面前，提升閱讀者的吸收意願。然而，我們依然可以舉出許多例子，即使是視覺化後的數據，依然不容易讓人讀懂其價值，也難以勾起人們理解的意願。  『使用者經驗（UX）』設計的最終目標是『Don’t Make Me Think』，也就是打造一段順暢的閱讀過程，讓使用者能夠更輕鬆理解背後的寶貴價值，而這之中除了透過視覺設計、互動設計將結果優化之外，還包括了使用者研究、需求釐清、文案優化等等技巧，如果能夠將使用者經驗設計相關方法結合資料視覺化技巧，能夠再一次降低數據解讀的門檻，讓更多人了解其價值意涵，也讓更多人被數據所感動。(字串長度:877)```company---->卡米爾股份有限公司(字串長度:27)---->數據專案經理(字串長度:18)```position---->數據專案經理(字串長度:18)---->是(字串長度:3)```desc---->許多人都了解資料視覺化的優點，例如可將資料抽象化、讓資訊降維等等，讓數據以更直覺的方式呈現在使用者面前，提升閱讀者的吸收意願。然而，我們依然可以舉出許多例子，即使是視覺化後的數據，依然不容易讓人讀懂其價值，也難以勾起人們理解的意願。<br><br>『使用者經驗（UX）』設計的最終目標是『Don’t Make Me Think』，也就是打造一段順暢的閱讀過程，讓使用者能夠更輕鬆理解背後的寶貴價值，而這之中除了透過視覺設計、互動設計將結果優化之外，還包括了使用者研究、需求釐清、文案優化等等技巧，如果能夠將使用者經驗設計相關方法結合資料視覺化技巧，能夠再一次降低數據解讀的門檻，讓更多人了解其價值意涵，也讓更多人被數據所感動。(字串長度:883)---->彭其捷畢業於交大數據分析實驗室，對於網路服務設計有巨大熱情，曾負責過：資料科學、資料視覺化、前端工程、服務設計等等類型專案，出版過 3 本使用者經驗專書，致力於打造良善的網路服務體驗，近期多投入資料科學相關主題，期待能夠創造更多資料之間的連結，讓數據分析更平民化。(字串長度:390)```conf---->DS(字串長度:2)---->彭其捷(字串長度:9)```website---->https://about.me/divaka(字串長度:23)---->http://彭其捷(字串長度:16)'),(67,'2017-08-28 08:55:25',21,'name---->林鉦育(字串長度:9)---->男(字串長度:3)```name2---->林鉦育(aha)(字串長度:14)---->玉山銀行(字串長度:12)```front_photo---->https://drive.google.com/open?id=0BxEplIEPKoHfdVEzRThnWVAtOU0(字串長度:61)---->是(字串長度:3)```talk_title---->Data and AI @ E.SUN bank(字串長度:24)---->根據玉山資料處理流程與業務應用經驗，分享實務故事與未來玉山AI發展應用的可能方向。(字串長度:119)```company---->玉山銀行(字串長度:12)---->襄理(字串長度:6)```position---->襄理(字串長度:6)---->等內容確定後才能決定(字串長度:30)```desc---->根據玉山資料處理流程與業務應用經驗，分享實務故事與未來玉山AI發展應用的可能方向。(字串長度:119)---->aha於2007年畢業於台大物理所，專長：奈米光學模擬，2009年進入中研院資訊所，主要研究：participantory sensing(參與式感測)，研究如何透過個人裝置感測搜集資料，並整合拼湊完整巨觀環境變化，2013年入行，初任職為商業分析小組，著重於透過資料探索找出商機與企劃方向．2015年成立並加入創新研發小組，主要負責各式新技術評估與導入，同時規劃與評估相關商業模式的落地與執行，參與數位旗艦分行與玉山小i 隨身金融顧問等專案。 aha 專長於組裝各式語言與各大平台服務，打造最小可行產品原型。同時關心社群與社會，如：Govcash 政治獻金研究是透過g0v的政治獻金數位化的成果；2014年為台灣資料科學愛好者年會R 課程講師．現正沈浸於台灣金融法規、具有台灣特色Fintech、AI、blockchain應用中。<br><br>(字串長度:934)```conf---->DS(字串長度:2)---->林鉦育(字串長度:9)```website---->https://funny.aha.taipei(字串長度:24)---->http://林鉦育(aha)(字串長度:21)'),(68,'2017-08-28 08:55:25',22,'name---->黃士傑(字串長度:9)---->男(字串長度:3)```name2---->黃士傑 Aja Huang(字串長度:19)---->Google DeepMind(字串長度:15)```front_photo---->(字串長度:0)---->否(字串長度:3)```talk_title---->AlphaGo - 深度學習與強化學習的勝利(字串長度:46)---->本次演講我將談到AlphaGo發展歷程中的幾個關鍵(特別是深度學習與強化學習所帶進的重大突破)，2017年初Master網路60局的小故事，以及在中國烏鎮AlphaGo與柯潔九段的人機大戰。(字串長度:229)```company---->Google DeepMind(字串長度:15)---->Research Scientist(字串長度:18)```position---->Research Scientist(字串長度:18)---->否(字串長度:3)```desc---->本次演講我將談到AlphaGo發展歷程中的幾個關鍵(特別是深度學習與強化學習所帶進的重大突破)，2017年初Master網路60局的小故事，以及在中國烏鎮AlphaGo與柯潔九段的人機大戰。(字串長度:229)---->AlphaGo首席工程師。畢業於國立台灣師範大學資訊工程研究所，現在是Google DeepMind的資深研究員。(字串長度:127)```conf---->DS(字串長度:2)---->黃士傑(字串長度:9)```website---->(字串長度:0)---->http://黃士傑 Aja Huang(字串長度:26)'),(69,'2017-08-28 08:57:17',1,'name---->馬匡六(字串長度:9)---->男(字串長度:3)```name2---->Kwan-Liu Ma(字串長度:11)---->University of California at Davis(字串長度:33)```front_photo---->https://drive.google.com/open?id=0B8aPckZapD1sR0JQV2NVTHpocDA(字串長度:61)---->是(字串長度:3)```talk_title---->Big Data Visualization(字串長度:22)---->Advanced computing and imaging technologies enable scientists to study natural phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Internet and mobile device users is expected to be even greater.  To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis methods. Visualization transforms large quantities of, often multiple-dimensional, data into graphical representations that exploit the high-bandwidth channel of the human visual system, leveraging the brain\'s remarkable ability to detect patterns and draw inferences.  It has been shown very effective in understanding large, complex data, and thus become an indispensable tool for many areas of research and practice. I will present several use cases of visualization based on new concepts and techniques that my group at UC Davis has introduced to further advance the visualization technology as a powerful discovery and communication tool.(字串長度:1113)```company---->University of California at Davis(字串長度:33)---->Professor(字串長度:9)```position---->Professor(字串長度:9)---->是(字串長度:3)```desc---->Advanced computing and imaging technologies enable scientists to study natural phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Internet and mobile device users is expected to be even greater.<br><br>To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis methods. Visualization transforms large quantities of, often multiple-dimensional, data into graphical representations that exploit the high-bandwidth channel of the human visual system, leveraging the brain\'s remarkable ability to detect patterns and draw inferences.<br><br>It has been shown very effective in understanding large, complex data, and thus become an indispensable tool for many areas of research and practice. I will present several use cases of visualization based on new concepts and techniques that my group at UC Davis has introduced to further advance the visualization technology as a powerful discovery and communication tool.(字串長度:1125)---->Kwan-Liu Ma (馬匡六) is a professor of computer science and the chair of the Graduate Group in Computer Science (GGCS) at the University of California-Davis, where he directs VIDI Labs and UC Davis Center of Excellence for Visualization. He is also a visiting professor of the National Taiwan University and a chair professor of the National Chiao Tung University. His research spans the fields of visualization, computer graphics, high-performance computing, and user interface design. Professor Ma received his PhD in computer science from the University of Utah in 1993. During 1993-1999, he was with ICASE/NASA Langley Research Center as a research scientist. He joined UC Davis in 1999.<br><br>Professor Ma is presently leading a team of 25 researchers pursuing projects driven by a wide range of real-world applications.<br><br>He received the NSF Presidential Early-Career Research Award (PECASE) in 2000, was elected an IEEE Fellow in 2012, and received the 2013 IEEE VGTC Visualization Technical Achievement Award for his outstanding research work. Professor Ma has been actively serving the research community by playing leading roles in many<br><br>professional activities in the area of visualization. He presently serves as the Associate Editor-in-Chief of the<br><br>IEEE Computer Graphics and Applications (CG&amp;A), and on the editorial board of the Journal of Visualization, the Journal of Visual Informatics, and the Journal of Computational Visual Media.(字串長度:1477)```conf---->DSK(字串長度:3)---->馬匡六(字串長度:9)```website---->http://ma@cs.ucdavis.edu(字串長度:24)---->http://Kwan-Liu Ma(字串長度:18)'),(70,'2017-08-28 08:57:17',4,'name---->孫民(字串長度:6)---->男(字串長度:3)```name2---->孫民(字串長度:6)---->國立清華大學(字串長度:18)```front_photo---->https://drive.google.com/open?id=0ByJgUdTb1N2CeE1TYmhhdTFOVzg(字串長度:61)---->是(字串長度:3)```talk_title---->Training a Deep Agent to See and Interact(字串長度:41)---->An intelligent agent should have the abilities to see and interact with the world in many different ways. In this talk, I summarize our recent work on seeing and interacting using language (e.g., video captioning), interacting by taking actions in specific applications (e.g., viewing angle selection in 360 videos), and interacting by attacking other agents. Ultimately, we long for an embodied intelligent agent to assist us in our daily life. Hence, we also propose a system to anticipate human intention in order to proactively provide assistance.(字串長度:551)```company---->國立清華大學(字串長度:18)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->是(字串長度:3)```desc---->An intelligent agent should have the abilities to see and interact with the world in many different ways. In this talk, I summarize our recent work on seeing and interacting using language (e.g., video captioning), interacting by taking actions in specific applications (e.g., viewing angle selection in 360 videos), and interacting by attacking other agents. Ultimately, we long for an embodied intelligent agent to assist us in our daily life. Hence, we also propose a system to anticipate human intention in order to proactively provide assistance.(字串長度:551)---->Min Sun is an assistant professor at National Tsing Hua University in Taiwan. Before that, he was a postdoctoral researcher at Washington University in Seattle and he graduated from the University of Michigan with a Ph.D. degree in EE: System. He also won the best paper award of 3dRR in 2007, paper award of CVGIP in 2015, 2016, and 2017, and the winner of Thor challenge in CVPR 2017.(字串長度:386)```conf---->DS(字串長度:2)---->孫民(字串長度:6)```website---->http://aliensunmin.github.io(字串長度:28)---->http://孫民(字串長度:13)'),(71,'2017-08-28 08:57:17',7,'name---->陳紹慶(字串長度:9)---->男(字串長度:3)```name2---->陳紹慶(字串長度:9)---->慈濟大學(字串長度:12)```front_photo---->https://drive.google.com/open?id=0B9mHtmglofDCU214NVpjbkRlVk0(字串長度:61)---->是(字串長度:3)```talk_title---->從資料發掘理論？用資料確認理論？淺談如何兼容資料科學與開放科學(字串長度:93)----> 從資料發掘新奇實用理論的機器學習，是近幾年台灣社會及學術界許多領域的顯學。近期學術界造假事件層出不窮，我認為這時候值得了解開放科學的宗旨：累積可重製的資料，才能建構經得起考驗的理論。  我以2011年心理學再現危機開始爆發的指標事件，與近兩年引發議論的「權力姿勢」為例，說明以新奇理論包裝的劣質研究能被發表，並獲得關注的關鍵。現在的開放科學採用那些措施扭轉這些問題。這些措施與資料科學的建模有沒有抵觸？或是相輔相成的可能？  最後以curate science project為例，說明如何運用累積數據，引導一般科學研究人員提昇研究操作品質，建構以可再現結果為基礎的實用理論。(字串長度:805)```company---->慈濟大學(字串長度:12)---->開放科學中心大使(字串長度:24)```position---->開放科學中心大使(字串長度:24)---->錄音不錄影(字串長度:15)```desc----> 從資料發掘新奇實用理論的機器學習，是近幾年台灣社會及學術界許多領域的顯學。近期學術界造假事件層出不窮，我認為這時候值得了解開放科學的宗旨：累積可重製的資料，才能建構經得起考驗的理論。<br><br>我以2011年心理學再現危機開始爆發的指標事件，與近兩年引發議論的「權力姿勢」為例，說明以新奇理論包裝的劣質研究能被發表，並獲得關注的關鍵。現在的開放科學採用那些措施扭轉這些問題。這些措施與資料科學的建模有沒有抵觸？或是相輔相成的可能？<br><br>最後以curate science project為例，說明如何運用累積數據，引導一般科學研究人員提昇研究操作品質，建構以可再現結果為基礎的實用理論。(字串長度:817)---->慈濟大人類發展與心理學系專任助理教授 開放科學中心大使(Center for Open Science Ambassador)<br><br>在台灣教了幾年書，2014年受到開放科學的知識論感召，親身到荷蘭體會身體力行之路。希望向華文大眾與學術界傳播分辨好壞科學的心法。(字串長度:307)```conf---->DS(字串長度:2)---->陳紹慶(字串長度:9)```website---->http://scchen.com(字串長度:17)---->http://陳紹慶(字串長度:16)'),(72,'2017-08-28 08:57:17',8,'name---->蘇中才(字串長度:9)---->男(字串長度:3)```name2---->蘇中才(字串長度:9)---->亞大基因(字串長度:12)```front_photo---->https://drive.google.com/open?id=0Bx2Wt2CYnDW2NkdtVFNhTDRETUU(字串長度:61)---->是(字串長度:3)```talk_title---->靠寫程式來翻轉醫療－從基因體大數據出發(字串長度:57)---->第一個人類基因體草稿於西元2000年被公布後，正式進入後基因體時代，但因基因體定序價錢居高不下而無法落實到臨床醫療。近年來，由於次世代定序技術的進步，人類全基因體定序的價錢下降速度比莫爾定律還快，市場化到臨床醫療的時代即將到來。藉由人工智慧與大數據的技術演進，大家可以靠寫程式來讓基因密碼一一解密，將從傳統醫療邁入精準醫療 (Precision Medicine)的世代，但大家是否準備好面對它呢？試問，以後大家去醫院看病，不是帶著健保卡，而是帶著你的基因序列去給醫生看？或者是給 AlphaGo 看呢？在台灣，多元成家議題一直爭議不休，試問，目前性別是依照性染色體來判斷，但這是否正確？針對這些具爭議性的議題用數據來進行探討，除了帶領各位資料科學家們一窺精準醫療的領域，更希望帶給大家以更多元的角度來面對自己生活週遭的每一個數據。(字串長度:1030)```company---->亞大基因(字串長度:12)---->科學長(字串長度:9)```position---->科學長(字串長度:9)---->是(字串長度:3)```desc---->第一個人類基因體草稿於西元2000年被公布後，正式進入後基因體時代，但因基因體定序價錢居高不下而無法落實到臨床醫療。近年來，由於次世代定序技術的進步，人類全基因體定序的價錢下降速度比莫爾定律還快，市場化到臨床醫療的時代即將到來。藉由人工智慧與大數據的技術演進，大家可以靠寫程式來讓基因密碼一一解密，將從傳統醫療邁入精準醫療 (Precision Medicine)的世代，但大家是否準備好面對它呢？試問，以後大家去醫院看病，不是帶著健保卡，而是帶著你的基因序列去給醫生看？或者是給 AlphaGo 看呢？在台灣，多元成家議題一直爭議不休，試問，目前性別是依照性染色體來判斷，但這是否正確？針對這些具爭議性的議題用數據來進行探討，除了帶領各位資料科學家們一窺精準醫療的領域，更希望帶給大家以更多元的角度來面對自己生活週遭的每一個數據。(字串長度:1030)---->2007年畢業於台灣大學資訊工程研究所，博士論文題目為「蛋白質非穩定區段之預測與特性研究」。之後加入趨勢科技從事雲端平台的建置與相關演算法開發，利用資料探勘與機器學習等技術來主動偵測垃圾郵件、色情網站及惡意軟體。將處理人類病毒相關演算法運用到處理電腦病毒上，跨領域活用演算法來有效處理資料。2015年成立亞大基因，建立一個擁有生物資訊和大數據分析技術雙重優勢的軟體研發團隊，專注致力於發展創新的人類基因序列數據挖掘（Data Mining）技術及分析軟體平台（Analytics Platform），進而幫助臨床醫師有效利用基因體資料提供病人精準的醫療。(字串長度:757)```conf---->DS(字串長度:2)---->蘇中才(字串長度:9)```website---->http://www.atgenomix.com(字串長度:24)---->http://蘇中才(字串長度:16)'),(73,'2017-08-28 08:57:17',10,'name---->吳牧恩(字串長度:9)---->男(字串長度:3)```name2---->吳牧恩(字串長度:9)---->台北科技大學(字串長度:18)```front_photo---->https://drive.google.com/open?id=0B0W2fuFPpPC9ZlZJMnhjejJvZDg(字串長度:61)---->是(字串長度:3)```talk_title---->資料科學時代下的智能交易初體驗(字串長度:45)---->隨著雲端技術與電腦運算的成長，金融交易模式也跟著大幅改變。過去人工方式選股，近年來程式交易興起，設計交易演算法由電腦自動下單逐漸蔚為風潮，許多人都想自行撰寫程式建立自己的演算法交易事業。而近年來交易環境的改變，利用傳統價量進行研發已不敷使用，而機器學習似乎為我們帶來一片希望? 究竟機器學習機制該如何用於交易策略研發? 深度學習技術能否帶來交易的AlphaGO?演算法交易該用什麼樣的工具? 本演講將針對這些問題進行探討。(字串長度:605)```company---->台北科技大學(字串長度:18)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->是(字串長度:3)```desc---->隨著雲端技術與電腦運算的成長，金融交易模式也跟著大幅改變。過去人工方式選股，近年來程式交易興起，設計交易演算法由電腦自動下單逐漸蔚為風潮，許多人都想自行撰寫程式建立自己的演算法交易事業。而近年來交易環境的改變，利用傳統價量進行研發已不敷使用，而機器學習似乎為我們帶來一片希望? 究竟機器學習機制該如何用於交易策略研發? 深度學習技術能否帶來交易的AlphaGO?演算法交易該用什麼樣的工具? 本演講將針對這些問題進行探討。(字串長度:605)---->吳牧恩教授為數理背景出身、資工博士畢業，其研究領域包括資訊理論與金融資料分析。現為台北科技大學資訊與財經管理系助理教授，致力於推廣資金管理的數學與使用 R 語言研發金融交易策略，期望破除市場上大眾追求穩賺聖盃的迷思。其亦為知名財金部落格幣圖誌 (Bituzi) 專欄作家 -- 牧清華。(字串長度:395)```conf---->DS(字串長度:2)---->吳牧恩(字串長度:9)```website---->(字串長度:0)---->http://吳牧恩(字串長度:16)'),(74,'2017-08-28 08:57:17',11,'name---->邵軒磊(字串長度:9)---->男(字串長度:3)```name2---->邵軒磊(字串長度:9)---->台師大(字串長度:9)```front_photo---->https://drive.google.com/open?id=0B0d16GGNlXlQWTRlUl9oNEpGMWs(字串長度:61)---->是(字串長度:3)```talk_title---->AI、大數據、法資訊學--法律與判決中的資料科學(字串長度:64)---->傳統上，法律與判決研究中多以價值判斷與理念分析為主，而在大數據與人工智慧（AI）的趨勢之下，逐漸有學者思考結合新技術開展「法資訊學研究」。本文作為階段性的成果，旨在分析「離婚後小孩歸誰？」（法學中稱為酌定親權人）之運作情形，而得到有效的計算結果。闡釋這類之分析結果，有助於當事人能對與自身訴訟優劣勢上有初步認識，也能對法官對自身判決與其他判決之「共通性」上有所預期（避免恐龍法官），有助於弭平「法院判決與社會民意」之巨大差異。隨著科技發達，法資訊學研究能夠做到研究大範圍時間段與多數案例重複出現之現象，而較少涉及非常特殊之個案，而受到影響。期望在法資訊學之開展中，能提供一個論證與分析的有效知識基礎，讓不同意見的研究者，有據以對話討論的平臺和起點；也藉此機會，歡迎資訊學科的同好一起合作。(字串長度:1028)```company---->台師大(字串長度:9)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->否(字串長度:3)```desc---->傳統上，法律與判決研究中多以價值判斷與理念分析為主，而在大數據與人工智慧（AI）的趨勢之下，逐漸有學者思考結合新技術開展「法資訊學研究」。本文作為階段性的成果，旨在分析「離婚後小孩歸誰？」（法學中稱為酌定親權人）之運作情形，而得到有效的計算結果。闡釋這類之分析結果，有助於當事人能對與自身訴訟優劣勢上有初步認識，也能對法官對自身判決與其他判決之「共通性」上有所預期（避免恐龍法官），有助於弭平「法院判決與社會民意」之巨大差異。隨著科技發達，法資訊學研究能夠做到研究大範圍時間段與多數案例重複出現之現象，而較少涉及非常特殊之個案，而受到影響。期望在法資訊學之開展中，能提供一個論證與分析的有效知識基礎，讓不同意見的研究者，有據以對話討論的平臺和起點；也藉此機會，歡迎資訊學科的同好一起合作。(字串長度:1028)---->黃詩淳，台大法學士、日本北海道大學法學碩士博士。主要研究領域為身分法， 近來關心高齡者人權、財產管理與代際財產移轉議題，包括繼承、遺囑、成年監護、信託等。教授課目有身分法、物權、民法概要、日文法學名著選讀等課程。近年開展法資訊學的研究。著有〈遺產繼承之圖像與原理解析〉、〈涉訟榮民遺囑之特徵與法律問題〉等論文，請參見http://homepage.ntu.edu.tw/~schhuang/Welcome.html 邵軒磊，台大法學士、政大法學碩士博士，專長：國際關係政治學。目前對資料蒐集、模型預測、可視化具有興趣。主要使用R(major)、python(minor)，以及SAS、SPSS、matlab等軟體，詳情請參考：http://web.ntnu.edu.tw/~hlshao/。(字串長度:803)```conf---->DS(字串長度:2)---->邵軒磊(字串長度:9)```website---->http://homepage.ntu.edu.tw/~schhuang/Welcome.html , http://web.ntnu.edu.tw/~hlshao/(字串長度:83)---->http://邵軒磊(字串長度:16)'),(75,'2017-08-28 08:57:17',12,'name---->李祈均(字串長度:9)---->男(字串長度:3)```name2---->李祈均 (Jeremy)(字串長度:18)---->清大電機(字串長度:12)```front_photo---->https://drive.google.com/open?id=0B9qZsKw-A44IR1p4d2ZtaDFEWjg(字串長度:61)---->是(字串長度:3)```talk_title---->機器智能與人類行為: 跨領域決策分析於醫療應用(字串長度:65)---->長年來領域專家透過解析感測器訊號、歸納有用資訊、並進行適切處理以完成各種與人息息相關的決策應用(decision analytics)。在(非)結構化資料快速累積、機器學習演算法高速進展，並配合深度跨領域知識整合下，依機器智開發嶄新的訊息決策分析架構正蓬勃發展中 。在此一演講中，我們會以多個醫療應用為主軸，分享我們近期透過機器智能並以人本運算角度為出發而開發出「高可信度(reliability)」、「可規模化(scalable)」、「客觀且一致(objective)」 的人類訊息行為分析決策於醫療應用(AI-in-Health Behavior Analytics)。實例分享包涵：臨床血癌診斷輔助、未來中風風險評估、可計算式自閉症失調行為、及內在情緒感受辨識等。此一技術發展更仰賴深度跨領域合作，以提供專家現實實務上有效且全新變革式的分析工具。(字串長度:946)```company---->清大電機(字串長度:12)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->是(字串長度:3)```desc---->長年來領域專家透過解析感測器訊號、歸納有用資訊、並進行適切處理以完成各種與人息息相關的決策應用(decision analytics)。在(非)結構化資料快速累積、機器學習演算法高速進展，並配合深度跨領域知識整合下，依機器智開發嶄新的訊息決策分析架構正蓬勃發展中 。在此一演講中，我們會以多個醫療應用為主軸，分享我們近期透過機器智能並以人本運算角度為出發而開發出「高可信度(reliability)」、「可規模化(scalable)」、「客觀且一致(objective)」 的人類訊息行為分析決策於醫療應用(AI-in-Health Behavior Analytics)。實例分享包涵：臨床血癌診斷輔助、未來中風風險評估、可計算式自閉症失調行為、及內在情緒感受辨識等。此一技術發展更仰賴深度跨領域合作，以提供專家現實實務上有效且全新變革式的分析工具。(字串長度:946)---->李祈均目前是國立清華大學電機工程學系助理教授，研究領域橫跨訊號處理與機器學習技術，尤其針對人類行為進行分析、預測及統計建模。他長期著重於運用不同種類的資料檔（文字、聲音、影像等）以研發跨領域，如醫療、教育、心理，相關演算法。他過去擔曾任ID Analytics公司旗下id:a lab的資料科學家，開發分析信用申請函進行金融信用產品的開發。他目前與榮總、國衛院、長庚、台大、國教院等機構合作研究，針對人類行為進行量化建模與客觀辨識。(字串長度:608)```conf---->DS(字串長度:2)---->李祈均(字串長度:9)```website---->http://biic.ee.nthu.edu.tw/(字串長度:27)---->http://李祈均 (Jeremy)(字串長度:25)'),(76,'2017-08-28 08:57:18',13,'name---->黃彥棕(字串長度:9)---->男(字串長度:3)```name2---->黃彥棕(字串長度:9)---->中研院統計所(字串長度:18)```front_photo---->https://drive.google.com/open?id=0B9NGw7J6b8gXaU00VzBmcGRoc1U(字串長度:61)---->是(字串長度:3)```talk_title---->Challenges and opportunities of confounding in data sciences: a case study of smoking and lung cancer mortality 干擾機制對資料科學帶來的挑戰與契機：以抽煙及肺癌存活的因果關係為例(字串長度:211)---->Complexity of the data may provide challenges for making valid causal inference about a scientific hypothesis (smoking vs. lung cancer mortality), particularly in the presence of unknown confounding (underlying lung function). Mendelian randomization (MR) addresses the issue of unknown confounding by using genetic information as an instrumental variable (IV) to estimate the causal effect of an exposure of interest on an outcome. Despite the popularity of IV analyses in fully observable outcomes, methodology is limited for time-to-event survival outcomes with censoring, a common data structure in biomedical sciences. We propose an IV analysis method in the survival context, estimating causal effects on a transformed survival time and the survival probabilities using semiparametric transformation models. We construct unbiased estimating equations to circumvent the difficulty in deriving joint likelihood of the exposure and the outcome, due to the unknown covariance by confounding. Asymptotic properties of the proposed estimators are established. We apply our methods to conduct an MR study for lung cancer survival, which suggests a harmful prognostic effect by smoking intensity (p=0.0067) that would have been missed by the conventional methods. (字串長度:1262)```company---->中研院統計所(字串長度:18)---->副研究員(字串長度:12)```position---->副研究員(字串長度:12)---->是(字串長度:3)```desc---->Complexity of the data may provide challenges for making valid causal inference about a scientific hypothesis (smoking vs. lung cancer mortality), particularly in the presence of unknown confounding (underlying lung function). Mendelian randomization (MR) addresses the issue of unknown confounding by using genetic information as an instrumental variable (IV) to estimate the causal effect of an exposure of interest on an outcome. Despite the popularity of IV analyses in fully observable outcomes, methodology is limited for time-to-event survival outcomes with censoring, a common data structure in biomedical sciences. We propose an IV analysis method in the survival context, estimating causal effects on a transformed survival time and the survival probabilities using semiparametric transformation models. We construct unbiased estimating equations to circumvent the difficulty in deriving joint likelihood of the exposure and the outcome, due to the unknown covariance by confounding. Asymptotic properties of the proposed estimators are established. We apply our methods to conduct an MR study for lung cancer survival, which suggests a harmful prognostic effect by smoking intensity (p=0.0067) that would have been missed by the conventional methods. (字串長度:1262)---->黃博士於2003自台大醫學系畢業，退伍後前往美國哈佛大學先後取得公共衛生碩士（2006, 主修計量方法）、生物統計碩士（2009）、流行病學及生物統計理學博士雙學位（2012）。研究所畢業後於美國布朗大學流行病學系及生物統計系擔任助理教授（2012-2016），於2016年9月返台於中研院統計所任職。黃博士之研究曾獲得國際生統學會東北美地區Research Advisory Board Poster Award（2012）、美國統計學會David P Byar Young Investigator Travel Award（2012）、布朗大學Richard Salomon Faculty Research Award（2013）及布朗大學Junior Research Award in Genetic Studies（2015）。黃博士研究領域包括：生物統計、因果推論、存活分析、流行病學及基因體學。(字串長度:806)```conf---->DS(字串長度:2)---->黃彥棕(字串長度:9)```website---->http://www.stat.sinica.edu.tw/personinfo/?sysid=5017880(字串長度:55)---->http://黃彥棕(字串長度:16)'),(77,'2017-08-28 08:57:18',14,'name---->李家岩(字串長度:9)---->男(字串長度:3)```name2---->李家岩(字串長度:9)---->教育(字串長度:6)```front_photo---->https://drive.google.com/open?id=0BwMeWHVjMsRLZGdZQXRnOEQ0VUE(字串長度:61)---->是(字串長度:3)```talk_title---->製造資料？科學？！(字串長度:27)---->資料科學 (Data Science) 與工業 4.0(Industry 4.0) 是近幾年來廣為討論的主題。在製造業的轉型過程中，如何以資料科學的角度，整合自動化實務和管理經驗，導入方法論以達到智慧製造 (Manufacturing Intelligence) 的理想。本演講藉由實際製造業個案瞭解製造現場解決問題的流程與方法，以實證「現地現物」角度來驗證模型的績效。從瞭解製造現場的特性開始著手，解說智慧型製造系統的概念與運作原理。並根據製造現場的特性，與資料所呈現出的特殊樣型作一比對，例如：平行機台、抽樣檢測、維修保養、換線換模前置作業等，來釐清製造現場與資料之間的迷思，與各位學員一同分享與探討。(字串長度:779)```company---->教育(字串長度:6)---->工作者(字串長度:9)```position---->工作者(字串長度:9)---->否(字串長度:3)```desc---->資料科學 (Data Science) 與工業 4.0(Industry 4.0) 是近幾年來廣為討論的主題。在製造業的轉型過程中，如何以資料科學的角度，整合自動化實務和管理經驗，導入方法論以達到智慧製造 (Manufacturing Intelligence) 的理想。本演講藉由實際製造業個案瞭解製造現場解決問題的流程與方法，以實證「現地現物」角度來驗證模型的績效。從瞭解製造現場的特性開始著手，解說智慧型製造系統的概念與運作原理。並根據製造現場的特性，與資料所呈現出的特殊樣型作一比對，例如：平行機台、抽樣檢測、維修保養、換線換模前置作業等，來釐清製造現場與資料之間的迷思，與各位學員一同分享與探討。(字串長度:779)---->喜歡被稱為「教育工作者」，希望能為教育坦然地做一點事。喜歡以參謀的角度思考與思辨，探究問題的本質。除了喜歡到處遊山玩水趴趴走，也喜歡進製造現場「觀察」，相信「魔鬼都藏在製造現場的細節裡」。他的研究興趣是作業研究、製造資料科學、生產經濟學、多目標決策分析。熱衷於以統計方法與最佳化技術改善製造現場的生產效率與品質，相關應用包含產能規劃、生產排程、錯誤診斷、預測保養、設備健康指標、虛擬物料品質檢測等。(字串長度:597)```conf---->DS(字串長度:2)---->李家岩(字串長度:9)```website---->http://polab.imis.ncku.edu.tw/(字串長度:30)---->http://李家岩(字串長度:16)'),(78,'2017-08-28 08:57:18',16,'name---->Meeyoung Cha(字串長度:12)---->女(字串長度:3)```name2---->Meeyoung Cha(字串長度:12)---->KAIST / School of Computing (字串長度:28)```front_photo---->https://drive.google.com/open?id=0B7s_TAiINyuFZWlfV1ZsekVUbHc(字串長度:61)---->否(字串長度:3)```talk_title---->Detecting rumors and fake news online(字串長度:37)---->Social platforms are an ideal place for spreading rumors and fake news. As more people seek information and read news online, automatically debunking such false claims has become an urgent problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand their propagation processes. In detecting rumors and fake news in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early detection. I will also highlight the latest detection studies with deep learning techniques.   Short Bio Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:1554)```company---->KAIST / School of Computing (字串長度:28)---->associate professor(字串長度:19)```position---->associate professor(字串長度:19)---->是(字串長度:3)```desc---->Social platforms are an ideal place for spreading rumors and fake news. As more people seek information and read news online, automatically debunking such false claims has become an urgent problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand their propagation processes. In detecting rumors and fake news in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early detection. I will also highlight the latest detection studies with deep learning techniques.<br><br> Short Bio Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:1560)---->Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:790)```conf---->DSK(字串長度:3)---->MEEYOUNG CHA(字串長度:12)```website---->http://ds.kaist.ac.kr/(字串長度:22)---->http://Meeyoung Cha(字串長度:19)'),(79,'2017-08-28 08:57:18',19,'name---->李宏毅(字串長度:9)---->男(字串長度:3)```name2---->李宏毅(字串長度:9)---->台大電機(字串長度:12)```front_photo---->https://drive.google.com/open?id=0Bzs7OI1N_nsYRDBsY1dqZE94c2s(字串長度:61)---->否(字串長度:3)```talk_title---->GAN (不是髒話)(字串長度:18)---->為什麼最近在做深度學習的人都一直在 GAN 來 GAN 去的呢？那是因為有一個新的技術叫生成式對抗網路 (Generative Adversarial Network, GAN)，這個技術顯然是深度學習的下一個熱點。在機器學習領域，回歸 (regression) 和分類 (classification) 這兩項任務的解法人們已經不再陌生，但是如何讓機器更進一步創造出有結構的複雜物件 (例如：圖片、文句) 仍是一大挑戰。用生成式對抗網路，機器不只可以畫出以假亂真的人臉，也可以根據一段敘述文字，自己畫出對應的圖案。本課程希望能帶大家認識生成式對抗網路這個深度學習最前沿的技術。(字串長度:699)```company---->台大電機(字串長度:12)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->是(字串長度:3)```desc---->為什麼最近在做深度學習的人都一直在 GAN 來 GAN 去的呢？那是因為有一個新的技術叫生成式對抗網路 (Generative Adversarial Network, GAN)，這個技術顯然是深度學習的下一個熱點。在機器學習領域，回歸 (regression) 和分類 (classification) 這兩項任務的解法人們已經不再陌生，但是如何讓機器更進一步創造出有結構的複雜物件 (例如：圖片、文句) 仍是一大挑戰。用生成式對抗網路，機器不只可以畫出以假亂真的人臉，也可以根據一段敘述文字，自己畫出對應的圖案。本課程希望能帶大家認識生成式對抗網路這個深度學習最前沿的技術。(字串長度:699)---->現為臺大電機系助理教授，他的研究主軸是以機器學習技術讓機器辨識並理解語音訊號的內容。以深度學習技術為基石，他正致力於語音數位內容搜尋、語音數位內容之自動化組織以及從語音數位內容擷取關鍵資訊等前瞻性研究，這些技術有很多的應用，例如：人機互動、問答系統、智慧型線上教學平台等等。他曾在資料科學愛好者協會講授《一日搞懂深度學習》四次，他在臺大開設的機器學習相關課程錄影請見：https://www.youtube.com/channel/UC2ggjtuuWvxrHHHiaDH1dlQ/playlists(字串長度:618)```conf---->DS(字串長度:2)---->李宏毅(字串長度:9)```website---->http://speech.ee.ntu.edu.tw/~tlkagk/(字串長度:36)---->http://李宏毅(字串長度:16)'),(80,'2017-08-28 08:57:18',20,'name---->彭其捷(字串長度:9)---->男(字串長度:3)```name2---->彭其捷(字串長度:9)---->卡米爾股份有限公司(字串長度:27)```front_photo---->https://drive.google.com/open?id=0B04RI8T3EjsKSGp6THdYWDNOYzg(字串長度:61)---->是(字串長度:3)```talk_title---->資料視覺化與使用者經驗設計(字串長度:39)---->許多人都了解資料視覺化的優點，例如可將資料抽象化、讓資訊降維等等，讓數據以更直覺的方式呈現在使用者面前，提升閱讀者的吸收意願。然而，我們依然可以舉出許多例子，即使是視覺化後的數據，依然不容易讓人讀懂其價值，也難以勾起人們理解的意願。  『使用者經驗（UX）』設計的最終目標是『Don’t Make Me Think』，也就是打造一段順暢的閱讀過程，讓使用者能夠更輕鬆理解背後的寶貴價值，而這之中除了透過視覺設計、互動設計將結果優化之外，還包括了使用者研究、需求釐清、文案優化等等技巧，如果能夠將使用者經驗設計相關方法結合資料視覺化技巧，能夠再一次降低數據解讀的門檻，讓更多人了解其價值意涵，也讓更多人被數據所感動。(字串長度:877)```company---->卡米爾股份有限公司(字串長度:27)---->數據專案經理(字串長度:18)```position---->數據專案經理(字串長度:18)---->是(字串長度:3)```desc---->許多人都了解資料視覺化的優點，例如可將資料抽象化、讓資訊降維等等，讓數據以更直覺的方式呈現在使用者面前，提升閱讀者的吸收意願。然而，我們依然可以舉出許多例子，即使是視覺化後的數據，依然不容易讓人讀懂其價值，也難以勾起人們理解的意願。<br><br>『使用者經驗（UX）』設計的最終目標是『Don’t Make Me Think』，也就是打造一段順暢的閱讀過程，讓使用者能夠更輕鬆理解背後的寶貴價值，而這之中除了透過視覺設計、互動設計將結果優化之外，還包括了使用者研究、需求釐清、文案優化等等技巧，如果能夠將使用者經驗設計相關方法結合資料視覺化技巧，能夠再一次降低數據解讀的門檻，讓更多人了解其價值意涵，也讓更多人被數據所感動。(字串長度:883)---->彭其捷畢業於交大數據分析實驗室，對於網路服務設計有巨大熱情，曾負責過：資料科學、資料視覺化、前端工程、服務設計等等類型專案，出版過 3 本使用者經驗專書，致力於打造良善的網路服務體驗，近期多投入資料科學相關主題，期待能夠創造更多資料之間的連結，讓數據分析更平民化。(字串長度:390)```conf---->DS(字串長度:2)---->彭其捷(字串長度:9)```website---->https://about.me/divaka(字串長度:23)---->http://彭其捷(字串長度:16)'),(81,'2017-08-28 09:01:44',1,'name---->馬匡六(字串長度:9)---->男(字串長度:3)```name2---->Kwan-Liu Ma(字串長度:11)---->University of California at Davis(字串長度:33)```front_photo---->https://drive.google.com/open?id=0B8aPckZapD1sR0JQV2NVTHpocDA(字串長度:61)---->是(字串長度:3)```talk_title---->Big Data Visualization(字串長度:22)---->Advanced computing and imaging technologies enable scientists to study natural phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Internet and mobile device users is expected to be even greater.  To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis methods. Visualization transforms large quantities of, often multiple-dimensional, data into graphical representations that exploit the high-bandwidth channel of the human visual system, leveraging the brain\'s remarkable ability to detect patterns and draw inferences.  It has been shown very effective in understanding large, complex data, and thus become an indispensable tool for many areas of research and practice. I will present several use cases of visualization based on new concepts and techniques that my group at UC Davis has introduced to further advance the visualization technology as a powerful discovery and communication tool.(字串長度:1113)```company---->University of California at Davis(字串長度:33)---->Professor(字串長度:9)```position---->Professor(字串長度:9)---->是(字串長度:3)```desc---->Advanced computing and imaging technologies enable scientists to study natural phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Internet and mobile device users is expected to be even greater.<br><br>To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis methods. Visualization transforms large quantities of, often multiple-dimensional, data into graphical representations that exploit the high-bandwidth channel of the human visual system, leveraging the brain\'s remarkable ability to detect patterns and draw inferences.<br><br>It has been shown very effective in understanding large, complex data, and thus become an indispensable tool for many areas of research and practice. I will present several use cases of visualization based on new concepts and techniques that my group at UC Davis has introduced to further advance the visualization technology as a powerful discovery and communication tool.(字串長度:1125)---->Kwan-Liu Ma (馬匡六) is a professor of computer science and the chair of the Graduate Group in Computer Science (GGCS) at the University of California-Davis, where he directs VIDI Labs and UC Davis Center of Excellence for Visualization. He is also a visiting professor of the National Taiwan University and a chair professor of the National Chiao Tung University. His research spans the fields of visualization, computer graphics, high-performance computing, and user interface design. Professor Ma received his PhD in computer science from the University of Utah in 1993. During 1993-1999, he was with ICASE/NASA Langley Research Center as a research scientist. He joined UC Davis in 1999.<br><br>Professor Ma is presently leading a team of 25 researchers pursuing projects driven by a wide range of real-world applications.<br><br>He received the NSF Presidential Early-Career Research Award (PECASE) in 2000, was elected an IEEE Fellow in 2012, and received the 2013 IEEE VGTC Visualization Technical Achievement Award for his outstanding research work. Professor Ma has been actively serving the research community by playing leading roles in many<br><br>professional activities in the area of visualization. He presently serves as the Associate Editor-in-Chief of the<br><br>IEEE Computer Graphics and Applications (CG&amp;A), and on the editorial board of the Journal of Visualization, the Journal of Visual Informatics, and the Journal of Computational Visual Media.(字串長度:1477)```conf---->DSK(字串長度:3)---->馬匡六(字串長度:9)```website---->http://ma@cs.ucdavis.edu(字串長度:24)---->http://Kwan-Liu Ma(字串長度:18)'),(82,'2017-08-28 09:01:44',4,'name---->孫民(字串長度:6)---->男(字串長度:3)```name2---->孫民(字串長度:6)---->國立清華大學(字串長度:18)```front_photo---->https://drive.google.com/open?id=0ByJgUdTb1N2CeE1TYmhhdTFOVzg(字串長度:61)---->是(字串長度:3)```talk_title---->Training a Deep Agent to See and Interact(字串長度:41)---->An intelligent agent should have the abilities to see and interact with the world in many different ways. In this talk, I summarize our recent work on seeing and interacting using language (e.g., video captioning), interacting by taking actions in specific applications (e.g., viewing angle selection in 360 videos), and interacting by attacking other agents. Ultimately, we long for an embodied intelligent agent to assist us in our daily life. Hence, we also propose a system to anticipate human intention in order to proactively provide assistance.(字串長度:551)```company---->國立清華大學(字串長度:18)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->是(字串長度:3)```desc---->An intelligent agent should have the abilities to see and interact with the world in many different ways. In this talk, I summarize our recent work on seeing and interacting using language (e.g., video captioning), interacting by taking actions in specific applications (e.g., viewing angle selection in 360 videos), and interacting by attacking other agents. Ultimately, we long for an embodied intelligent agent to assist us in our daily life. Hence, we also propose a system to anticipate human intention in order to proactively provide assistance.(字串長度:551)---->Min Sun is an assistant professor at National Tsing Hua University in Taiwan. Before that, he was a postdoctoral researcher at Washington University in Seattle and he graduated from the University of Michigan with a Ph.D. degree in EE: System. He also won the best paper award of 3dRR in 2007, paper award of CVGIP in 2015, 2016, and 2017, and the winner of Thor challenge in CVPR 2017.(字串長度:386)```conf---->DS(字串長度:2)---->孫民(字串長度:6)```website---->http://aliensunmin.github.io(字串長度:28)---->http://孫民(字串長度:13)'),(83,'2017-08-28 09:01:44',7,'name---->陳紹慶(字串長度:9)---->男(字串長度:3)```name2---->陳紹慶(字串長度:9)---->慈濟大學(字串長度:12)```front_photo---->https://drive.google.com/open?id=0B9mHtmglofDCU214NVpjbkRlVk0(字串長度:61)---->是(字串長度:3)```talk_title---->從資料發掘理論？用資料確認理論？淺談如何兼容資料科學與開放科學(字串長度:93)----> 從資料發掘新奇實用理論的機器學習，是近幾年台灣社會及學術界許多領域的顯學。近期學術界造假事件層出不窮，我認為這時候值得了解開放科學的宗旨：累積可重製的資料，才能建構經得起考驗的理論。  我以2011年心理學再現危機開始爆發的指標事件，與近兩年引發議論的「權力姿勢」為例，說明以新奇理論包裝的劣質研究能被發表，並獲得關注的關鍵。現在的開放科學採用那些措施扭轉這些問題。這些措施與資料科學的建模有沒有抵觸？或是相輔相成的可能？  最後以curate science project為例，說明如何運用累積數據，引導一般科學研究人員提昇研究操作品質，建構以可再現結果為基礎的實用理論。(字串長度:805)```company---->慈濟大學(字串長度:12)---->開放科學中心大使(字串長度:24)```position---->開放科學中心大使(字串長度:24)---->錄音不錄影(字串長度:15)```desc----> 從資料發掘新奇實用理論的機器學習，是近幾年台灣社會及學術界許多領域的顯學。近期學術界造假事件層出不窮，我認為這時候值得了解開放科學的宗旨：累積可重製的資料，才能建構經得起考驗的理論。<br><br>我以2011年心理學再現危機開始爆發的指標事件，與近兩年引發議論的「權力姿勢」為例，說明以新奇理論包裝的劣質研究能被發表，並獲得關注的關鍵。現在的開放科學採用那些措施扭轉這些問題。這些措施與資料科學的建模有沒有抵觸？或是相輔相成的可能？<br><br>最後以curate science project為例，說明如何運用累積數據，引導一般科學研究人員提昇研究操作品質，建構以可再現結果為基礎的實用理論。(字串長度:817)---->慈濟大人類發展與心理學系專任助理教授 開放科學中心大使(Center for Open Science Ambassador)<br><br>在台灣教了幾年書，2014年受到開放科學的知識論感召，親身到荷蘭體會身體力行之路。希望向華文大眾與學術界傳播分辨好壞科學的心法。(字串長度:307)```conf---->DS(字串長度:2)---->陳紹慶(字串長度:9)```website---->http://scchen.com(字串長度:17)---->http://陳紹慶(字串長度:16)'),(84,'2017-08-28 09:01:44',8,'name---->蘇中才(字串長度:9)---->男(字串長度:3)```name2---->蘇中才(字串長度:9)---->亞大基因(字串長度:12)```front_photo---->https://drive.google.com/open?id=0Bx2Wt2CYnDW2NkdtVFNhTDRETUU(字串長度:61)---->是(字串長度:3)```talk_title---->靠寫程式來翻轉醫療－從基因體大數據出發(字串長度:57)---->第一個人類基因體草稿於西元2000年被公布後，正式進入後基因體時代，但因基因體定序價錢居高不下而無法落實到臨床醫療。近年來，由於次世代定序技術的進步，人類全基因體定序的價錢下降速度比莫爾定律還快，市場化到臨床醫療的時代即將到來。藉由人工智慧與大數據的技術演進，大家可以靠寫程式來讓基因密碼一一解密，將從傳統醫療邁入精準醫療 (Precision Medicine)的世代，但大家是否準備好面對它呢？試問，以後大家去醫院看病，不是帶著健保卡，而是帶著你的基因序列去給醫生看？或者是給 AlphaGo 看呢？在台灣，多元成家議題一直爭議不休，試問，目前性別是依照性染色體來判斷，但這是否正確？針對這些具爭議性的議題用數據來進行探討，除了帶領各位資料科學家們一窺精準醫療的領域，更希望帶給大家以更多元的角度來面對自己生活週遭的每一個數據。(字串長度:1030)```company---->亞大基因(字串長度:12)---->科學長(字串長度:9)```position---->科學長(字串長度:9)---->是(字串長度:3)```desc---->第一個人類基因體草稿於西元2000年被公布後，正式進入後基因體時代，但因基因體定序價錢居高不下而無法落實到臨床醫療。近年來，由於次世代定序技術的進步，人類全基因體定序的價錢下降速度比莫爾定律還快，市場化到臨床醫療的時代即將到來。藉由人工智慧與大數據的技術演進，大家可以靠寫程式來讓基因密碼一一解密，將從傳統醫療邁入精準醫療 (Precision Medicine)的世代，但大家是否準備好面對它呢？試問，以後大家去醫院看病，不是帶著健保卡，而是帶著你的基因序列去給醫生看？或者是給 AlphaGo 看呢？在台灣，多元成家議題一直爭議不休，試問，目前性別是依照性染色體來判斷，但這是否正確？針對這些具爭議性的議題用數據來進行探討，除了帶領各位資料科學家們一窺精準醫療的領域，更希望帶給大家以更多元的角度來面對自己生活週遭的每一個數據。(字串長度:1030)---->2007年畢業於台灣大學資訊工程研究所，博士論文題目為「蛋白質非穩定區段之預測與特性研究」。之後加入趨勢科技從事雲端平台的建置與相關演算法開發，利用資料探勘與機器學習等技術來主動偵測垃圾郵件、色情網站及惡意軟體。將處理人類病毒相關演算法運用到處理電腦病毒上，跨領域活用演算法來有效處理資料。2015年成立亞大基因，建立一個擁有生物資訊和大數據分析技術雙重優勢的軟體研發團隊，專注致力於發展創新的人類基因序列數據挖掘（Data Mining）技術及分析軟體平台（Analytics Platform），進而幫助臨床醫師有效利用基因體資料提供病人精準的醫療。(字串長度:757)```conf---->DS(字串長度:2)---->蘇中才(字串長度:9)```website---->http://www.atgenomix.com(字串長度:24)---->http://蘇中才(字串長度:16)'),(85,'2017-08-28 09:01:44',10,'name---->吳牧恩(字串長度:9)---->男(字串長度:3)```name2---->吳牧恩(字串長度:9)---->台北科技大學(字串長度:18)```front_photo---->https://drive.google.com/open?id=0B0W2fuFPpPC9ZlZJMnhjejJvZDg(字串長度:61)---->是(字串長度:3)```talk_title---->資料科學時代下的智能交易初體驗(字串長度:45)---->隨著雲端技術與電腦運算的成長，金融交易模式也跟著大幅改變。過去人工方式選股，近年來程式交易興起，設計交易演算法由電腦自動下單逐漸蔚為風潮，許多人都想自行撰寫程式建立自己的演算法交易事業。而近年來交易環境的改變，利用傳統價量進行研發已不敷使用，而機器學習似乎為我們帶來一片希望? 究竟機器學習機制該如何用於交易策略研發? 深度學習技術能否帶來交易的AlphaGO?演算法交易該用什麼樣的工具? 本演講將針對這些問題進行探討。(字串長度:605)```company---->台北科技大學(字串長度:18)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->是(字串長度:3)```desc---->隨著雲端技術與電腦運算的成長，金融交易模式也跟著大幅改變。過去人工方式選股，近年來程式交易興起，設計交易演算法由電腦自動下單逐漸蔚為風潮，許多人都想自行撰寫程式建立自己的演算法交易事業。而近年來交易環境的改變，利用傳統價量進行研發已不敷使用，而機器學習似乎為我們帶來一片希望? 究竟機器學習機制該如何用於交易策略研發? 深度學習技術能否帶來交易的AlphaGO?演算法交易該用什麼樣的工具? 本演講將針對這些問題進行探討。(字串長度:605)---->吳牧恩教授為數理背景出身、資工博士畢業，其研究領域包括資訊理論與金融資料分析。現為台北科技大學資訊與財經管理系助理教授，致力於推廣資金管理的數學與使用 R 語言研發金融交易策略，期望破除市場上大眾追求穩賺聖盃的迷思。其亦為知名財金部落格幣圖誌 (Bituzi) 專欄作家 -- 牧清華。(字串長度:395)```conf---->DS(字串長度:2)---->吳牧恩(字串長度:9)```website---->(字串長度:0)---->http://吳牧恩(字串長度:16)'),(86,'2017-08-28 09:01:44',11,'name---->邵軒磊(字串長度:9)---->男(字串長度:3)```name2---->邵軒磊(字串長度:9)---->台師大(字串長度:9)```front_photo---->https://drive.google.com/open?id=0B0d16GGNlXlQWTRlUl9oNEpGMWs(字串長度:61)---->是(字串長度:3)```talk_title---->AI、大數據、法資訊學--法律與判決中的資料科學(字串長度:64)---->傳統上，法律與判決研究中多以價值判斷與理念分析為主，而在大數據與人工智慧（AI）的趨勢之下，逐漸有學者思考結合新技術開展「法資訊學研究」。本文作為階段性的成果，旨在分析「離婚後小孩歸誰？」（法學中稱為酌定親權人）之運作情形，而得到有效的計算結果。闡釋這類之分析結果，有助於當事人能對與自身訴訟優劣勢上有初步認識，也能對法官對自身判決與其他判決之「共通性」上有所預期（避免恐龍法官），有助於弭平「法院判決與社會民意」之巨大差異。隨著科技發達，法資訊學研究能夠做到研究大範圍時間段與多數案例重複出現之現象，而較少涉及非常特殊之個案，而受到影響。期望在法資訊學之開展中，能提供一個論證與分析的有效知識基礎，讓不同意見的研究者，有據以對話討論的平臺和起點；也藉此機會，歡迎資訊學科的同好一起合作。(字串長度:1028)```company---->台師大(字串長度:9)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->否(字串長度:3)```desc---->傳統上，法律與判決研究中多以價值判斷與理念分析為主，而在大數據與人工智慧（AI）的趨勢之下，逐漸有學者思考結合新技術開展「法資訊學研究」。本文作為階段性的成果，旨在分析「離婚後小孩歸誰？」（法學中稱為酌定親權人）之運作情形，而得到有效的計算結果。闡釋這類之分析結果，有助於當事人能對與自身訴訟優劣勢上有初步認識，也能對法官對自身判決與其他判決之「共通性」上有所預期（避免恐龍法官），有助於弭平「法院判決與社會民意」之巨大差異。隨著科技發達，法資訊學研究能夠做到研究大範圍時間段與多數案例重複出現之現象，而較少涉及非常特殊之個案，而受到影響。期望在法資訊學之開展中，能提供一個論證與分析的有效知識基礎，讓不同意見的研究者，有據以對話討論的平臺和起點；也藉此機會，歡迎資訊學科的同好一起合作。(字串長度:1028)---->黃詩淳，台大法學士、日本北海道大學法學碩士博士。主要研究領域為身分法， 近來關心高齡者人權、財產管理與代際財產移轉議題，包括繼承、遺囑、成年監護、信託等。教授課目有身分法、物權、民法概要、日文法學名著選讀等課程。近年開展法資訊學的研究。著有〈遺產繼承之圖像與原理解析〉、〈涉訟榮民遺囑之特徵與法律問題〉等論文，請參見http://homepage.ntu.edu.tw/~schhuang/Welcome.html 邵軒磊，台大法學士、政大法學碩士博士，專長：國際關係政治學。目前對資料蒐集、模型預測、可視化具有興趣。主要使用R(major)、python(minor)，以及SAS、SPSS、matlab等軟體，詳情請參考：http://web.ntnu.edu.tw/~hlshao/。(字串長度:803)```conf---->DS(字串長度:2)---->邵軒磊(字串長度:9)```website---->http://homepage.ntu.edu.tw/~schhuang/Welcome.html , http://web.ntnu.edu.tw/~hlshao/(字串長度:83)---->http://邵軒磊(字串長度:16)'),(87,'2017-08-28 09:01:44',12,'name---->李祈均(字串長度:9)---->男(字串長度:3)```name2---->李祈均 (Jeremy)(字串長度:18)---->清大電機(字串長度:12)```front_photo---->https://drive.google.com/open?id=0B9qZsKw-A44IR1p4d2ZtaDFEWjg(字串長度:61)---->是(字串長度:3)```talk_title---->機器智能與人類行為: 跨領域決策分析於醫療應用(字串長度:65)---->長年來領域專家透過解析感測器訊號、歸納有用資訊、並進行適切處理以完成各種與人息息相關的決策應用(decision analytics)。在(非)結構化資料快速累積、機器學習演算法高速進展，並配合深度跨領域知識整合下，依機器智開發嶄新的訊息決策分析架構正蓬勃發展中 。在此一演講中，我們會以多個醫療應用為主軸，分享我們近期透過機器智能並以人本運算角度為出發而開發出「高可信度(reliability)」、「可規模化(scalable)」、「客觀且一致(objective)」 的人類訊息行為分析決策於醫療應用(AI-in-Health Behavior Analytics)。實例分享包涵：臨床血癌診斷輔助、未來中風風險評估、可計算式自閉症失調行為、及內在情緒感受辨識等。此一技術發展更仰賴深度跨領域合作，以提供專家現實實務上有效且全新變革式的分析工具。(字串長度:946)```company---->清大電機(字串長度:12)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->是(字串長度:3)```desc---->長年來領域專家透過解析感測器訊號、歸納有用資訊、並進行適切處理以完成各種與人息息相關的決策應用(decision analytics)。在(非)結構化資料快速累積、機器學習演算法高速進展，並配合深度跨領域知識整合下，依機器智開發嶄新的訊息決策分析架構正蓬勃發展中 。在此一演講中，我們會以多個醫療應用為主軸，分享我們近期透過機器智能並以人本運算角度為出發而開發出「高可信度(reliability)」、「可規模化(scalable)」、「客觀且一致(objective)」 的人類訊息行為分析決策於醫療應用(AI-in-Health Behavior Analytics)。實例分享包涵：臨床血癌診斷輔助、未來中風風險評估、可計算式自閉症失調行為、及內在情緒感受辨識等。此一技術發展更仰賴深度跨領域合作，以提供專家現實實務上有效且全新變革式的分析工具。(字串長度:946)---->李祈均目前是國立清華大學電機工程學系助理教授，研究領域橫跨訊號處理與機器學習技術，尤其針對人類行為進行分析、預測及統計建模。他長期著重於運用不同種類的資料檔（文字、聲音、影像等）以研發跨領域，如醫療、教育、心理，相關演算法。他過去擔曾任ID Analytics公司旗下id:a lab的資料科學家，開發分析信用申請函進行金融信用產品的開發。他目前與榮總、國衛院、長庚、台大、國教院等機構合作研究，針對人類行為進行量化建模與客觀辨識。(字串長度:608)```conf---->DS(字串長度:2)---->李祈均(字串長度:9)```website---->http://biic.ee.nthu.edu.tw/(字串長度:27)---->http://李祈均 (Jeremy)(字串長度:25)'),(88,'2017-08-28 09:01:44',13,'name---->黃彥棕(字串長度:9)---->男(字串長度:3)```name2---->黃彥棕(字串長度:9)---->中研院統計所(字串長度:18)```front_photo---->https://drive.google.com/open?id=0B9NGw7J6b8gXaU00VzBmcGRoc1U(字串長度:61)---->是(字串長度:3)```talk_title---->Challenges and opportunities of confounding in data sciences: a case study of smoking and lung cancer mortality 干擾機制對資料科學帶來的挑戰與契機：以抽煙及肺癌存活的因果關係為例(字串長度:211)---->Complexity of the data may provide challenges for making valid causal inference about a scientific hypothesis (smoking vs. lung cancer mortality), particularly in the presence of unknown confounding (underlying lung function). Mendelian randomization (MR) addresses the issue of unknown confounding by using genetic information as an instrumental variable (IV) to estimate the causal effect of an exposure of interest on an outcome. Despite the popularity of IV analyses in fully observable outcomes, methodology is limited for time-to-event survival outcomes with censoring, a common data structure in biomedical sciences. We propose an IV analysis method in the survival context, estimating causal effects on a transformed survival time and the survival probabilities using semiparametric transformation models. We construct unbiased estimating equations to circumvent the difficulty in deriving joint likelihood of the exposure and the outcome, due to the unknown covariance by confounding. Asymptotic properties of the proposed estimators are established. We apply our methods to conduct an MR study for lung cancer survival, which suggests a harmful prognostic effect by smoking intensity (p=0.0067) that would have been missed by the conventional methods. (字串長度:1262)```company---->中研院統計所(字串長度:18)---->副研究員(字串長度:12)```position---->副研究員(字串長度:12)---->是(字串長度:3)```desc---->Complexity of the data may provide challenges for making valid causal inference about a scientific hypothesis (smoking vs. lung cancer mortality), particularly in the presence of unknown confounding (underlying lung function). Mendelian randomization (MR) addresses the issue of unknown confounding by using genetic information as an instrumental variable (IV) to estimate the causal effect of an exposure of interest on an outcome. Despite the popularity of IV analyses in fully observable outcomes, methodology is limited for time-to-event survival outcomes with censoring, a common data structure in biomedical sciences. We propose an IV analysis method in the survival context, estimating causal effects on a transformed survival time and the survival probabilities using semiparametric transformation models. We construct unbiased estimating equations to circumvent the difficulty in deriving joint likelihood of the exposure and the outcome, due to the unknown covariance by confounding. Asymptotic properties of the proposed estimators are established. We apply our methods to conduct an MR study for lung cancer survival, which suggests a harmful prognostic effect by smoking intensity (p=0.0067) that would have been missed by the conventional methods. (字串長度:1262)---->黃博士於2003自台大醫學系畢業，退伍後前往美國哈佛大學先後取得公共衛生碩士（2006, 主修計量方法）、生物統計碩士（2009）、流行病學及生物統計理學博士雙學位（2012）。研究所畢業後於美國布朗大學流行病學系及生物統計系擔任助理教授（2012-2016），於2016年9月返台於中研院統計所任職。黃博士之研究曾獲得國際生統學會東北美地區Research Advisory Board Poster Award（2012）、美國統計學會David P Byar Young Investigator Travel Award（2012）、布朗大學Richard Salomon Faculty Research Award（2013）及布朗大學Junior Research Award in Genetic Studies（2015）。黃博士研究領域包括：生物統計、因果推論、存活分析、流行病學及基因體學。(字串長度:806)```conf---->DS(字串長度:2)---->黃彥棕(字串長度:9)```website---->http://www.stat.sinica.edu.tw/personinfo/?sysid=5017880(字串長度:55)---->http://黃彥棕(字串長度:16)'),(89,'2017-08-28 09:01:44',14,'name---->李家岩(字串長度:9)---->男(字串長度:3)```name2---->李家岩(字串長度:9)---->教育(字串長度:6)```front_photo---->https://drive.google.com/open?id=0BwMeWHVjMsRLZGdZQXRnOEQ0VUE(字串長度:61)---->是(字串長度:3)```talk_title---->製造資料？科學？！(字串長度:27)---->資料科學 (Data Science) 與工業 4.0(Industry 4.0) 是近幾年來廣為討論的主題。在製造業的轉型過程中，如何以資料科學的角度，整合自動化實務和管理經驗，導入方法論以達到智慧製造 (Manufacturing Intelligence) 的理想。本演講藉由實際製造業個案瞭解製造現場解決問題的流程與方法，以實證「現地現物」角度來驗證模型的績效。從瞭解製造現場的特性開始著手，解說智慧型製造系統的概念與運作原理。並根據製造現場的特性，與資料所呈現出的特殊樣型作一比對，例如：平行機台、抽樣檢測、維修保養、換線換模前置作業等，來釐清製造現場與資料之間的迷思，與各位學員一同分享與探討。(字串長度:779)```company---->教育(字串長度:6)---->工作者(字串長度:9)```position---->工作者(字串長度:9)---->否(字串長度:3)```desc---->資料科學 (Data Science) 與工業 4.0(Industry 4.0) 是近幾年來廣為討論的主題。在製造業的轉型過程中，如何以資料科學的角度，整合自動化實務和管理經驗，導入方法論以達到智慧製造 (Manufacturing Intelligence) 的理想。本演講藉由實際製造業個案瞭解製造現場解決問題的流程與方法，以實證「現地現物」角度來驗證模型的績效。從瞭解製造現場的特性開始著手，解說智慧型製造系統的概念與運作原理。並根據製造現場的特性，與資料所呈現出的特殊樣型作一比對，例如：平行機台、抽樣檢測、維修保養、換線換模前置作業等，來釐清製造現場與資料之間的迷思，與各位學員一同分享與探討。(字串長度:779)---->喜歡被稱為「教育工作者」，希望能為教育坦然地做一點事。喜歡以參謀的角度思考與思辨，探究問題的本質。除了喜歡到處遊山玩水趴趴走，也喜歡進製造現場「觀察」，相信「魔鬼都藏在製造現場的細節裡」。他的研究興趣是作業研究、製造資料科學、生產經濟學、多目標決策分析。熱衷於以統計方法與最佳化技術改善製造現場的生產效率與品質，相關應用包含產能規劃、生產排程、錯誤診斷、預測保養、設備健康指標、虛擬物料品質檢測等。(字串長度:597)```conf---->DS(字串長度:2)---->李家岩(字串長度:9)```website---->http://polab.imis.ncku.edu.tw/(字串長度:30)---->http://李家岩(字串長度:16)'),(90,'2017-08-28 09:01:44',16,'name---->Meeyoung Cha(字串長度:12)---->女(字串長度:3)```name2---->Meeyoung Cha(字串長度:12)---->KAIST / School of Computing (字串長度:28)```front_photo---->https://drive.google.com/open?id=0B7s_TAiINyuFZWlfV1ZsekVUbHc(字串長度:61)---->否(字串長度:3)```talk_title---->Detecting rumors and fake news online(字串長度:37)---->Social platforms are an ideal place for spreading rumors and fake news. As more people seek information and read news online, automatically debunking such false claims has become an urgent problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand their propagation processes. In detecting rumors and fake news in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early detection. I will also highlight the latest detection studies with deep learning techniques.   Short Bio Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:1554)```company---->KAIST / School of Computing (字串長度:28)---->associate professor(字串長度:19)```position---->associate professor(字串長度:19)---->是(字串長度:3)```desc---->Social platforms are an ideal place for spreading rumors and fake news. As more people seek information and read news online, automatically debunking such false claims has become an urgent problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand their propagation processes. In detecting rumors and fake news in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early detection. I will also highlight the latest detection studies with deep learning techniques.<br><br> Short Bio Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:1560)---->Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:790)```conf---->DSK(字串長度:3)---->MEEYOUNG CHA(字串長度:12)```website---->http://ds.kaist.ac.kr/(字串長度:22)---->http://Meeyoung Cha(字串長度:19)'),(91,'2017-08-28 09:01:45',19,'name---->李宏毅(字串長度:9)---->男(字串長度:3)```name2---->李宏毅(字串長度:9)---->台大電機(字串長度:12)```front_photo---->https://drive.google.com/open?id=0Bzs7OI1N_nsYRDBsY1dqZE94c2s(字串長度:61)---->否(字串長度:3)```talk_title---->GAN (不是髒話)(字串長度:18)---->為什麼最近在做深度學習的人都一直在 GAN 來 GAN 去的呢？那是因為有一個新的技術叫生成式對抗網路 (Generative Adversarial Network, GAN)，這個技術顯然是深度學習的下一個熱點。在機器學習領域，回歸 (regression) 和分類 (classification) 這兩項任務的解法人們已經不再陌生，但是如何讓機器更進一步創造出有結構的複雜物件 (例如：圖片、文句) 仍是一大挑戰。用生成式對抗網路，機器不只可以畫出以假亂真的人臉，也可以根據一段敘述文字，自己畫出對應的圖案。本課程希望能帶大家認識生成式對抗網路這個深度學習最前沿的技術。(字串長度:699)```company---->台大電機(字串長度:12)---->助理教授(字串長度:12)```position---->助理教授(字串長度:12)---->是(字串長度:3)```desc---->為什麼最近在做深度學習的人都一直在 GAN 來 GAN 去的呢？那是因為有一個新的技術叫生成式對抗網路 (Generative Adversarial Network, GAN)，這個技術顯然是深度學習的下一個熱點。在機器學習領域，回歸 (regression) 和分類 (classification) 這兩項任務的解法人們已經不再陌生，但是如何讓機器更進一步創造出有結構的複雜物件 (例如：圖片、文句) 仍是一大挑戰。用生成式對抗網路，機器不只可以畫出以假亂真的人臉，也可以根據一段敘述文字，自己畫出對應的圖案。本課程希望能帶大家認識生成式對抗網路這個深度學習最前沿的技術。(字串長度:699)---->現為臺大電機系助理教授，他的研究主軸是以機器學習技術讓機器辨識並理解語音訊號的內容。以深度學習技術為基石，他正致力於語音數位內容搜尋、語音數位內容之自動化組織以及從語音數位內容擷取關鍵資訊等前瞻性研究，這些技術有很多的應用，例如：人機互動、問答系統、智慧型線上教學平台等等。他曾在資料科學愛好者協會講授《一日搞懂深度學習》四次，他在臺大開設的機器學習相關課程錄影請見：https://www.youtube.com/channel/UC2ggjtuuWvxrHHHiaDH1dlQ/playlists(字串長度:618)```conf---->DS(字串長度:2)---->李宏毅(字串長度:9)```website---->http://speech.ee.ntu.edu.tw/~tlkagk/(字串長度:36)---->http://李宏毅(字串長度:16)'),(92,'2017-08-28 09:01:45',20,'name---->彭其捷(字串長度:9)---->男(字串長度:3)```name2---->彭其捷(字串長度:9)---->卡米爾股份有限公司(字串長度:27)```front_photo---->https://drive.google.com/open?id=0B04RI8T3EjsKSGp6THdYWDNOYzg(字串長度:61)---->是(字串長度:3)```talk_title---->資料視覺化與使用者經驗設計(字串長度:39)---->許多人都了解資料視覺化的優點，例如可將資料抽象化、讓資訊降維等等，讓數據以更直覺的方式呈現在使用者面前，提升閱讀者的吸收意願。然而，我們依然可以舉出許多例子，即使是視覺化後的數據，依然不容易讓人讀懂其價值，也難以勾起人們理解的意願。  『使用者經驗（UX）』設計的最終目標是『Don’t Make Me Think』，也就是打造一段順暢的閱讀過程，讓使用者能夠更輕鬆理解背後的寶貴價值，而這之中除了透過視覺設計、互動設計將結果優化之外，還包括了使用者研究、需求釐清、文案優化等等技巧，如果能夠將使用者經驗設計相關方法結合資料視覺化技巧，能夠再一次降低數據解讀的門檻，讓更多人了解其價值意涵，也讓更多人被數據所感動。(字串長度:877)```company---->卡米爾股份有限公司(字串長度:27)---->數據專案經理(字串長度:18)```position---->數據專案經理(字串長度:18)---->是(字串長度:3)```desc---->許多人都了解資料視覺化的優點，例如可將資料抽象化、讓資訊降維等等，讓數據以更直覺的方式呈現在使用者面前，提升閱讀者的吸收意願。然而，我們依然可以舉出許多例子，即使是視覺化後的數據，依然不容易讓人讀懂其價值，也難以勾起人們理解的意願。<br><br>『使用者經驗（UX）』設計的最終目標是『Don’t Make Me Think』，也就是打造一段順暢的閱讀過程，讓使用者能夠更輕鬆理解背後的寶貴價值，而這之中除了透過視覺設計、互動設計將結果優化之外，還包括了使用者研究、需求釐清、文案優化等等技巧，如果能夠將使用者經驗設計相關方法結合資料視覺化技巧，能夠再一次降低數據解讀的門檻，讓更多人了解其價值意涵，也讓更多人被數據所感動。(字串長度:883)---->彭其捷畢業於交大數據分析實驗室，對於網路服務設計有巨大熱情，曾負責過：資料科學、資料視覺化、前端工程、服務設計等等類型專案，出版過 3 本使用者經驗專書，致力於打造良善的網路服務體驗，近期多投入資料科學相關主題，期待能夠創造更多資料之間的連結，讓數據分析更平民化。(字串長度:390)```conf---->DS(字串長度:2)---->彭其捷(字串長度:9)```website---->https://about.me/divaka(字串長度:23)---->http://彭其捷(字串長度:16)'),(93,'2017-08-28 09:02:00',2,'name---->男(字串長度:3)---->張詠淳(字串長度:9)```name2---->台北醫學大學大數據科技與管理研究所(字串長度:51)---->張詠淳(字串長度:9)```front_photo---->是(字串長度:3)---->https://drive.google.com/open?id=0Bw_OtFTydVCySU94dXlYZGFYOHc(字串長度:61)```talk_title---->在這個網路普及的時代，社群多媒體已經成為人們分享與取得知識的重要來源，讓我們一同探索文字探勘技術用於社群多媒體分析的奧妙吧！！(字串長度:189)---->一窺文字探勘之奧妙(字串長度:27)```company---->博士(字串長度:6)---->台北醫學大學大數據科技與管理研究所(字串長度:51)```position---->是(字串長度:3)---->博士(字串長度:6)```desc---->張詠淳博士專精於文字探勘與檢索、對談式問答機器人，以及相關的機器學習與語言辨識技術。張博士自2008年就在中央研究院服國防役，其間參與了自然輸入法開發，於2016年擔任博士後研究員，並榮獲中研院博士後傑出研究獎。張博士近年來發表多篇國內外期刊論文以及會議論文，其中包含了頂尖期刊TKDE以及頂尖國際會議論文ACL等。近期的研究重點之一在於結合句構知識表達與機器學習，打造一個具深度理解的智慧型對談機器人。另一個研究重點則是基於過去在文字分類上相關的研究成果，研發一個全自動的文字分類架構，此應用非常廣泛，例如：情感分析、主題偵測、甚至是問答系統等。(字串長度:792)---->在這個網路普及的時代，社群多媒體已經成為人們分享與取得知識的重要來源，讓我們一同探索文字探勘技術用於社群多媒體分析的奧妙吧！！(字串長度:189)```website---->http://張詠淳(字串長度:16)---->https://www.iis.sinica.edu.tw/pages/changyc(字串長度:43)'),(94,'2017-08-28 09:02:00',3,'name---->男(字串長度:3)---->王耀聰(字串長度:9)```name2---->TenMax(字串長度:6)---->Jazz Wang(字串長度:9)```front_photo---->否(字串長度:3)---->https://drive.google.com/open?id=0B6_uKxs0uwmubnhwZDdIRkN2eWc(字串長度:61)```talk_title---->中小企業與新創團隊看過來～本分享將以訪客追蹤像素（Tracking Pixel）為例，說明如何蒐集網站訪客的線上行為，並比較不同的資料匯流（Data Pipeline）設計的成本結構，藉此說明近期雲服務 Serverless 與微服務 (Microservices) 的趨勢如何幫您省錢。(字串長度:320)---->Data Pipeline Matters -- 以 Tracking Pixel 為例(字串長度:50)```company---->Data Architect(字串長度:14)---->TenMax(字串長度:6)```position---->是(字串長度:3)---->Data Architect(字串長度:14)```desc---->- 台灣資料工程協會發起人 - 台灣 Hadoop 使用者社群共同創辦人/傳教士 - Hadoop : The Definitive Guide、Hadoop Operations 兩書譯者(字串長度:157)---->中小企業與新創團隊看過來～本分享將以訪客追蹤像素（Tracking Pixel）為例，說明如何蒐集網站訪客的線上行為，並比較不同的資料匯流（Data Pipeline）設計的成本結構，藉此說明近期雲服務 Serverless 與微服務 (Microservices) 的趨勢如何幫您省錢。(字串長度:320)```website---->http://Jazz Wang(字串長度:16)---->https://about.me/jazzwang(字串長度:25)'),(95,'2017-08-28 09:02:00',5,'name---->女(字串長度:3)---->胡筱薇(字串長度:9)```name2---->School of BigData Management(字串長度:28)---->Camille Hu(字串長度:10)```front_photo---->否(字串長度:3)---->https://drive.google.com/open?id=0B1-7HeusmFbPTWk0cXdiLUpUc1E(字串長度:61)```company---->Assistant Professor(字串長度:19)---->School of BigData Management(字串長度:28)```position---->是(字串長度:3)---->Assistant Professor(字串長度:19)```desc---->喜愛新鮮的事物，對於資訊科技充滿熱情，外表看起來很活潑，但骨子裡是個阿宅，沒事喜歡窩在家裡觀察其他阿宅都在做什麼，愛家、愛耶穌，目前擔任東吳大學巨量資料管理學院助理教授，勉強算是個Data Watcher，也勉強算是個Data Player，近年來致力於巨量資料探勘以及社群網路分析應用之研究，同時也成立了資料實驗室(Data Lab)，並透過實際的專案項目，培養巨量資料分析人才。(字串長度:507)---->TBA(字串長度:3)'),(96,'2017-08-28 09:02:00',6,'name---->男(字串長度:3)---->曾俊雄(字串長度:9)```name2---->元智大學通訊工程系(字串長度:27)---->曾俊雄(字串長度:9)```front_photo---->否(字串長度:3)---->https://drive.google.com/open?id=0B59IzUnV5CLjcXNmRHFrVkE3NjQ(字串長度:61)```talk_title---->身為教職人員，看著講臺下的學子們，不免有一些期許，但是，我經常不由得問自己，小朋友們到底有沒有在聽課呢? 因緣際會，接觸了情感學習這個領域，帶著實驗團隊找了一些從情感反映來窺探小朋友想法的小研究，在本次演講中，會試著將這些研究結果進行說明及整理。(字串長度:365)---->課堂資料學：到底有沒有在上課(字串長度:42)```company---->助理教授/軟體工坊主持人(字串長度:34)---->元智大學通訊工程系(字串長度:27)```position---->是(字串長度:3)---->助理教授/軟體工坊主持人(字串長度:34)```desc---->我常覺得我們處於一個很有趣的時代，有機會親身體會到應用領域轉變的好幾個過程：首先是從一般單機型應用轉變成伺服端的架構，另外這幾年慢慢從伺服端架構又轉變成web-based的架構，大家討論的議題又從分散式計算轉成grid computing、web service，big data，以及 cloud computing，這幾年人工智慧又重新受到關注。面對這樣的轉變，我覺得過去我們談論的許多模型，不論是程式開發的模型，或是分散式計算的處理，其實有機會作一些調整，悠遊於新的時代，有很多新東西可以玩，在這樣的時間點做一個研究人員，是很幸福的。(字串長度:694)---->身為教職人員，看著講臺下的學子們，不免有一些期許，但是，我經常不由得問自己，小朋友們到底有沒有在聽課呢? 因緣際會，接觸了情感學習這個領域，帶著實驗團隊找了一些從情感反映來窺探小朋友想法的小研究，在本次演講中，會試著將這些研究結果進行說明及整理。(字串長度:365)'),(97,'2017-08-28 09:02:00',9,'name---->男(字串長度:3)---->蔡炎龍(字串長度:9)```name2---->政治大學應用數學系(字串長度:27)---->蔡炎龍(字串長度:9)```front_photo---->是(字串長度:3)---->https://drive.google.com/open?id=0ByqkbzbmgT9kWkJEMTBmWVAyalU(字串長度:61)```talk_title---->學習過基本深度學習的概念, 也會一些建構神經網路方便的工具如 Keras 之後,  我們總覺得馬上就可以用深度學習的方法做點東西出來, 但往往不如我們預想中的順利。這個演講中我們會先複習基本神經網路的架構, 接著分享在實務應用上經歷過的種種卡關, 以及克服這些障礙的方式。(字串長度:372)---->深度學習實作上的各種卡關(字串長度:36)```company---->副教授(字串長度:9)---->政治大學應用數學系(字串長度:27)```position---->是(字串長度:3)---->副教授(字串長度:9)```desc---->美國加州大學爾灣校區 (UC Irvine) 數學博士, 專攻純數學「代數幾何」領域, 但多年來看上 Python 「簡單、強大、可專注在問題而非程式語言本身」, 所以一直致力推廣 Python 程式語言。碩士班時期就研究神經網路, 直到約一年半前才重回深度學習的領域。已在多個場合介紹過深度學習的基本理論和實作方式, 也試著用深度學習的方法解決不同領域的問題。(字串長度:465)---->學習過基本深度學習的概念, 也會一些建構神經網路方便的工具如 Keras 之後,<br><br>我們總覺得馬上就可以用深度學習的方法做點東西出來, 但往往不如我們預想中的順利。這個演講中我們會先複習基本神經網路的架構, 接著分享在實務應用上經歷過的種種卡關, 以及克服這些障礙的方式。(字串長度:378)'),(98,'2017-08-28 09:02:00',15,'name---->男(字串長度:3)---->邱泊寰(字串長度:9)```name2---->卡米爾股份有限公司(字串長度:27)---->邱泊寰(字串長度:9)```front_photo---->否(字串長度:3)---->https://drive.google.com/open?id=0ByCp5JP3Rf13MXRnaC0xNUxIRk0(字串長度:61)```talk_title---->數據分析最困難的任務之一是找對題目，好題目的關鍵在相同預算創造最大效益ROI。政府單位其實臥虎藏龍，但礙於種種制度，有時難以伸展。透過Google Sprint體驗設計流程，漫天飛舞的便利貼融合共享大腦記憶體，鎖定數據洞見，創造幸福價值。(字串長度:325)---->政府數據分析與Google Sprint體驗設計(字串長度:46)```company---->創辦人(字串長度:9)---->卡米爾股份有限公司(字串長度:27)```position---->是(字串長度:3)---->創辦人(字串長度:9)```desc---->喜歡體驗設計、愛好說故事、一星期看七本書(字串長度:60)---->數據分析最困難的任務之一是找對題目，好題目的關鍵在相同預算創造最大效益ROI。政府單位其實臥虎藏龍，但礙於種種制度，有時難以伸展。透過Google Sprint體驗設計流程，漫天飛舞的便利貼融合共享大腦記憶體，鎖定數據洞見，創造幸福價值。(字串長度:325)'),(99,'2017-08-28 09:02:01',17,'name---->男(字串長度:3)---->陳榮錡(字串長度:9)```name2---->國泰金控(字串長度:12)---->RC(字串長度:2)```front_photo---->否(字串長度:3)---->https://drive.google.com/open?id=0B9ZFnovPXi90RXdXeWs0R0tpaFU(字串長度:61)```talk_title---->資料處理微服務架構設計與實現(字串長度:42)---->資料科學在國泰(字串長度:21)```company---->資料科學家(字串長度:15)---->國泰金控(字串長度:12)```position---->是(字串長度:3)---->資料科學家(字串長度:15)```desc---->曾任YAHOO,Trendmicro,Amazon等軟體公司的工程師，後來不務正業開始了資料科學研究之旅，直到現在。而現今，工作卻像是資料科學工作者的打雜者。(字串長度:191)---->資料處理微服務架構設計與實現(字串長度:42)'),(100,'2017-08-28 09:02:01',18,'name---->女(字串長度:3)---->鄭旭詠(字串長度:9)```name2---->中央大學資訊工程系(字串長度:27)---->鄭旭詠(字串長度:9)```front_photo---->否(字串長度:3)---->https://drive.google.com/open?id=0B38yagaBviZYZXBwYzUxbHd1aTA(字串長度:61)```talk_title---->智慧型監控的應用非常廣泛，本次演講將介紹兩種截然不同的應用，首先為應用於智慧型運輸系統之交通參數擷取，第二個是應用於綠色能源之太陽輻射量預測。從這兩個不同類型的應用中，可觀察出智慧型監控需要合適的取相裝置以和不同的應用特性配合，而不同的應用在取相與分析的過程中也會面臨不同的挑戰。從之亦可歸納出智慧型監控系統模組中的共同要件，並希望能夠啟發研究者將智慧型監控技術應用在其他跨領域之研究上。(字串長度:582)---->智慧型監控系統之廣泛應用(字串長度:36)```company---->教授(字串長度:6)---->中央大學資訊工程系(字串長度:27)```position---->否(字串長度:3)---->教授(字串長度:6)```desc---->鄭旭詠博士為美國華盛頓大學電機博士及國立中央大學資訊工程博士。於2008年開始擔任國立中央大學資訊工程系助理教授，並於2013年升等為副教授，2017年升等為教授。研究領域為電腦視覺、視訊分析、影像處理以及智慧型系統等。學術成果豐碩。主持或參與多項相關研究計畫，對學術研究以及產業界皆有具體之貢獻。曾獲多項論文相關獎項。並於2013年獲得中央大學資訊電機學院101學年度新進研究優良教師，2015, 2016, 2017中央大學研究傑出獎，105年度中華民國系統學會學術傑出青年獎，105年度中國電機工程學會優秀青年電機工程師獎，105年度青年獎章等獎項。 (字串長度:747)---->智慧型監控的應用非常廣泛，本次演講將介紹兩種截然不同的應用，首先為應用於智慧型運輸系統之交通參數擷取，第二個是應用於綠色能源之太陽輻射量預測。從這兩個不同類型的應用中，可觀察出智慧型監控需要合適的取相裝置以和不同的應用特性配合，而不同的應用在取相與分析的過程中也會面臨不同的挑戰。從之亦可歸納出智慧型監控系統模組中的共同要件，並希望能夠啟發研究者將智慧型監控技術應用在其他跨領域之研究上。(字串長度:582)```website---->http://鄭旭詠(字串長度:16)---->http://fox1.csie.ncu.edu.tw/~chengsy/index.shtml(字串長度:48)'),(101,'2017-08-28 09:02:01',21,'name---->男(字串長度:3)---->林鉦育(字串長度:9)```name2---->玉山銀行(字串長度:12)---->林鉦育(aha)(字串長度:14)```front_photo---->是(字串長度:3)---->https://drive.google.com/open?id=0BxEplIEPKoHfdVEzRThnWVAtOU0(字串長度:61)```talk_title---->根據玉山資料處理流程與業務應用經驗，分享實務故事與未來玉山AI發展應用的可能方向。(字串長度:119)---->Data and AI @ E.SUN bank(字串長度:24)```company---->襄理(字串長度:6)---->玉山銀行(字串長度:12)```position---->等內容確定後才能決定(字串長度:30)---->襄理(字串長度:6)```desc---->aha於2007年畢業於台大物理所，專長：奈米光學模擬，2009年進入中研院資訊所，主要研究：participantory sensing(參與式感測)，研究如何透過個人裝置感測搜集資料，並整合拼湊完整巨觀環境變化，2013年入行，初任職為商業分析小組，著重於透過資料探索找出商機與企劃方向．2015年成立並加入創新研發小組，主要負責各式新技術評估與導入，同時規劃與評估相關商業模式的落地與執行，參與數位旗艦分行與玉山小i 隨身金融顧問等專案。 aha 專長於組裝各式語言與各大平台服務，打造最小可行產品原型。同時關心社群與社會，如：Govcash 政治獻金研究是透過g0v的政治獻金數位化的成果；2014年為台灣資料科學愛好者年會R 課程講師．現正沈浸於台灣金融法規、具有台灣特色Fintech、AI、blockchain應用中。<br><br>(字串長度:934)---->根據玉山資料處理流程與業務應用經驗，分享實務故事與未來玉山AI發展應用的可能方向。(字串長度:119)```website---->http://林鉦育(aha)(字串長度:21)---->https://funny.aha.taipei(字串長度:24)'),(102,'2017-08-28 09:02:01',22,'name---->男(字串長度:3)---->黃士傑(字串長度:9)```name2---->Google DeepMind(字串長度:15)---->黃士傑 Aja Huang(字串長度:19)```front_photo---->否(字串長度:3)---->(字串長度:0)```talk_title---->本次演講我將談到AlphaGo發展歷程中的幾個關鍵(特別是深度學習與強化學習所帶進的重大突破)，2017年初Master網路60局的小故事，以及在中國烏鎮AlphaGo與柯潔九段的人機大戰。(字串長度:229)---->AlphaGo - 深度學習與強化學習的勝利(字串長度:46)```company---->Research Scientist(字串長度:18)---->Google DeepMind(字串長度:15)```position---->否(字串長度:3)---->Research Scientist(字串長度:18)```desc---->AlphaGo首席工程師。畢業於國立台灣師範大學資訊工程研究所，現在是Google DeepMind的資深研究員。(字串長度:127)---->本次演講我將談到AlphaGo發展歷程中的幾個關鍵(特別是深度學習與強化學習所帶進的重大突破)，2017年初Master網路60局的小故事，以及在中國烏鎮AlphaGo與柯潔九段的人機大戰。(字串長度:229)'),(103,'2017-08-29 02:24:03',22,'name---->黃士傑(字串長度:9)---->楊立偉(字串長度:9)```name2---->黃士傑 Aja Huang(字串長度:19)---->楊立偉(字串長度:9)```front_photo---->(字串長度:0)---->https://drive.google.com/open?id=0B2Xcy2sXQoHmR0NpUlNZZDdUcVE(字串長度:61)```talk_title---->AlphaGo - 深度學習與強化學習的勝利(字串長度:46)---->文字分析在財務上的應用-以股價預測為例(字串長度:55)```company---->Google DeepMind(字串長度:15)---->國立台灣大學(字串長度:18)```position---->Research Scientist(字串長度:18)---->教授(字串長度:6)```desc---->本次演講我將談到AlphaGo發展歷程中的幾個關鍵(特別是深度學習與強化學習所帶進的重大突破)，2017年初Master網路60局的小故事，以及在中國烏鎮AlphaGo與柯潔九段的人機大戰。(字串長度:229)---->神奇的文字分析，能夠從字裡行間，了解群眾及媒體對於股價的看好或看壞，進而預測未來的走勢。將以淺顯易懂的內容，搭配實際的例子，帶領聽眾感受非結構性資料分析的威力。(字串長度:240)```website---->http://黃士傑 Aja Huang(字串長度:26)---->http://homepage.ntu.edu.tw/~wyang/(字串長度:34)'),(104,'2017-08-29 05:42:18',3,'company---->TenMax(字串長度:6)---->騰學廣告(字串長度:12)'),(105,'2017-08-29 05:42:18',4,'company---->國立清華大學(字串長度:18)---->國立清華大學/電機系(字串長度:28)'),(106,'2017-08-29 05:42:18',5,'company---->School of BigData Management(字串長度:28)---->東吳大學/巨量資料管理學院(字串長度:37)'),(107,'2017-08-29 05:42:19',7,'company---->慈濟大學(字串長度:12)---->慈濟大學人類發展與心理學系(字串長度:39)'),(108,'2017-08-29 05:42:19',10,'company---->台北科技大學(字串長度:18)---->台北科技大學/資訊與財經管理系(字串長度:43)'),(109,'2017-08-29 05:42:19',11,'company---->台師大(字串長度:9)---->台師大/東亞系(字串長度:19)'),(110,'2017-08-29 05:42:19',12,'company---->清大電機(字串長度:12)---->國立清華大學電機工程系(字串長度:33)'),(111,'2017-08-29 05:42:19',13,'company---->中研院統計所(字串長度:18)---->中研院/統計所(字串長度:19)'),(112,'2017-08-29 05:42:19',14,'company---->教育(字串長度:6)---->國立成功大學資訊工程系暨製造資訊與系統研究所(字串長度:66)'),(113,'2017-08-29 05:42:19',17,'company---->國泰金控(字串長度:12)---->國泰金控資料科學實驗室(字串長度:33)'),(114,'2017-08-29 05:42:19',19,'company---->台大電機(字串長度:12)---->台大電機系(字串長度:15)'),(115,'2017-08-29 05:42:19',21,'company---->玉山銀行(字串長度:12)---->CRV(字串長度:3)'),(116,'2017-08-29 05:42:19',22,'company---->國立台灣大學(字串長度:18)---->國立台灣大學/工商管理學系(字串長度:37)'),(117,'2017-08-29 05:42:19',23,'company---->國立台灣大學(字串長度:18)---->國立台灣大學 / 地理環境資源系(字串長度:42)'),(118,'2017-08-29 05:42:20',24,'company---->清大資應所(字串長度:15)---->國立清華大學資應所(字串長度:27)'),(119,'2017-08-29 05:42:20',25,'company---->HTC DeepQ(字串長度:9)---->HTC Research & Healthcare(字串長度:25)'),(120,'2017-08-29 05:42:20',27,'company---->KKBOX(字串長度:5)---->KKBOX/Research Develop Center(字串長度:29)'),(121,'2017-08-29 05:42:20',29,'company---->中央研究院(字串長度:15)---->中央研究院 生物多樣性研究中心(字串長度:43)'),(122,'2017-08-29 05:42:20',31,'company---->東華大學應數系(字串長度:21)---->國立東華大學應用數學系(字串長度:33)'),(123,'2017-08-29 05:42:20',32,'company---->whoscall(字串長度:8)---->Gogolook(字串長度:8)'),(124,'2017-08-29 05:42:20',36,'company---->清華大學(字串長度:12)---->清華大學資工系(字串長度:21)'),(125,'2017-08-29 05:42:20',38,'company---->KKBOX RDC (字串長度:10)---->KKBOX (字串長度:6)'),(126,'2017-08-31 04:12:26',4,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(127,'2017-08-31 04:12:26',9,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(128,'2017-08-31 04:12:26',10,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(129,'2017-08-31 04:12:26',11,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(130,'2017-08-31 04:12:26',12,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(131,'2017-08-31 04:12:26',19,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(132,'2017-08-31 04:12:26',21,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(133,'2017-08-31 04:12:26',25,'conf---->DSK(字串長度:3)---->AIK(字串長度:3)'),(134,'2017-08-31 04:12:26',39,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(135,'2017-08-31 04:12:26',43,'position---->Kaggle Master(字串長度:13)---->kaggler(字串長度:7)```desc---->kaggle數海獅競賽簡介、心得。(字串長度:39)---->kaggle數海獅競賽簡介、心得。使用深度學習方法協助NOAA生物學家監控阿留申群島的海獅數量。(字串長度:124)```website---->(字串長度:0)---->https://www.kaggle.com/outrunner(字串長度:32)'),(136,'2017-08-31 05:34:12',4,'company---->國立清華大學/電機系(字串長度:28)---->清華大學電機系(字串長度:21)```conf---->DS(字串長度:2)---->AI(字串長度:2)'),(137,'2017-08-31 05:34:12',5,'company---->東吳大學/巨量資料管理學院(字串長度:37)---->東吳大學巨量資料管理學院(字串長度:36)'),(138,'2017-08-31 05:34:12',9,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(139,'2017-08-31 05:34:12',10,'company---->台北科技大學/資訊與財經管理系(字串長度:43)---->台北科技大學資訊與財經管理系(字串長度:42)```conf---->DS(字串長度:2)---->AI(字串長度:2)'),(140,'2017-08-31 05:34:12',11,'company---->台師大/東亞系(字串長度:19)---->台灣師範大學東亞系(字串長度:27)```conf---->DS(字串長度:2)---->AI(字串長度:2)'),(141,'2017-08-31 05:34:12',12,'company---->國立清華大學電機工程系(字串長度:33)---->清華大學電機工程系(字串長度:27)```conf---->DS(字串長度:2)---->AI(字串長度:2)'),(142,'2017-08-31 05:34:12',13,'company---->中研院/統計所(字串長度:19)---->中研院統計所(字串長度:18)'),(143,'2017-08-31 05:34:12',14,'company---->國立成功大學資訊工程系暨製造資訊與系統研究所(字串長度:66)---->成功大學製造資訊與系統研究所(字串長度:42)'),(144,'2017-08-31 05:34:12',16,'company---->KAIST / School of Computing (字串長度:28)---->KAIST School of Computing (字串長度:26)'),(145,'2017-08-31 05:34:12',19,'company---->台大電機系(字串長度:15)---->台灣大學電機工程系(字串長度:27)```conf---->DS(字串長度:2)---->AI(字串長度:2)'),(146,'2017-08-31 05:34:12',21,'company---->CRV(字串長度:3)---->玉山銀行 CRV(字串長度:16)'),(147,'2017-08-31 05:34:12',22,'company---->國立台灣大學/工商管理學系(字串長度:37)---->台灣大學工商管理學系(字串長度:30)'),(148,'2017-08-31 05:34:12',23,'company---->國立台灣大學 / 地理環境資源系(字串長度:42)---->台灣大學地理環境資源系(字串長度:33)'),(149,'2017-08-31 05:34:12',24,'company---->國立清華大學資應所(字串長度:27)---->清華大學資應所(字串長度:21)'),(150,'2017-08-31 05:34:12',25,'conf---->DSK(字串長度:3)---->AIK(字串長度:3)'),(151,'2017-08-31 05:34:12',27,'company---->KKBOX/Research Develop Center(字串長度:29)---->KKBOX Research Develop Center(字串長度:29)'),(152,'2017-08-31 05:34:12',28,'company---->台大醫院/均一教育平台(字串長度:31)---->台大醫院 / 均一教育平台(字串長度:33)'),(153,'2017-08-31 05:34:12',29,'company---->中央研究院 生物多樣性研究中心(字串長度:43)---->中央研究院生物多樣性研究中心(字串長度:42)'),(154,'2017-08-31 05:34:12',31,'company---->國立東華大學應用數學系(字串長度:33)---->東華大學應用數學系(字串長度:27)'),(155,'2017-08-31 05:34:12',33,'company---->國立交通大學(字串長度:18)---->交通大學資訊工程系(字串長度:27)'),(156,'2017-08-31 05:34:12',35,'company---->中央研究院統計科學所(字串長度:30)---->中央研究院統計科學研究所(字串長度:36)'),(157,'2017-08-31 05:34:12',37,'company---->中研院民族所(字串長度:18)---->中央研究院民族學研究所(字串長度:33)'),(158,'2017-08-31 05:34:12',40,'company---->中央研究院統計科學研究所(字串長度:36)---->中研院統計所(字串長度:18)'),(159,'2017-08-31 05:34:12',42,'company---->國立臺北商業大學 商業設計管理系(字串長度:46)---->臺北商業大學商業設計管理系(字串長度:39)'),(160,'2017-08-31 05:34:12',44,'company---->國立中正大學(字串長度:18)---->中正大學(字串長度:12)'),(161,'2017-08-31 05:34:12',46,'company---->Google, Inc(字串長度:11)---->Google(字串長度:6)'),(162,'2017-08-31 05:34:12',47,'company---->Skywatch Inc.(字串長度:13)---->Skywatch(字串長度:8)'),(163,'2017-08-31 07:15:30',52,'company---->政治大學/資訊科學系(字串長度:28)---->政治大學資訊科學系(字串長度:27)'),(164,'2017-08-31 07:15:31',60,'company---->工業技術研究院／巨量資訊科技中心(字串長度:48)---->工業技術研究院巨量資訊科技中心(字串長度:45)'),(165,'2017-08-31 07:15:31',61,'company---->無/創業中(字串長度:13)---->(字串長度:0)'),(166,'2017-08-31 07:15:31',62,'company---->趨勢科技/研究開發部(字串長度:28)---->趨勢科技研究開發部(字串長度:27)'),(167,'2017-08-31 07:15:31',65,'company---->交通大學資訊工程(字串長度:24)---->交通大學資訊工程系(字串長度:27)'),(168,'2017-09-01 03:01:59',25,'name---->張智威 (Edward Y. Chang)(字串長度:27)---->張智威(字串長度:9)'),(169,'2017-09-01 03:01:59',54,'company---->宏達國際電子(HTC)/健康醫療事業部(HealthCare)(字串長度:57)---->HTC Research & Healthcare(字串長度:25)'),(170,'2017-09-01 03:01:59',55,'company---->TrendMicro/SPN(字串長度:14)---->TrendMicro SPN(字串長度:14)'),(171,'2017-09-01 03:01:59',61,'company---->(字串長度:0)---->創業中(字串長度:9)'),(172,'2017-09-01 03:01:59',67,'company---->國家衛生研究院/群體健康科學研究所(字串長度:49)---->國家衛生研究院群體健康科學研究所(字串長度:48)'),(173,'2017-09-01 06:19:38',25,'conf---->DSK(字串長度:3)---->DS(字串長度:2)'),(174,'2017-09-01 06:19:38',72,'company---->替代役(字串長度:9)---->講師(字串長度:6)'),(175,'2017-09-02 15:08:10',1,'me_desc---->(字串長度:0)---->Kwan-Liu Ma (馬匡六) is a professor of computer science and the chair of the Graduate Group in Computer Science (GGCS) at the University of California-Davis, where he directs VIDI Labs and UC Davis Center of Excellence for Visualization. He is also a visiting professor of the National Taiwan University and a chair professor of the National Chiao Tung University. His research spans the fields of visualization, computer graphics, high-performance computing, and user interface design. Professor Ma received his PhD in computer science from the University of Utah in 1993. During 1993-1999, he was with ICASE/NASA Langley Research Center as a research scientist. He joined UC Davis in 1999.<br><br>Professor Ma is presently leading a team of 25 researchers pursuing projects driven by a wide range of real-world applications.<br><br>He received the NSF Presidential Early-Career Research Award (PECASE) in 2000, was elected an IEEE Fellow in 2012, and received the 2013 IEEE VGTC Visualization Technical Achievement Award for his outstanding research work. Professor Ma has been actively serving the research community by playing leading roles in many<br><br>professional activities in the area of visualization. He presently serves as the Associate Editor-in-Chief of the<br><br>IEEE Computer Graphics and Applications (CG&amp;A), and on the editorial board of the Journal of Visualization, the Journal of Visual Informatics, and the Journal of Computational Visual Media.(字串長度:1477)'),(176,'2017-09-02 15:08:10',2,'me_desc---->(字串長度:0)---->張詠淳博士專精於文字探勘與檢索、對談式問答機器人，以及相關的機器學習與語言辨識技術。張博士自2008年就在中央研究院服國防役，其間參與了自然輸入法開發，於2016年擔任博士後研究員，並榮獲中研院博士後傑出研究獎。張博士近年來發表多篇國內外期刊論文以及會議論文，其中包含了頂尖期刊TKDE以及頂尖國際會議論文ACL等。近期的研究重點之一在於結合句構知識表達與機器學習，打造一個具深度理解的智慧型對談機器人。另一個研究重點則是基於過去在文字分類上相關的研究成果，研發一個全自動的文字分類架構，此應用非常廣泛，例如：情感分析、主題偵測、甚至是問答系統等。(字串長度:792)'),(177,'2017-09-02 15:08:11',3,'me_desc---->(字串長度:0)---->- 台灣資料工程協會發起人 - 台灣 Hadoop 使用者社群共同創辦人/傳教士 - Hadoop : The Definitive Guide、Hadoop Operations 兩書譯者(字串長度:157)'),(178,'2017-09-02 15:08:11',4,'me_desc---->(字串長度:0)---->Min Sun is an assistant professor at National Tsing Hua University in Taiwan. Before that, he was a postdoctoral researcher at Washington University in Seattle and he graduated from the University of Michigan with a Ph.D. degree in EE: System. He also won the best paper award of 3dRR in 2007, paper award of CVGIP in 2015, 2016, and 2017, and the winner of Thor challenge in CVPR 2017.(字串長度:386)'),(179,'2017-09-02 15:08:11',5,'me_desc---->(字串長度:0)---->喜愛新鮮的事物，對於資訊科技充滿熱情，外表看起來很活潑，但骨子裡是個阿宅，沒事喜歡窩在家裡觀察其他阿宅都在做什麼，愛家、愛耶穌，目前擔任東吳大學巨量資料管理學院助理教授，勉強算是個Data Watcher，也勉強算是個Data Player，近年來致力於巨量資料探勘以及社群網路分析應用之研究，同時也成立了資料實驗室(Data Lab)，並透過實際的專案項目，培養巨量資料分析人才。(字串長度:507)'),(180,'2017-09-02 15:08:11',6,'me_desc---->(字串長度:0)---->我常覺得我們處於一個很有趣的時代，有機會親身體會到應用領域轉變的好幾個過程：首先是從一般單機型應用轉變成伺服端的架構，另外這幾年慢慢從伺服端架構又轉變成web-based的架構，大家討論的議題又從分散式計算轉成grid computing、web service，big data，以及 cloud computing，這幾年人工智慧又重新受到關注。面對這樣的轉變，我覺得過去我們談論的許多模型，不論是程式開發的模型，或是分散式計算的處理，其實有機會作一些調整，悠遊於新的時代，有很多新東西可以玩，在這樣的時間點做一個研究人員，是很幸福的。(字串長度:694)'),(181,'2017-09-02 15:08:11',7,'me_desc---->(字串長度:0)---->慈濟大人類發展與心理學系專任助理教授 開放科學中心大使(Center for Open Science Ambassador)<br><br>在台灣教了幾年書，2014年受到開放科學的知識論感召，親身到荷蘭體會身體力行之路。希望向華文大眾與學術界傳播分辨好壞科學的心法。(字串長度:307)'),(182,'2017-09-02 15:08:11',8,'me_desc---->(字串長度:0)---->2007年畢業於台灣大學資訊工程研究所，博士論文題目為「蛋白質非穩定區段之預測與特性研究」。之後加入趨勢科技從事雲端平台的建置與相關演算法開發，利用資料探勘與機器學習等技術來主動偵測垃圾郵件、色情網站及惡意軟體。將處理人類病毒相關演算法運用到處理電腦病毒上，跨領域活用演算法來有效處理資料。2015年成立亞大基因，建立一個擁有生物資訊和大數據分析技術雙重優勢的軟體研發團隊，專注致力於發展創新的人類基因序列數據挖掘（Data Mining）技術及分析軟體平台（Analytics Platform），進而幫助臨床醫師有效利用基因體資料提供病人精準的醫療。(字串長度:757)'),(183,'2017-09-02 15:08:11',9,'me_desc---->(字串長度:0)---->美國加州大學爾灣校區 (UC Irvine) 數學博士, 專攻純數學「代數幾何」領域, 但多年來看上 Python 「簡單、強大、可專注在問題而非程式語言本身」, 所以一直致力推廣 Python 程式語言。碩士班時期就研究神經網路, 直到約一年半前才重回深度學習的領域。已在多個場合介紹過深度學習的基本理論和實作方式, 也試著用深度學習的方法解決不同領域的問題。(字串長度:465)'),(184,'2017-09-02 15:08:11',10,'me_desc---->(字串長度:0)---->吳牧恩教授為數理背景出身、資工博士畢業，其研究領域包括資訊理論與金融資料分析。現為台北科技大學資訊與財經管理系助理教授，致力於推廣資金管理的數學與使用 R 語言研發金融交易策略，期望破除市場上大眾追求穩賺聖盃的迷思。其亦為知名財金部落格幣圖誌 (Bituzi) 專欄作家 -- 牧清華。(字串長度:395)'),(185,'2017-09-02 15:08:11',11,'me_desc---->(字串長度:0)---->黃詩淳，台大法學士、日本北海道大學法學碩士博士。主要研究領域為身分法， 近來關心高齡者人權、財產管理與代際財產移轉議題，包括繼承、遺囑、成年監護、信託等。教授課目有身分法、物權、民法概要、日文法學名著選讀等課程。近年開展法資訊學的研究。著有〈遺產繼承之圖像與原理解析〉、〈涉訟榮民遺囑之特徵與法律問題〉等論文，請參見http://homepage.ntu.edu.tw/~schhuang/Welcome.html 邵軒磊，台大法學士、政大法學碩士博士，專長：國際關係政治學。目前對資料蒐集、模型預測、可視化具有興趣。主要使用R(major)、python(minor)，以及SAS、SPSS、matlab等軟體，詳情請參考：http://web.ntnu.edu.tw/~hlshao/。(字串長度:803)'),(186,'2017-09-02 15:08:11',12,'me_desc---->(字串長度:0)---->李祈均目前是國立清華大學電機工程學系助理教授，研究領域橫跨訊號處理與機器學習技術，尤其針對人類行為進行分析、預測及統計建模。他長期著重於運用不同種類的資料檔（文字、聲音、影像等）以研發跨領域，如醫療、教育、心理，相關演算法。他過去擔曾任ID Analytics公司旗下id:a lab的資料科學家，開發分析信用申請函進行金融信用產品的開發。他目前與榮總、國衛院、長庚、台大、國教院等機構合作研究，針對人類行為進行量化建模與客觀辨識。(字串長度:608)'),(187,'2017-09-02 15:08:11',13,'me_desc---->(字串長度:0)---->黃博士於2003自台大醫學系畢業，退伍後前往美國哈佛大學先後取得公共衛生碩士（2006, 主修計量方法）、生物統計碩士（2009）、流行病學及生物統計理學博士雙學位（2012）。研究所畢業後於美國布朗大學流行病學系及生物統計系擔任助理教授（2012-2016），於2016年9月返台於中研院統計所任職。黃博士之研究曾獲得國際生統學會東北美地區Research Advisory Board Poster Award（2012）、美國統計學會David P Byar Young Investigator Travel Award（2012）、布朗大學Richard Salomon Faculty Research Award（2013）及布朗大學Junior Research Award in Genetic Studies（2015）。黃博士研究領域包括：生物統計、因果推論、存活分析、流行病學及基因體學。(字串長度:806)'),(188,'2017-09-02 15:08:11',14,'me_desc---->(字串長度:0)---->喜歡被稱為「教育工作者」，希望能為教育坦然地做一點事。喜歡以參謀的角度思考與思辨，探究問題的本質。除了喜歡到處遊山玩水趴趴走，也喜歡進製造現場「觀察」，相信「魔鬼都藏在製造現場的細節裡」。他的研究興趣是作業研究、製造資料科學、生產經濟學、多目標決策分析。熱衷於以統計方法與最佳化技術改善製造現場的生產效率與品質，相關應用包含產能規劃、生產排程、錯誤診斷、預測保養、設備健康指標、虛擬物料品質檢測等。(字串長度:597)'),(189,'2017-09-02 15:08:11',15,'me_desc---->(字串長度:0)---->喜歡體驗設計、愛好說故事、一星期看七本書(字串長度:60)'),(190,'2017-09-02 15:08:11',16,'me_desc---->(字串長度:0)---->Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:790)'),(191,'2017-09-02 15:08:11',17,'me_desc---->(字串長度:0)---->曾任YAHOO,Trendmicro,Amazon等軟體公司的工程師，後來不務正業開始了資料科學研究之旅，直到現在。而現今，工作卻像是資料科學工作者的打雜者。(字串長度:191)'),(192,'2017-09-02 15:08:11',18,'me_desc---->(字串長度:0)---->鄭旭詠博士為美國華盛頓大學電機博士及國立中央大學資訊工程博士。於2008年開始擔任國立中央大學資訊工程系助理教授，並於2013年升等為副教授，2017年升等為教授。研究領域為電腦視覺、視訊分析、影像處理以及智慧型系統等。學術成果豐碩。主持或參與多項相關研究計畫，對學術研究以及產業界皆有具體之貢獻。曾獲多項論文相關獎項。並於2013年獲得中央大學資訊電機學院101學年度新進研究優良教師，2015, 2016, 2017中央大學研究傑出獎，105年度中華民國系統學會學術傑出青年獎，105年度中國電機工程學會優秀青年電機工程師獎，105年度青年獎章等獎項。 (字串長度:747)'),(193,'2017-09-02 15:08:11',19,'me_desc---->(字串長度:0)---->現為臺大電機系助理教授，他的研究主軸是以機器學習技術讓機器辨識並理解語音訊號的內容。以深度學習技術為基石，他正致力於語音數位內容搜尋、語音數位內容之自動化組織以及從語音數位內容擷取關鍵資訊等前瞻性研究，這些技術有很多的應用，例如：人機互動、問答系統、智慧型線上教學平台等等。他曾在資料科學愛好者協會講授《一日搞懂深度學習》四次，他在臺大開設的機器學習相關課程錄影請見：https://www.youtube.com/channel/UC2ggjtuuWvxrHHHiaDH1dlQ/playlists(字串長度:618)'),(194,'2017-09-02 15:08:11',20,'me_desc---->(字串長度:0)---->彭其捷畢業於交大數據分析實驗室，對於網路服務設計有巨大熱情，曾負責過：資料科學、資料視覺化、前端工程、服務設計等等類型專案，出版過 3 本使用者經驗專書，致力於打造良善的網路服務體驗，近期多投入資料科學相關主題，期待能夠創造更多資料之間的連結，讓數據分析更平民化。(字串長度:390)'),(195,'2017-09-02 15:08:11',21,'me_desc---->(字串長度:0)---->aha於2007年畢業於台大物理所，專長：奈米光學模擬，2009年進入中研院資訊所，主要研究：participantory sensing(參與式感測)，研究如何透過個人裝置感測搜集資料，並整合拼湊完整巨觀環境變化，2013年入行，初任職為商業分析小組，著重於透過資料探索找出商機與企劃方向．2015年成立並加入創新研發小組，主要負責各式新技術評估與導入，同時規劃與評估相關商業模式的落地與執行，參與數位旗艦分行與玉山小i 隨身金融顧問等專案。 aha 專長於組裝各式語言與各大平台服務，打造最小可行產品原型。同時關心社群與社會，如：Govcash 政治獻金研究是透過g0v的政治獻金數位化的成果；2014年為台灣資料科學愛好者年會R 課程講師．現正沈浸於台灣金融法規、具有台灣特色Fintech、AI、blockchain應用中。<br><br>(字串長度:934)'),(196,'2017-09-02 15:08:11',22,'me_desc---->(字串長度:0)---->楊立偉教授研究領域為資料庫及語意分析技術、搜尋引擎、數位行銷，於國立台灣大學工商管理學系暨商學研究所任教超過十年。同時，是台灣規模最大的網路情報與社群口碑自動分析平台OpView及意藍資訊公司的創辦人，也是資訊及通信國家標準技術委員，擁有超過20項語意分析專利，授課生動幽默，兼具理論及實務背景。(字串長度:428)'),(197,'2017-09-02 15:08:11',23,'me_desc---->(字串長度:0)---->溫在弘博士於2009年任職於國立台灣大學地理環境資源學系，並兼任台大人口與性別研究中心、統計碩士學位學程以及傳染病防治研究與教育中心等教研工作。研究興趣是地理過程的數理建模與空間分析，著重於都市環境人口移動導致的傳染病疫情、連續犯罪等的地理擴散現象。近年獲得科技部的學術獎勵，包括：｢優秀年輕學者研究計畫｣（2014-2016）、｢吳大猷先生紀念獎｣<br><br>(2015)以及｢補助大專校院延攬特殊優秀人才措施｣ (2016)等。(字串長度:586)'),(198,'2017-09-02 15:08:11',24,'me_desc---->(字串長度:0)---->劉憲錡 Toby Liu 是由智庫驅動發起之第二期資料英雄計畫參與者之一，中大經濟系畢業後，目前是清大資應用所碩士生，研究主題包含資料探勘、語意分析、情緒分析等。研究之餘，他還是個開放原始碼社群貢獻者，參與社群包括開源人年會、零時政府年會、台灣摩茲社群、國立中央大學網路開源社。 他信仰資料科學，並且希望這個世界能藉由資料科學的力量變得稍微好一點點。 (字串長度:504)'),(199,'2017-09-02 15:08:11',25,'desc---->The approaches in feature extraction can be divided into two categories: model-centric and data-driven. The model-centric approach relies on human heuristics to develop a computer model to extract features from data. These models were engineered by scientists and then validated via empirical studies. A major shortcoming of the model-centric approach is that unusual circumstances that a model does not take into consideration during its design can render the engineered features less effective.<br><br>Contrast to the model-centric approach, which dictates representations independent of data, the data-driven approach learns representations from data.<br><br>Example data-driven algorithms are multilayer perceptron (MLP) and convolutional neural network (CNN), which belong to the general category of neural network and deep learning.<br><br>In this talk I will first explain why my team at Google in 2006 embarked on the data-drive approach.<br><br>In 2008, we funded the ImageNet project at Stanford, and subsequently in 2011 filed two data-driven patents, one on data-driven feature extraction and the other on data-driven object recognition.<br><br>We parallelized five widely used machine learning algorithms including SVMs, PFP, LDA, Spectral Clustering, and CNN, and open-sourced all these algorithms.<br><br>Recently at HTC, we announced DeepQ Open AI platform, which features these scalable algorithms with enhancements.<br><br>In particular, I will explain how we have made configuring a distributed system to run CNN both simple and cost effective.<br><br><br><br>In 2012, the world was convinced that the data-driven approach to be effective, after Alexnet achieved breakthrough accuracy in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition.<br><br>This talk will walk through subsequent enhancements on Alexnet in three perspectives: representation ability, optimization ability, and generalization ability.<br><br>Unfortunately, most applications face the problem of small data. I will share our experiences with transfer learning and GANs, both positive and negative ones. This talk concludes with a list of open research issues. (字串長度:2174)---->The approaches in feature extraction can be divided into two categories: model-centric and data-driven. The model-centric approach relies on human heuristics to develop a computer model to extract features from data. These models were engineered by scientists and then validated via empirical studies. A major shortcoming of the model-centric approach is that unusual circumstances that a model does not take into consideration during its design can render the engineered features less effective.<br><br>Contrast to the model-centric approach, which dictates representations independent of data, the data-driven approach learns representations from data.<br><br>Example data-driven algorithms are multilayer perceptron (MLP) and convolutional neural network (CNN), which belong to the general category of neural network and deep learning.<br><br>In this talk I will first explain why my team at Google in 2006 embarked on the data-drive approach.<br><br>In 2010/11, we funded the ImageNet project at Stanford, and subsequently in 2011 filed two data-driven patents, one on data-driven feature extraction and the other on data-driven object recognition.<br><br>We parallelized five widely used machine learning algorithms including SVMs, PFP, LDA, Spectral Clustering, and CNN, and open-sourced all these algorithms.<br><br>Recently at HTC, we announced DeepQ Open AI platform, which features these scalable algorithms with enhancements.<br><br>In particular, I will explain how we have made configuring a distributed system to run CNN both simple and cost effective.<br><br><br><br>In 2012, the world was convinced that the data-driven approach to be effective, after Alexnet achieved breakthrough accuracy in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition.<br><br>This talk will walk through subsequent enhancements on Alexnet in three perspectives: representation ability, optimization ability, and generalization ability.<br><br>Unfortunately, most applications face the problem of small data. I will share our experiences with transfer learning and GANs, both positive and negative ones. This talk concludes with a list of open research issues.(字串長度:2176)```me_desc---->(字串長度:0)---->Edward Chang currently serves as the President of Research and Healthcare (DeepQ) at HTC. Ed\'s most notable work is co-leading the DeepQ project (with Prof. CK Peng at Harvard), working with a team of physicians, scientists, and engineers to design and develop mobile wireless diagnostic instruments. Such instruments can help consumers make their own reliable health diagnoses anywhere at any time. The project entered the Tricorder XPRIZE competition in 2013 with 310 other entrants and was awarded second place in April 2017 with 1M USD prize. The deep architecture that powers DeepQ is also applied to power Vivepaper, an AR product Ed\'s team launched in 2016 to support immersive augmented reality experiences (for education, training, and entertainment). Prior to his HTC post, Ed was a director of Google Research for 6.5 years, leading research and development in several areas including scalable machine learning, indoor localization, social networking and search integration, and Web search (spam fighting). His contributions in parallel machine learning algorithms and data-driven deep learning (US patents 8798375 and 9547914) are recognized through several keynote invitations and the developed open-source codes have been collectively downloaded over 30,000 times. His work on IMU calibration/fusion (US patents 8362949,<br><br>9135802, 9295027, 9383202, and 9625290) with project X was first deployed via Google Indoor Maps (see XINX paper and ASIST/ACM SIGIR/ICADL keynotes) and is now widely used on mobile phones and VR/AR devices. Ed\'s team also developed the Google Q&amp;A system (codename Confucius), which was launched in over 60 countries. Prior to Google, Ed was a full professor of Electrical Engineering at the University of California, Santa Barbara (UCSB). He joined UCSB in 1999 after receiving his PhD from Stanford University, and was tenured in 2003 and promoted to full professor in 2006. Ed has served on ACM (SIGMOD, KDD, MM, CIKM), VLDB, IEEE, WWW, and SIAM conference program committees, and co-chaired several conferences including MMM, ACM MM, ICDE, and WWW. He is a recipient of the NSF Career Award, IBM Faculty Partnership Award, and Google Innovation Award. He is also an IEEE Fellow for his contributions to scalable machine learning.(字串長度:2279)'),(200,'2017-09-02 15:08:11',26,'me_desc---->(字串長度:0)---->優拓資訊 Data Scientist Lead，負責團隊建制以及機器學習模型的訓練和佈署。平時發一些 PR 到開源的 python 專案，偶爾參加 Kaggle 競賽，最近在追 Reinforcement Learning 的論文(字串長度:220)'),(201,'2017-09-02 15:08:11',27,'me_desc---->(字串長度:0)---->清華大學統計所畢，目前於 KKBOX 研究開發中心 (Research Develop Center) 擔任資料科學家。(字串長度:112)'),(202,'2017-09-02 15:08:11',28,'me_desc---->(字串長度:0)---->吳沛燊，臺大醫院復健科總醫師，均一教育平台 Data Scientist，協助建立data driven的文化，透過各種管道自學以逐步累積實戰經驗。(字串長度:170)'),(203,'2017-09-02 15:08:11',29,'me_desc---->(字串長度:0)---->補(字串長度:3)'),(204,'2017-09-02 15:08:11',30,'me_desc---->(字串長度:0)---->蘇黎於 2012 年畢業於國立台灣大學電信工程學研究所，2012 至 2016 年間為中央研究院音樂與音訊運算實驗室之博士後研究員，2017 年起為中央研究院資訊課學研究所助研究員，專長為訊號處理、機器學習與音樂資訊檢索，所掌握的技術包含自動採譜、音高偵測、拍點追蹤、聲源分離、樂器辨識、自動音樂對譜、音樂互動式系統、音樂與多媒體應用等等。所開發之自動多重音高偵測 系統在 MIREX 競賽中名列史上前五強。自 2014 年起擔任國際音樂資訊檢索會 議 (ISMIR) 之技術會議委員。所研發之自動音樂對譜以及視覺化之系統將於 2017年11月於國家音樂廳展演。(字串長度:737)'),(205,'2017-09-02 15:08:11',31,'me_desc---->(字串長度:0)---->東華應數副教授，今年在台灣<br><br>Python 年會專業課程、TWSIAM 會前工作坊 PyDay、台大黑客松資料科學學程、Taipei.py 、南港高中及花蓮.py Omega Zone 開設資料科學、影像處理的工作坊或課程。 創辦花蓮.py 及實做數學，定期、不定期組織各項活動。(字串長度:322)'),(206,'2017-09-02 15:08:11',32,'me_desc---->(字串長度:0)---->郭博士與鄭勝丰、宋政桓一同創立走著瞧 (Gogolook) 公司，目前擔任 whoscall 開發團隊執行長。曾就讀於成大工業設計學系，畢業於清華大學工業工程研究所，其專精領域為產品設計與使用者經驗研究。(字串長度:262)'),(207,'2017-09-02 15:08:11',33,'me_desc---->(字串長度:0)---->Yu-Shuen Wang is an Associate professor of the Department of Computer Science at National Chiao-Tung University. He received his PhD degree from Visual System Laboratory, National Cheng Kung University, Tainan, Taiwan, ROC, in 2010. His research interests include Computer Graphics, Visual Media Retargeting, Image Manipulation, Focus+Context Visualization and Skeletonization. (字串長度:378)'),(208,'2017-09-02 15:08:11',34,'me_desc---->(字串長度:0)---->張俊鴻醫師具有醫學與資訊工程學位, 為國內少數從事臨床服務並能自行分析醫療資料研究的專科醫師, 近三年七次於國際學會口頭論文報告, 並且獲六項國際學會獎項.(字串長度:220)'),(209,'2017-09-02 15:08:11',35,'me_desc---->(字串長度:0)---->Frederick Kin Hing Phoa, Ph.D: He has been an Associate Research Fellow of the Institute of Statistical Science at Academia Sinica (ISSAS) since 2013. He received his B.S. Physical Chemistry, B.S. Applied Mathematics, M.S. Statistics and Ph.D. Statistics from University of California at Los Angeles (UCLA) in 2001, 2002, 2006 and 2009 respectively. He was an Assistant Research Fellow of the ISSAS in 2009-2013. He conducted the Excellent Young Researcher Research Project supported by Ministry of Science and Technology (MOST) during 2013-2016. He received the Career Development Award from Academia Sinica in 2014. He received the Ta-You Wu Memorial Award (Young Researcher Award) from MOST. He received the best paper award in the World Congress of Engineering in 2015. He received an International Cost-Share Exchange Scheme funded by MOST and Royal Society of UK during 2016-2018. In the past eight years (2009-2017), he has published 44 papers in the areas of design and analysis of experiments, network data analysis, nature-inspired metaheuristic optimization and many others. In addition, he has given 88 invited talks in the international conferences and 59 seminar talks in the universities around the world. He organized two conferences of experimental design and analysis (CEDA) in 2014 and 2016. His international collaborations cover research groups in USA, UK, Canada, Japan, China, Hong Kong, and many other countries.(字串長度:1436)'),(210,'2017-09-02 15:08:11',36,'me_desc---->(字串長度:0)---->陳煥宗是清華大學資工系副教授，研究領域為電腦視覺。(字串長度:75)'),(211,'2017-09-02 15:08:11',37,'me_desc---->(字串長度:0)---->現任中研究民族所助研究員，兼任台大心理系助理教授。研究領域為文化與社會認知、正向心理學、自我與人際動機、正念。研究旨趣聚焦於台灣社會文化之快速變化對人我適應的影響，係從 「自我彰顯之緩解」（可參考「研之有物訪談」http://research.sinica.edu.tw/mindfulness-chang-jen-ho/）及「文化訊息多元性的整合」兩方面切入。(字串長度:431)'),(212,'2017-09-02 15:08:11',38,'me_desc---->(字串長度:0)---->Ann Chen, 台大資管系博士。鑽研的領域為資料探勘、文字探勘及機器學習。先前於台灣雅虎電商部門開發推薦系統及個人化搜尋，現於 KKBOX 負責音樂串流服務個人化體驗的打造(字串長度:230)'),(213,'2017-09-02 15:08:11',39,'me_desc---->(字串長度:0)---->AlphaGo首席工程師。畢業於國立台灣師範大學資訊工程研究所，現在是Google DeepMind的資深研究員。(字串長度:127)'),(214,'2017-09-02 15:08:11',40,'me_desc---->(字串長度:0)---->張源俊畢業於University of Illinois at Urbana/Champaign 統計系，目前是中央研究院統計科學研究所研究員。研究領域包括 Sequential Analysis, Machine Learning, Educational and Psyhcological Testing, Classification, and their applications in Biostatistics or Bio-medicine research.(字串長度:308)'),(215,'2017-09-02 15:08:11',41,'me_desc---->(字串長度:0)---->杜書儒在長庚大學醫學影像暨放射科學系擔任副教授。在美國Indiana 州West Lafayette 的Purdue University拿到物理學博士學位，博士論文是利用蒙地卡羅與平行高速電腦運算的方法研究中子星的能量問題。之後在Texas 州Houston的 The University of Texas MD Anderson Cancer Center 擔任博士後研究員。研究方向與興趣包括近代醫學物理、醫學影像特徵分析、機器學習、電腦斷層掃描、磁共振影像、與放射治療。教授課程包括近代醫學物理專題與特論、放射物理學、放射物理學實驗、應用數學、機器學習、醫學影像特論、與進階近代物理。平常的喜好包括美食、游泳、與爬山。(字串長度:734)'),(216,'2017-09-02 15:08:11',42,'me_desc---->(字串長度:0)---->現任教於台北商業大學商業設計與管理系，其研究興趣專注於發展互聯網與行動App產品、更特別著重在用戶體驗、商業模式、以及營銷增長機制等設計思考研究與實務應用。溫博士過去曾分別服務於美國矽谷Palo Alto研究中心電腦科學實驗室、中央研究院資訊科學研究所，並曾參與四家互聯網新創公司創立與經營。近三年分別赴產官學界分享近70場創業與設計主題相關演講，亦輔導多項學界研發成果參與網路App創業，獲得多項全國性的創業獎項。其研發成果亦代表科技部參展2015年台北國際發明展，擔任開募記者會代表。(字串長度:690)'),(217,'2017-09-02 15:08:11',43,'me_desc---->(字串長度:0)---->kaggle業餘玩家(字串長度:18)'),(218,'2017-09-02 15:08:11',44,'me_desc---->(字串長度:0)---->朱威達教授於 2006 年在台灣大學取得博士學位，2007 年起任職於中正大學資訊工程學系。曾在多媒體頂尖研討會 ACM Multimedia 獲得最佳論文獎，也曾到美國哥倫比亞大學、日本名古屋大學擔任短期訪問學者。研究方向為多媒體內容分析與檢索、電腦視覺、圖形識別、訊號處理等，已發表超過八十篇國際會議與期刊論文。近年來致力於深度學習技術應用於影像美學、異質媒體關聯性分析、漫畫的組成與風格、以及影像風格分析，期盼以更貼近人的感官的角度推展多媒體內容分析研究。(字串長度:639)'),(219,'2017-09-02 15:08:11',45,'me_desc---->(字串長度:0)---->王淳恆，目前於聯發科技擔任技術副理，從事車用晶片開發。15年以上通訊與影像的晶片設計、訊號處理與演算法相關的工作經驗。閒暇之餘使用資料科學進行股價分析，開發相關算法超過10年。(字串長度:256)'),(220,'2017-09-02 15:08:11',46,'me_desc---->(字串長度:0)---->Ed H. Chi is a Research Scientist at Google, leading a team focused on recommendation systems, machine learning, and social interaction research. He has launched significant improvements of recommenders for YouTube, Google Play Store and Google+. With over 35 patents and over 100 research articles, he is known for research on Web and online social systems, and the effects of social signals on user behavior.<br><br> Prior to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center\'s Augmented Social Cognition<br><br>Group, where he led the group in understanding how social systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press, and has won awards for both teaching and research. He has a<br><br>In his spare time, Ed is an avid photographer and snowboarder.(字串長度:1079)'),(221,'2017-09-02 15:08:11',47,'me_desc---->(字串長度:0)---->陳維超是行品股份有限公司 (Skywatch Inc.) 的創辦人，負責雲端視訊監控與 IoT 相關之產品與技術開發。他同時也是台灣大學資訊工程學系的兼任助理教授，主要研究興趣為 GPU 繪圖硬體，電腦繪圖，計算照相學，擴增實境，與電腦視覺等。陳博士畢業於台灣大學電機系、電機研究所、美國北卡羅萊納大學 (UNC-Chapel Hill)，並曾任職於加州矽谷 NVIDIA 擔任系統架構師，與 Nokia Research Palo Alto 擔任資深研究員。(字串長度:532)'),(222,'2017-09-02 15:08:11',48,'me_desc---->(字串長度:0)---->張智傑博士，於2012年取得博士學位，目前任職於富邦創新科技辦公室，主要負責富邦集團大數據/資料科學/人工智慧發展整合及規劃。(字串長度:177)'),(223,'2017-09-02 15:08:11',49,'me_desc---->(字串長度:0)---->（請先延用去年的資料好嗎，謝謝）(字串長度:48)'),(224,'2017-09-02 15:08:11',50,'me_desc---->(字串長度:0)---->世恩目前為 QSearch 技術長，在數據分析與系統開發有很高的熱忱，投入於社群分析已超過5年，與夥伴協助客戶於市場調查、合作夥伴分析、社群危機處理，社群。2017 年以商用技術類獲選為 Forbes 30 Under 30 。(字串長度:272)'),(225,'2017-09-02 15:08:11',51,'me_desc---->(字串長度:0)---->從 2005 年接觸人工智慧到現在，玩過 AI 整合大型 CNC 工具機、自學習行走機器人、個人化推薦系統、資訊安全、開源專案、AI 開放課程、智能穿載式裝置開發、互聯網、電子商務、數據分析、敏捷開發、AI 程式交易、金融數據分析等等，擁有大型跨國產品開發和領導國際團隊經驗，目前成立台灣機器學習和中國手撕深度學習，以培育能落實 AI 技術並貢獻社會的人才種子為休閒目標。(字串長度:508)'),(226,'2017-09-02 15:08:11',52,'me_desc---->(字串長度:0)---->First degree and Master\'s degree in Computer Science, both @ NTHU Taiwan; PhD research on alignment uncertainty and evolutionary analysis of Drosophila odorant receptor genes based on RNAseq @ CRG, Barcelona. Interested in the prediction of protein functional classes; interaction between alignment and evolution; evolutionary analysis based on RNAseq.(字串長度:352)'),(227,'2017-09-02 15:08:11',53,'me_desc---->(字串長度:0)---->郭立言，國家中山科學研究院助理研究員、國立臺灣大學電機所博士生、資料科學家。具備資料收集、整理、分析等實務經驗。熱愛軟體開發與創新，專業致力於資料探勘、深度學習以及推薦系統相關應用。擅長根據專案需求，打造資料分析系統。(字串長度:327)'),(228,'2017-09-02 15:08:11',54,'me_desc---->(字串長度:0)---->軟體架構師，專研後端技術與 DevOps 。於現職領導團隊開發雲端服務基礎架構以及深度學習平台(字串長度:125)'),(229,'2017-09-02 15:08:11',55,'me_desc---->(字串長度:0)---->新技術，新架構，新應用愛好者，在Machine Learning火熱的時代，也得努力從資料中找尋新樂趣、新價值的工程師。(字串長度:148)'),(230,'2017-09-02 15:08:11',56,'me_desc---->(字串長度:0)---->Wush 是有四年經驗的資料科學家、R語言套件的開發者，長年致力於R語言的推廣。 Wush曾參與許多世界級R語言套件的開發，如digest、knitr、swirlify與FeatureHashing等等，對R語言有深入的理解( https://github.com/wush978 )。Wush也曾在台灣經營R社群與推廣R語言，是Taiwan R User Group ( https://www.meetup.com/taiwan-R/ ) 的共同創辦人與ptt R_Language版 ( https://www.ptt.cc/bbs/R_Language/index.html )的創板板主。 在推廣R上，Wush與社群一同撰寫了一套免費開放的R語言的基礎、資料工程到資料視覺化的教材： http://datascienceandr.org 。(字串長度:649)'),(231,'2017-09-02 15:08:11',57,'me_desc---->(字串長度:0)---->台大土木所博士，畢業即創業，成立「創代科技股份有限公司」出任執行長，用資料科學專業，改變了過去交通領域中，偏向硬體投資的傳統思維。短短三年內，創造了許多創新且極具重大影響的代表作，亦擔任數據顧問/教授，包含：中國北京運輸研究院、重慶交通科學研究院、世界ITS大會、台灣大學、淡江大學、臺北捷運公司等。(字串長度:442)'),(232,'2017-09-02 15:08:11',58,'me_desc---->(字串長度:0)---->陳煥宗是清華大學資工系副教授，研究領域為電腦視覺。(字串長度:75)'),(233,'2017-09-02 15:08:11',59,'me_desc---->(字串長度:0)---->覺得有趣選了物理系，結果差點畢不了業。想做遊戲所以開始寫程式，結果沒進過遊戲公司。覺得電腦圖學好玩，結果接觸了電腦視覺。覺得機器學習很酷，結果待續。現在在 KKStream 披著 Android Adaptive Player 的皮，企圖跟同事強迫推銷自己都不太懂的演算法。(字串長度:341)'),(234,'2017-09-02 15:08:11',60,'me_desc---->(字串長度:0)---->張如瑩，成功大學資訊工程所博士，大學與碩士期間主修資訊管理，擁有MBA學歷。現為工業技術研究巨資中心成員，先前曾任職於中央研究院和一醫療資訊處理公司。在自然語言處理、文本探勘與分析、資訊檢索以及機器學習領域，累積近20年相當紮實且深厚的實作經驗及理論基礎，並擁有巨量資料處理和雲端計算的實作經驗，歷年作品曾榮獲--中華民國資訊月、教育部、僑委會、蕃薯藤等政府機關與商業團體的獎勵。除技術研發外，也擁有專案統籌規劃與管理、系統分析與設計、技術推廣及教育訓練等專業經驗。學術上，累積發表過三十餘篇論文，擁有專利，亦曾擔任過大學兼任講師、受邀擔任僑委會海外巡迴講師、自然語言與語音處理研討會Program Committee以及SCIE期刊的reviewer。(字串長度:909)'),(235,'2017-09-02 15:08:11',61,'me_desc---->(字串長度:0)---->林宭宇(Cliff Chiung Yu Lin)曾在Stanford研究人工智慧。畢業後首先任職於Google (2011-2015)，優化學術論文搜索系統，處理搜索系統中各式資料科學問題。之後於2015年加入在矽谷的早期新創團隊MetaMind，為客戶提供深度學習(Deep Learning)解決方案及開發所需之機器學習系統。於2016年被Salesforce併購後擔任人工智慧技術主管，建構可靠且可擴展的雲端平台，支援各式企業客戶的機器學習需求。目前在台灣創業中，專注於開發製造業所需之人工智慧系統。 (字串長度:584)'),(236,'2017-09-02 15:08:11',62,'me_desc---->(字串長度:0)---->Charles Chang is a Machine Learning Researcher at Trend Micro in the Core Technology Department. Charles has abundant experiences in using machine learning with computer go games and malware classification. He is now leading the Machine Learning Research/Operation team in XGen Endpoint Security product of Trend Micro.(字串長度:319)'),(237,'2017-09-02 15:08:11',63,'me_desc---->(字串長度:0)---->Coming soon(字串長度:11)'),(238,'2017-09-02 15:08:11',64,'me_desc---->(字串長度:0)---->林守德教授擁有台灣大學電機工程學士、密西根大學電機碩士、南加州大學計算語言學碩士學位和計算機科學博士學位，目前為國立臺灣大學資訊工程學系的教授，他創立台大的機器發明與社群網路探勘實驗室。在加入台大之前，他是羅沙拉摩斯國家實驗室的博士後研究員。林教授的研究包括機器學習和資料探勘、社群網絡分析和自然語言處理等領域。他的國際學術成就包括 2003 年 IEEE 網路智能會議最佳論文獎、2007 年 Google 研究獎、三度獲得微軟研究獎、IBM 研究獎助，2010 年及 2014 TAAI 優秀論文獎、2011 年 ASONAM 最佳論文獎，以及連續 5 年獲得美國空軍 AFOSR / AOARD 研究獎。他亦是 ACM KDD Cup 的 All Time Leader，領導或共同領導台大團隊贏得 5 次冠軍，今年也帶領研究團隊贏得 2016 年 WSDM Cup。他也曾獲得科技部吳大猷先生紀念獎，傑出人才基金會年輕學者研究獎，曾擔任 SIGKDD 的 senior program chair 及 ACL 的 area chair，目前是國際社群網絡探勘期刊、資訊科學與工程期刊和中文計算語言學期刊的副主編，也是科學人的「網路不打烊」單元作家之一。(字串長度:1252)'),(239,'2017-09-02 15:08:11',65,'me_desc---->(字串長度:0)----> Wen-Chih Peng received the BS and MS degrees from the National Chiao Tung University, Taiwan, in 1995 and 1997, respectively, and the Ph.D. degree in Electrical Engineering from the National Taiwan University, Taiwan, R.O.C in 2001. Currently, he is a professor at the department of Computer Science, National Chiao Tung University, Taiwan. Prior to joining the department of Computer Science and Information Engineering, National Chiao Tung University, he was mainly involved in the projects related to mobile computing, data broadcasting and network data management. Dr. Peng published some papers in several prestigious conferences, such as IEEE International Conference on Data Engineering (ICDE), IEEE International Conference on Data Mining (ICDM) and ACM Conference on Information and Knowledge Management (ACM CIKM) and prestigious journals (e.g., IEEE TKDE, IEEE TMC, IEEE TPDS). Dr. Peng has the best paper award in ACM Workshop on location-based social network 2009 and the best student paper award in IEEE International Conference on Mobile Data Management 2011. His research interests include mobile computing, network data management and data mining. He is a member of IEEE.(字串長度:1189)'),(240,'2017-09-02 15:08:11',66,'me_desc---->(字串長度:0)---->郭耀仁畢業於臺灣大學商學研究所，是資料科學與推廣教育的愛好者，目前是新創團隊 Kyosei.ai 的共同創辦人之一，在臺大資工系統訓練班開設多門 R 語言與 Python 的資料科學相關課程，亦與企業合作提供客製化的內訓課程。在 Kyosei.ai 之前任職過上海的新創團隊、台北的軟體公司與銀行，閒暇時喜歡長跑與乒乓球；著有 R 語言使用者的 Python 學習筆記系列文，是 2017 iT 邦幫忙鐵人賽 Big Data 組冠軍，著有輕鬆學習 R 語言一書，由碁峰出版社出版。(字串長度:591)'),(241,'2017-09-02 15:08:11',67,'me_desc---->(字串長度:0)---->林煜軒博士為國內少數兼具臨床、企業界資歷之精神科醫師。<br><br>近年投入網路心理學等多項跨領域研究：譯作《網路成癮：評估及治療指引手冊》榮獲國健署「優良健康讀物推介獎」、編製「智慧型手機成癮量表」，獲得各國學者要求授權翻譯為德、法、義大利、匈牙利、芬蘭、葡萄牙、土耳其、印度等多國語言版本的高度肯定。致力研發雲端服務的手機程式 (App)，獲得多項國內外專利。受邀編寫國際知名網路成癮教科書中「智慧型手機成癮」章節。現為衛生福利部「本國兒童青少年健康政策白皮書—3C產品使用」召集人。<br><br> 林醫師經常於校園、社區、職場演講，積極建立產學合作平台，深耕社區健康促進，並協助研擬相關政策；推廣近年風行各大跨國企業的「員工協助計畫」(Employee Assistant Program)。(字串長度:947)'),(242,'2017-09-02 15:08:11',68,'me_desc---->(字串長度:0)---->在軟體界打滾，初探深度學習領域的迷途小書僮(字串長度:63)'),(243,'2017-09-02 15:08:11',69,'me_desc---->(字串長度:0)---->現任均一教育平台「軟體團隊活化者」的角色，非常高興有機會加入均一的團隊，均一非常看重自己的文化：「誠信正直、實事求是、積極主動、以終為始 . . .」，建構在文化的基礎上，我們致力於用證據、論述和辯證找出團隊的方向和做事的方法。<br><br>我非常相信善用「資料」，我們可以更認清「事實」，而認清事實可以幫助我們做更對的決定，最終有更大的機會幫助團隊解決困難的問題。(字串長度:524)'),(244,'2017-09-02 15:08:11',70,'me_desc---->(字串長度:0)---->NingChen擅長網站、產業分析及社群經營，並相信資料可以讓世界變得不一樣。她嘗試將時間及精力花在創造一個對於初學者及專家都友善的協作環境。 曾經是雅虎媒體數據分析師，此外，做過16年選舉輿情分析，做不同溫層議題發酵觀察；此外，參與環保署開放資料污染監測系統，NGO捐款行為分析。(字串長度:395)'),(245,'2017-09-02 15:08:11',71,'me_desc---->(字串長度:0)---->洪瑞鴻副教授 現任國立交通大學資訊工程系副教授，曾任美國麻州醫學大學附設醫院之博士後研究員、交通大學生物科技學系以及生物資訊所副教授。專長為次世代定序的高速演算法，以及發展機器學習技術應用於基因體生物資訊的分析。(字串長度:316)'),(246,'2017-09-02 15:08:11',72,'me_desc---->(字串長度:0)---->我希望能將資料科學與人工智慧的工具，應用在解決是常生活的問題上。(字串長度:96)'),(247,'2017-09-02 15:08:11',73,'me_desc---->(字串長度:0)---->加州大學柏克萊分校經濟學博士，現為香港中文大學經濟系講師及計算設施主任，負責學系的高性能計算機集群及計算機實驗室。(字串長度:171)'),(248,'2017-09-02 15:08:11',74,'me_desc---->(字串長度:0)---->Jiazheng Zhou is a senior engineer at Computational Intelligence Technology Center, Industrial Technology Research Institute. Prior to joining ITRI in 2015, he was a postdoctoral research fellow in Department of Computer Science at National Tsing Hua University, where he received his Ph.D. and M.S. degrees in 2011 and 2004, respectively. His research interests include machine learning, cloud computing, high performance computing, cluster computing, and interconnection networks.(字串長度:482)'),(249,'2017-09-02 15:08:11',75,'me_desc---->(字串長度:0)---->陳宜欣，美國南加州大學資訊科學博士，目前在清華大學資訊工程系任教， 自大學與碩士時期在管理學院的薰陶，始終相信：好的科技技術應該蘊涵著 人文關懷。帶領來自世界各地的研究團隊成員，致力運用資料分析技術來找 出大數據背後的群眾智慧，最近將焦點放在跨語系情緒與心理分析技術、以及將資料科學技術應用在音樂治療上。研究之餘，協助實驗教育的照海華德福建校，並執筆人氣部落格『 教書匠的夢 想 』。(字串長度:564)'),(250,'2017-09-02 15:08:11',76,'me_desc---->(字串長度:0)---->任職於成功大學心理學系，專長為心理學中的量化方法與模型，喜歡將統計應用於日常生活與公共領域，深信解讀統計資訊將成為國民的基本素養。開設有「日常生活中的統計」課程，並與學生一起維護與課程同名的臉書專頁。著有「R在行為科學的應用」以及「給論文寫作者的統計指南：傻瓜也會跑統計」。(字串長度:409)'),(251,'2017-09-02 15:08:11',77,'me_desc---->(字串長度:0)---->後補。(字串長度:9)'),(252,'2017-09-02 15:08:11',78,'me_desc---->(字串長度:0)---->臺灣大學心理學系黃從仁助理教授畢業於臺灣大學數學系、臺灣大學物理學研究所、波士頓大學認知與神經系統研究所。他的研究專長為應用類神經網路等數理模型來研究人類認知行為。目前他的「模型建構與資訊學實驗室」致力於使用資訊科技來進行心理學與神經科學的巨量資料研究，在議題上聚焦於社會認知/學習/記憶。研究之外，他曾擔任科羅拉多大學認知科學研究所以及臺灣大學身體心靈文化整合影像研究中心的資訊系統架構師兼管理員。(字串長度:596)'),(253,'2017-09-02 15:08:11',79,'company---->中央研究院 / 資訊科學所(字串長度:33)---->中央研究院資訊科學研究所(字串長度:36)```me_desc---->(字串長度:0)---->台大心理所畢，現為中研院資料洞察實驗室資料科學家。大學及研究所期間在行為科學與神經科學的灰色世界遊走，擅長實驗設計與統計分析以及功能性磁振造影分析與建模。在發現現實世界滿出來的資料就是最大的實驗室及遊樂場後，一腳踩進這巨大的社會實驗室裡，期盼某天能透過龐大的社會實驗與資料找到讓社會更美好的方法。 曾協助電子票證交易公司、知名交友 APP 及網路書商進行資料分析與建立預測性模型，現則走進人工智慧領域, 以深度學習協助製造業，如材料、電子與紡織業做到更佳的品質監測與管理。 (字串長度:693)'),(254,'2017-09-02 15:08:11',80,'company---->台大資訊系(字串長度:15)---->台灣大學資訊工程系(字串長度:27)```me_desc---->(字串長度:0)---->Yung-Yu Chuang is a professor in the Department of Computer Science and Information Engineering at National Taiwan University. He received his B.S. and M.S. from National Taiwan University in 1993 and 1995 respectively, Ph.D. from the University of Washington in 2004, all in Computer Science. His research interests include computer graphics and computer vision, more specifically computation photography, stereoscopic media processing and rendering. (字串長度:452)'),(255,'2017-09-03 14:47:04',1,'desc---->Advanced computing and imaging technologies enable scientists to study natural phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Internet and mobile device users is expected to be even greater.<br><br>To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis methods. Visualization transforms large quantities of, often multiple-dimensional, data into graphical representations that exploit the high-bandwidth channel of the human visual system, leveraging the brain\'s remarkable ability to detect patterns and draw inferences.<br><br>It has been shown very effective in understanding large, complex data, and thus become an indispensable tool for many areas of research and practice. I will present several use cases of visualization based on new concepts and techniques that my group at UC Davis has introduced to further advance the visualization technology as a powerful discovery and communication tool.(字串長度:1125)---->ㄠ(字串長度:3)'),(256,'2017-09-03 14:48:58',1,'desc---->ㄠ(字串長度:3)---->Advanced computing and imaging technologies enable scientists to study natural phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Internet and mobile device users is expected to be even greater.<br><br>To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis methods. Visualization transforms large quantities of, often multiple-dimensional, data into graphical representations that exploit the high-bandwidth channel of the human visual system, leveraging the brain\'s remarkable ability to detect patterns and draw inferences.<br><br>It has been shown very effective in understanding large, complex data, and thus become an indispensable tool for many areas of research and practice. I will present several use cases of visualization based on new concepts and techniques that my group at UC Davis has introduced to further advance the visualization technology as a powerful discovery and communication tool.(字串長度:1125)'),(257,'2017-09-04 07:29:15',1,'me_desc---->Kwan-Liu Ma (馬匡六) is a professor of computer science and the chair of the Graduate Group in Computer Science (GGCS) at the University of California-Davis, where he directs VIDI Labs and UC Davis Center of Excellence for Visualization. He is also a visiting professor of the National Taiwan University and a chair professor of the National Chiao Tung University. His research spans the fields of visualization, computer graphics, high-performance computing, and user interface design. Professor Ma received his PhD in computer science from the University of Utah in 1993. During 1993-1999, he was with ICASE/NASA Langley Research Center as a research scientist. He joined UC Davis in 1999.<br><br>Professor Ma is presently leading a team of 25 researchers pursuing projects driven by a wide range of real-world applications.<br><br>He received the NSF Presidential Early-Career Research Award (PECASE) in 2000, was elected an IEEE Fellow in 2012, and received the 2013 IEEE VGTC Visualization Technical Achievement Award for his outstanding research work. Professor Ma has been actively serving the research community by playing leading roles in many<br><br>professional activities in the area of visualization. He presently serves as the Associate Editor-in-Chief of the<br><br>IEEE Computer Graphics and Applications (CG&amp;A), and on the editorial board of the Journal of Visualization, the Journal of Visual Informatics, and the Journal of Computational Visual Media.(字串長度:1477)---->馬匡六，現任美國加州大學戴維斯分校電腦工程系教授兼研究所主任，並領導一個25名研究員的團隊 (VIDI Labs)。 馬教授于 1993 年在美國猶他大學獲得電腦科學博士學位後，加入美國太空總署 Langley 研究中心擔任研究員，1999 年成為加州大學副教授。馬教授的研究興趣廣泛，主要在可視化，高性能計算，交互式技術之開發，整合及應用。<br><br>因為他在研究方面的傑出貢獻，于 2000 年獲得美國總統青年科學家獎 (PECASE)，2012 年獲選美國電子電機工程師協會院士，又于 2013 年獲頒可視化研究領域最高榮譽的計術成就獎。 他目前是台大資工的客座教授，交大資工的榮譽教授，並参于中研院的研究計劃。(字串長度:793)'),(258,'2017-09-04 07:39:38',1,'me_desc---->馬匡六，現任美國加州大學戴維斯分校電腦工程系教授兼研究所主任，並領導一個25名研究員的團隊 (VIDI Labs)。 馬教授于 1993 年在美國猶他大學獲得電腦科學博士學位後，加入美國太空總署 Langley 研究中心擔任研究員，1999 年成為加州大學副教授。馬教授的研究興趣廣泛，主要在可視化，高性能計算，交互式技術之開發，整合及應用。<br><br>因為他在研究方面的傑出貢獻，于 2000 年獲得美國總統青年科學家獎 (PECASE)，2012 年獲選美國電子電機工程師協會院士，又于 2013 年獲頒可視化研究領域最高榮譽的計術成就獎。 他目前是台大資工的客座教授，交大資工的榮譽教授，並参于中研院的研究計劃。(字串長度:793)---->馬匡六，現任美國加州大學戴維斯分校電腦工程系教授兼研究所主任，並領導一個25名研究員的團隊 (VIDI Labs)。 馬教授於 1993 年在美國猶他大學獲得電腦科學博士學位後，加入美國太空總署 Langley 研究中心擔任研究員，1999 年成為加州大學副教授。馬教授的研究興趣廣泛，主要在可視化，高性能計算，交互式技術之開發，整合及應用。<br><br>他因為在研究方面的傑出貢獻，於 2000 年獲得美國總統青年科學家獎 (PECASE)，2012 年獲選美國電子電機工程師協會院士，又於2013 年獲頒可視化研究領域最高榮譽的計術成就獎。 他目前是台大資工的客座教授，交大資工的榮譽教授，並参與中研院的研究計劃。(字串長度:792)'),(259,'2017-09-04 09:56:38',6,'position---->助理教授/軟體工坊主持人(字串長度:34)---->助理教授(字串長度:12)'),(260,'2017-09-04 09:56:39',16,'position---->associate professor(字串長度:19)---->Associate Professor(字串長度:19)'),(261,'2017-09-04 09:56:39',21,'name2---->林鉦育(aha)(字串長度:14)---->林鉦育 (aha)(字串長度:15)'),(262,'2017-09-04 09:56:39',28,'position---->醫師/資料科學家(字串長度:22)---->醫師(字串長度:6)'),(263,'2017-09-04 09:56:39',36,'position---->老師(字串長度:6)---->教授(字串長度:6)'),(264,'2017-09-04 09:56:39',38,'position---->machine learning researcher (字串長度:28)---->Machine Learning Researcher (字串長度:28)'),(265,'2017-09-04 09:56:39',43,'position---->kaggler(字串長度:7)---->Kaggler(字串長度:7)'),(266,'2017-09-04 09:56:39',49,'position---->教授 / smilepoll.tw 平台主持人(字串長度:37)---->教授 / Smilepoll.tw 平台主持人(字串長度:37)'),(267,'2017-09-04 09:56:39',52,'position---->助教教授(字串長度:12)---->助理教授(字串長度:12)'),(268,'2017-09-04 09:56:39',65,'position---->教授/多媒體研究所 所長(字串長度:32)---->教授暨多媒體研究所所長(字串長度:33)'),(269,'2017-09-04 09:56:39',67,'position---->助研究員級主治醫師(字串長度:27)---->主治醫師(字串長度:12)'),(270,'2017-09-04 09:56:39',72,'position---->(留白)(字串長度:8)---->Peter Yeh(字串長度:9)'),(271,'2017-09-04 09:56:39',73,'position---->講師 - 計算設施主任(字串長度:27)---->講師暨計算設施主任(字串長度:27)'),(272,'2017-09-04 09:56:39',75,'position---->不顯示(字串長度:9)---->副教授(字串長度:9)'),(273,'2017-09-04 09:56:39',81,'company---->ASAP省錢購物網，Uitox全球電商集團(字串長度:45)---->ASAP 省錢購物網，Uitox全球電商集團(字串長度:46)'),(274,'2017-09-04 09:56:39',87,'name2---->可以(字串長度:6)---->(字串長度:0)```position---->可以(字串長度:6)---->伴讀書僮(字串長度:12)'),(275,'2017-09-05 12:13:42',2,'company---->台北醫學大學大數據科技與管理研究所(字串長度:51)---->台北醫學大學大數據科管所(字串長度:36)'),(276,'2017-09-05 12:13:42',25,'conf---->AIK(字串長度:3)---->DSK(字串長度:3)'),(277,'2017-09-05 12:13:42',67,'company---->國家衛生研究院群體健康科學研究所(字串長度:48)---->國衛生院群體健康科學研究所(字串長度:39)'),(278,'2017-09-05 12:13:42',74,'company---->工業技術研究院巨量資訊科技中心(字串長度:45)---->工研院巨量資訊科技中心(字串長度:33)'),(279,'2017-09-05 12:13:42',81,'company---->ASAP 省錢購物網，Uitox全球電商集團(字串長度:46)---->ASAP 省錢購物網(字串長度:20)'),(280,'2017-09-05 12:13:42',86,'talk_title---->Taiwan\'s Opportunities in the AI-First World (字串長度:45)---->Taiwan’s Opportunities in the AI-First World (字串長度:47)```desc---->We all have witnessed the fast growth of mobile internet applications and their impact to our daily life. The development of the ICT industry continues to move quickly. We are seeing new dynamics in IoT and AI, such as smart home, smart car, e-health and robotics. Very likely these developments will create a new paradigm shift and lead to another industry revolution and change our life into a new era. AI and IoT technology will be widespread. There will be plenty of opportunities for Taiwan\'s industry and academia to develop and take on. In this talk, I will attempt to explore several topics/directions that Taiwan can leverage and explore. (字串長度:648)---->We all have witnessed the fast growth of mobile internet applications and their impact to our daily life. The development of the ICT industry continues to move quickly. We are seeing new dynamics in IoT and AI, such as smart home, smart car, e-health and robotics. Very likely these developments will create a new paradigm shift and lead to another industry revolution and change our life into a new era. AI and IoT technology will be widespread. There will be plenty of opportunities for Taiwan’s industry and academia to develop and take on. In this talk, I will attempt to explore several topics/directions that Taiwan can leverage and explore. (字串長度:650)```conf---->AIK(字串長度:3)---->DSK(字串長度:3)'),(281,'2017-09-05 12:13:42',88,'company---->中央研究院/統計科學研究所(字串長度:37)---->中央研究院統計科學研究所(字串長度:36)'),(282,'2017-09-05 12:13:42',90,'company---->台大經濟系(字串長度:15)---->台灣大學經濟系(字串長度:21)'),(283,'2017-09-05 12:13:42',91,'company---->微軟(字串長度:6)---->台灣微軟(字串長度:12)'),(284,'2017-09-05 12:13:42',92,'company---->台灣大學/資訊工程系(字串長度:28)---->台灣大學資訊工程系(字串長度:27)'),(285,'2017-09-06 09:04:42',44,'company---->中正大學(字串長度:12)---->中正大學資訊工程系(字串長度:27)'),(286,'2017-09-06 09:04:42',64,'company---->台大資工系(字串長度:15)---->台灣大學資工系(字串長度:21)'),(287,'2017-09-06 09:04:42',67,'company---->國衛生院群體健康科學研究所(字串長度:39)---->國衛院群體健康科學研究所(字串長度:36)'),(288,'2017-09-06 09:04:42',83,'company---->國立台灣大學資訊工程學系(字串長度:36)---->台灣大學資訊工程學系(字串長度:30)'),(289,'2017-09-06 09:04:42',89,'company---->國立臺灣大學資訊管理學系(字串長度:36)---->台灣大學資訊管理學系(字串長度:30)'),(290,'2017-09-06 09:04:42',93,'company---->國立雲林科技大學資訊工程系(字串長度:39)---->雲林科技大學資訊工程系(字串長度:33)'),(291,'2017-09-06 09:04:42',94,'company---->中央研究院(字串長度:15)---->中央研究院資訊科學研究所(字串長度:36)'),(292,'2017-09-06 09:04:42',95,'company---->國立交通大學資訊工程系(字串長度:33)---->交通大學資訊工程系(字串長度:27)'),(293,'2017-09-06 09:04:42',97,'company---->國立交通大學 運輸與物流管理學系(字串長度:46)---->交通大學運輸與物流管理學系(字串長度:39)'),(294,'2017-09-06 09:04:42',98,'company---->Cloudeep（雲深創新）(字串長度:26)---->Cloudeep (雲深創新)(字串長度:23)'),(295,'2017-09-07 06:23:47',1,'me_desc---->馬匡六，現任美國加州大學戴維斯分校電腦工程系教授兼研究所主任，並領導一個25名研究員的團隊 (VIDI Labs)。 馬教授於 1993 年在美國猶他大學獲得電腦科學博士學位後，加入美國太空總署 Langley 研究中心擔任研究員，1999 年成為加州大學副教授。馬教授的研究興趣廣泛，主要在可視化，高性能計算，交互式技術之開發，整合及應用。<br><br>他因為在研究方面的傑出貢獻，於 2000 年獲得美國總統青年科學家獎 (PECASE)，2012 年獲選美國電子電機工程師協會院士，又於2013 年獲頒可視化研究領域最高榮譽的計術成就獎。 他目前是台大資工的客座教授，交大資工的榮譽教授，並参與中研院的研究計劃。(字串長度:792)---->Kwan-Liu Ma (馬匡六) is a professor of computer science and the chair of the Graduate Group in Computer Science (GGCS) at the University of California-Davis, where he directs VIDI Labs and UC Davis Center of Excellence for Visualization. He is also a visiting professor of the National Taiwan University and a chair professor of the National Chiao Tung University. His research spans the fields of visualization, computer graphics, high-performance computing, and user interface design. Professor Ma received his PhD in computer science from the University of Utah in 1993. During 1993-1999, he was with ICASE/NASA Langley Research Center as a research scientist. He joined UC Davis in 1999.<br><br>Professor Ma is presently leading a team of 25 researchers pursuing projects driven by a wide range of real-world applications.<br><br>He received the NSF Presidential Early-Career Research Award (PECASE) in 2000, was elected an IEEE Fellow in 2012, and received the 2013 IEEE VGTC Visualization Technical Achievement Award for his outstanding research work. Professor Ma has been actively serving the research community by playing leading roles in many<br><br>professional activities in the area of visualization. He presently serves as the Associate Editor-in-Chief of the<br><br>IEEE Computer Graphics and Applications (CG&amp;A), and on the editorial board of the Journal of Visualization, the Journal of Visual Informatics, and the Journal of Computational Visual Media.(字串長度:1477)'),(296,'2017-09-07 06:23:48',25,'name---->張智威(字串長度:9)---->張智威 (Edward Y. Chang)(字串長度:27)```desc---->The approaches in feature extraction can be divided into two categories: model-centric and data-driven. The model-centric approach relies on human heuristics to develop a computer model to extract features from data. These models were engineered by scientists and then validated via empirical studies. A major shortcoming of the model-centric approach is that unusual circumstances that a model does not take into consideration during its design can render the engineered features less effective.<br><br>Contrast to the model-centric approach, which dictates representations independent of data, the data-driven approach learns representations from data.<br><br>Example data-driven algorithms are multilayer perceptron (MLP) and convolutional neural network (CNN), which belong to the general category of neural network and deep learning.<br><br>In this talk I will first explain why my team at Google in 2006 embarked on the data-drive approach.<br><br>In 2010/11, we funded the ImageNet project at Stanford, and subsequently in 2011 filed two data-driven patents, one on data-driven feature extraction and the other on data-driven object recognition.<br><br>We parallelized five widely used machine learning algorithms including SVMs, PFP, LDA, Spectral Clustering, and CNN, and open-sourced all these algorithms.<br><br>Recently at HTC, we announced DeepQ Open AI platform, which features these scalable algorithms with enhancements.<br><br>In particular, I will explain how we have made configuring a distributed system to run CNN both simple and cost effective.<br><br><br><br>In 2012, the world was convinced that the data-driven approach to be effective, after Alexnet achieved breakthrough accuracy in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition.<br><br>This talk will walk through subsequent enhancements on Alexnet in three perspectives: representation ability, optimization ability, and generalization ability.<br><br>Unfortunately, most applications face the problem of small data. I will share our experiences with transfer learning and GANs, both positive and negative ones. This talk concludes with a list of open research issues.(字串長度:2176)---->The approaches in feature extraction can be divided into two categories: model-centric and data-driven. The model-centric approach relies on human heuristics to develop a computer model to extract features from data. These models were engineered by scientists and then validated via empirical studies. A major shortcoming of the model-centric approach is that unusual circumstances that a model does not take into consideration during its design can render the engineered features less effective.<br><br>Contrast to the model-centric approach, which dictates representations independent of data, the data-driven approach learns representations from data.<br><br>Example data-driven algorithms are multilayer perceptron (MLP) and convolutional neural network (CNN), which belong to the general category of neural network and deep learning.<br><br>In this talk I will first explain why my team at Google in 2006 embarked on the data-drive approach.<br><br>In 2008, we funded the ImageNet project at Stanford, and subsequently in 2011 filed two data-driven patents, one on data-driven feature extraction and the other on data-driven object recognition.<br><br>We parallelized five widely used machine learning algorithms including SVMs, PFP, LDA, Spectral Clustering, and CNN, and open-sourced all these algorithms.<br><br>Recently at HTC, we announced DeepQ Open AI platform, which features these scalable algorithms with enhancements.<br><br>In particular, I will explain how we have made configuring a distributed system to run CNN both simple and cost effective.<br><br><br><br>In 2012, the world was convinced that the data-driven approach to be effective, after Alexnet achieved breakthrough accuracy in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition.<br><br>This talk will walk through subsequent enhancements on Alexnet in three perspectives: representation ability, optimization ability, and generalization ability.<br><br>Unfortunately, most applications face the problem of small data. I will share our experiences with transfer learning and GANs, both positive and negative ones. This talk concludes with a list of open research issues. (字串長度:2174)```me_desc---->Edward Chang currently serves as the President of Research and Healthcare (DeepQ) at HTC. Ed\'s most notable work is co-leading the DeepQ project (with Prof. CK Peng at Harvard), working with a team of physicians, scientists, and engineers to design and develop mobile wireless diagnostic instruments. Such instruments can help consumers make their own reliable health diagnoses anywhere at any time. The project entered the Tricorder XPRIZE competition in 2013 with 310 other entrants and was awarded second place in April 2017 with 1M USD prize. The deep architecture that powers DeepQ is also applied to power Vivepaper, an AR product Ed\'s team launched in 2016 to support immersive augmented reality experiences (for education, training, and entertainment). Prior to his HTC post, Ed was a director of Google Research for 6.5 years, leading research and development in several areas including scalable machine learning, indoor localization, social networking and search integration, and Web search (spam fighting). His contributions in parallel machine learning algorithms and data-driven deep learning (US patents 8798375 and 9547914) are recognized through several keynote invitations and the developed open-source codes have been collectively downloaded over 30,000 times. His work on IMU calibration/fusion (US patents 8362949,<br><br>9135802, 9295027, 9383202, and 9625290) with project X was first deployed via Google Indoor Maps (see XINX paper and ASIST/ACM SIGIR/ICADL keynotes) and is now widely used on mobile phones and VR/AR devices. Ed\'s team also developed the Google Q&amp;A system (codename Confucius), which was launched in over 60 countries. Prior to Google, Ed was a full professor of Electrical Engineering at the University of California, Santa Barbara (UCSB). He joined UCSB in 1999 after receiving his PhD from Stanford University, and was tenured in 2003 and promoted to full professor in 2006. Ed has served on ACM (SIGMOD, KDD, MM, CIKM), VLDB, IEEE, WWW, and SIAM conference program committees, and co-chaired several conferences including MMM, ACM MM, ICDE, and WWW. He is a recipient of the NSF Career Award, IBM Faculty Partnership Award, and Google Innovation Award. He is also an IEEE Fellow for his contributions to scalable machine learning.(字串長度:2279)---->Edward Chang currently serves as the President of Research and Healthcare (DeepQ) at HTC. Ed\'s most notable work is co-leading the DeepQ project (with Prof. CK Peng at Harvard), working with a team of physicians, scientists, and engineers to design and develop mobile wireless diagnostic instruments. Such instruments can help consumers make their own reliable health diagnoses anywhere at any time. The project entered the Tricorder XPRIZE competition in 2013 with 310 other entrants and was awarded second place in April 2017 with 1M USD prize. The deep architecture that powers DeepQ is also applied to power Vivepaper, an AR product Ed\'s team launched in 2016 to support immersive augmented reality experiences.<br><br>Prior to his HTC post, Ed was a director of Google Research for 6.5 years, leading research and development in several areas including scalable machine learning, indoor localization, social networking and search integration, and Web search (spam fighting). His contributions in parallel machine learning algorithms and data-driven deep learning (US patents 8798375 and 9547914) are recognized through several keynote invitations and the developed open-source codes have been collectively downloaded over 30,000 times. His work on IMU calibration/fusion (US patents 8362949, 9135802, 9295027, 9383202, and 9625290) with project X was first deployed via Google Indoor Maps (see XINX paper and ASIST/ACM SIGIR/ICADL keynotes) and is now widely used on mobile phones and VR/AR devices. Ed\'s team also developed the Google Q&amp;A system (codename Confucius), which was launched in over 60 countries.<br><br>Prior to Google, Ed was a full professor of Electrical Engineering at the University of California, Santa Barbara (UCSB). He joined UCSB in 1999 after receiving his PhD from Stanford University. He is a recipient of the NSF Career Award and Google Innovation Award. He is also an IEEE Fellow for his contributions to scalable machine learning.(字串長度:1969)'),(297,'2017-09-07 06:23:49',37,'position---->博士(字串長度:6)---->助研究員(字串長度:12)'),(298,'2017-09-07 06:23:50',52,'talk_title---->電腦不只選土豆，還會猜蛋白質功能?(字串長度:49)---->電腦不只選土豆，還會看病給藥與猜功能？(字串長度:57)```desc---->人類基因草圖在2000年完成後，隨著近來次世代定序技術發展，生物資料呈現爆炸性的增長，這時需要電腦來幫忙，演講中將分享其中一個應用，利用機器學習來預測蛋白質的功能，這與文件分類的問題很像，但是不同又在哪裡? 再進一步延伸，為何精準醫療機器學習也可以幫上嗎?(字串長度:370)---->人類基因草圖在2000年完成後，隨著近來次世代定序技術發展，如：富士康與華大基因 (Beijing Genomics Institute) 將合作生產次世代定序儀 BGISeq-50，代表個人基因體時代即將來臨，如何利用電腦來處理巨量增長的生物資料？演講中將分享幾個應用，首先機器學習在精準醫療，利用個人基因來幫助癌症用藥，機器學習也可以預測蛋白質的功能，這與文件分類的問題很像，但是不同又在哪裡?(字串長度:507)```me_desc---->First degree and Master\'s degree in Computer Science, both @ NTHU Taiwan; PhD research on alignment uncertainty and evolutionary analysis of Drosophila odorant receptor genes based on RNAseq @ CRG, Barcelona. Interested in the prediction of protein functional classes; interaction between alignment and evolution; evolutionary analysis based on RNAseq.(字串長度:352)---->First degree and Master\'s degree in Computer Science, both @ NTHU Taiwan; PhD research on alignment uncertainty and evolutionary analysis of Drosophila odorant receptor genes based on RNAseq @ CRG, Barcelona. Currently, working as an asssistent proferssor @ NCCU Taiwan. Interested in the prediction of protein functional classes; interaction between alignment and evolution; comparative genomics; next-generation sequencing analysis.(字串長度:434)'),(299,'2017-09-07 06:23:52',91,'company---->台灣微軟(字串長度:12)---->大中華區微軟(字串長度:18)'),(300,'2017-09-07 06:24:52',1,'me_desc---->Kwan-Liu Ma (馬匡六) is a professor of computer science and the chair of the Graduate Group in Computer Science (GGCS) at the University of California-Davis, where he directs VIDI Labs and UC Davis Center of Excellence for Visualization. He is also a visiting professor of the National Taiwan University and a chair professor of the National Chiao Tung University. His research spans the fields of visualization, computer graphics, high-performance computing, and user interface design. Professor Ma received his PhD in computer science from the University of Utah in 1993. During 1993-1999, he was with ICASE/NASA Langley Research Center as a research scientist. He joined UC Davis in 1999.<br><br>Professor Ma is presently leading a team of 25 researchers pursuing projects driven by a wide range of real-world applications.<br><br>He received the NSF Presidential Early-Career Research Award (PECASE) in 2000, was elected an IEEE Fellow in 2012, and received the 2013 IEEE VGTC Visualization Technical Achievement Award for his outstanding research work. Professor Ma has been actively serving the research community by playing leading roles in many<br><br>professional activities in the area of visualization. He presently serves as the Associate Editor-in-Chief of the<br><br>IEEE Computer Graphics and Applications (CG&amp;A), and on the editorial board of the Journal of Visualization, the Journal of Visual Informatics, and the Journal of Computational Visual Media.(字串長度:1477)---->馬匡六，現任美國加州大學戴維斯分校電腦工程系教授兼研究所主任，並領導一個25名研究員的團隊 (VIDI Labs)。 馬教授於 1993 年在美國猶他大學獲得電腦科學博士學位後，加入美國太空總署 Langley 研究中心擔任研究員，1999 年成為加州大學副教授。馬教授的研究興趣廣泛，主要在可視化，高性能計算，交互式技術之開發，整合及應用。<br><br>他因為在研究方面的傑出貢獻，於 2000 年獲得美國總統青年科學家獎 (PECASE)，2012 年獲選美國電子電機工程師協會院士，又於2013 年獲頒可視化研究領域最高榮譽的計術成就獎。 他目前是台大資工的客座教授，交大資工的榮譽教授，並参與中研院的研究計劃。(字串長度:792)'),(301,'2017-09-07 06:24:55',99,'conf---->(字串長度:0)---->DS(字串長度:2)'),(302,'2017-09-07 06:24:55',100,'conf---->(字串長度:0)---->DS(字串長度:2)'),(303,'2017-09-07 06:24:55',101,'conf---->(字串長度:0)---->DS(字串長度:2)'),(304,'2017-09-07 12:14:53',1,'desc---->Advanced computing and imaging technologies enable scientists to study natural phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Internet and mobile device users is expected to be even greater.<br><br>To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis methods. Visualization transforms large quantities of, often multiple-dimensional, data into graphical representations that exploit the high-bandwidth channel of the human visual system, leveraging the brain\'s remarkable ability to detect patterns and draw inferences.<br><br>It has been shown very effective in understanding large, complex data, and thus become an indispensable tool for many areas of research and practice. I will present several use cases of visualization based on new concepts and techniques that my group at UC Davis has introduced to further advance the visualization technology as a powerful discovery and communication tool.(字串長度:1125)---->Advanced computing and imaging technologies enable scientists to study natural phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Internet and mobile device users is expected to be even greater.  To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis methods. Visualization transforms large quantities of, often multiple-dimensional, data into graphical representations that exploit the high-bandwidth channel of the human visual system, leveraging the brain\'s remarkable ability to detect patterns and draw inferences.  It has been shown very effective in understanding large, complex data, and thus become an indispensable tool for many areas of research and practice. I will present several use cases of visualization based on new concepts and techniques that my group at UC Davis has introduced to further advance the visualization technology as a powerful discovery and communication tool.(字串長度:1113)```me_desc---->馬匡六，現任美國加州大學戴維斯分校電腦工程系教授兼研究所主任，並領導一個25名研究員的團隊 (VIDI Labs)。 馬教授於 1993 年在美國猶他大學獲得電腦科學博士學位後，加入美國太空總署 Langley 研究中心擔任研究員，1999 年成為加州大學副教授。馬教授的研究興趣廣泛，主要在可視化，高性能計算，交互式技術之開發，整合及應用。<br><br>他因為在研究方面的傑出貢獻，於 2000 年獲得美國總統青年科學家獎 (PECASE)，2012 年獲選美國電子電機工程師協會院士，又於2013 年獲頒可視化研究領域最高榮譽的計術成就獎。 他目前是台大資工的客座教授，交大資工的榮譽教授，並参與中研院的研究計劃。(字串長度:792)---->馬匡六，現任美國加州大學戴維斯分校電腦工程系教授兼研究所主任，並領導一個25名研究員的團隊 (VIDI Labs)。 馬教授於 1993 年在美國猶他大學獲得電腦科學博士學位後，加入美國太空總署 Langley 研究中心擔任研究員，1999 年成為加州大學副教授。馬教授的研究興趣廣泛，主要在可視化，高性能計算，交互式技術之開發，整合及應用。  他因為在研究方面的傑出貢獻，於 2000 年獲得美國總統青年科學家獎 (PECASE)，2012 年獲選美國電子電機工程師協會院士，又於2013 年獲頒可視化研究領域最高榮譽的計術成就獎。 他目前是台大資工的客座教授，交大資工的榮譽教授，並参與中研院的研究計劃。(字串長度:786)'),(305,'2017-09-07 12:14:59',7,'desc----> 從資料發掘新奇實用理論的機器學習，是近幾年台灣社會及學術界許多領域的顯學。近期學術界造假事件層出不窮，我認為這時候值得了解開放科學的宗旨：累積可重製的資料，才能建構經得起考驗的理論。<br><br>我以2011年心理學再現危機開始爆發的指標事件，與近兩年引發議論的「權力姿勢」為例，說明以新奇理論包裝的劣質研究能被發表，並獲得關注的關鍵。現在的開放科學採用那些措施扭轉這些問題。這些措施與資料科學的建模有沒有抵觸？或是相輔相成的可能？<br><br>最後以curate science project為例，說明如何運用累積數據，引導一般科學研究人員提昇研究操作品質，建構以可再現結果為基礎的實用理論。(字串長度:817)----> 從資料發掘新奇實用理論的機器學習，是近幾年台灣社會及學術界許多領域的顯學。近期學術界造假事件層出不窮，我認為這時候值得了解開放科學的宗旨：累積可重製的資料，才能建構經得起考驗的理論。  我以2011年心理學再現危機開始爆發的指標事件，與近兩年引發議論的「權力姿勢」為例，說明以新奇理論包裝的劣質研究能被發表，並獲得關注的關鍵。現在的開放科學採用那些措施扭轉這些問題。這些措施與資料科學的建模有沒有抵觸？或是相輔相成的可能？  最後以curate science project為例，說明如何運用累積數據，引導一般科學研究人員提昇研究操作品質，建構以可再現結果為基礎的實用理論。(字串長度:805)```me_desc---->慈濟大人類發展與心理學系專任助理教授 開放科學中心大使(Center for Open Science Ambassador)<br><br>在台灣教了幾年書，2014年受到開放科學的知識論感召，親身到荷蘭體會身體力行之路。希望向華文大眾與學術界傳播分辨好壞科學的心法。(字串長度:307)---->慈濟大人類發展與心理學系專任助理教授 開放科學中心大使(Center for Open Science Ambassador)  在台灣教了幾年書，2014年受到開放科學的知識論感召，親身到荷蘭體會身體力行之路。希望向華文大眾與學術界傳播分辨好壞科學的心法。(字串長度:301)'),(306,'2017-09-07 12:15:01',9,'desc---->學習過基本深度學習的概念, 也會一些建構神經網路方便的工具如 Keras 之後,<br><br>我們總覺得馬上就可以用深度學習的方法做點東西出來, 但往往不如我們預想中的順利。這個演講中我們會先複習基本神經網路的架構, 接著分享在實務應用上經歷過的種種卡關, 以及克服這些障礙的方式。(字串長度:378)---->學習過基本深度學習的概念, 也會一些建構神經網路方便的工具如 Keras 之後,  我們總覺得馬上就可以用深度學習的方法做點東西出來, 但往往不如我們預想中的順利。這個演講中我們會先複習基本神經網路的架構, 接著分享在實務應用上經歷過的種種卡關, 以及克服這些障礙的方式。(字串長度:372)'),(307,'2017-09-07 12:15:10',16,'desc---->Social platforms are an ideal place for spreading rumors and fake news. As more people seek information and read news online, automatically debunking such false claims has become an urgent problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand their propagation processes. In detecting rumors and fake news in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early detection. I will also highlight the latest detection studies with deep learning techniques.<br><br> Short Bio Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:1560)---->Social platforms are an ideal place for spreading rumors and fake news. As more people seek information and read news online, automatically debunking such false claims has become an urgent problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand their propagation processes. In detecting rumors and fake news in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early detection. I will also highlight the latest detection studies with deep learning techniques.   Short Bio Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:1554)'),(308,'2017-09-07 12:15:15',20,'desc---->許多人都了解資料視覺化的優點，例如可將資料抽象化、讓資訊降維等等，讓數據以更直覺的方式呈現在使用者面前，提升閱讀者的吸收意願。然而，我們依然可以舉出許多例子，即使是視覺化後的數據，依然不容易讓人讀懂其價值，也難以勾起人們理解的意願。<br><br>『使用者經驗（UX）』設計的最終目標是『Don’t Make Me Think』，也就是打造一段順暢的閱讀過程，讓使用者能夠更輕鬆理解背後的寶貴價值，而這之中除了透過視覺設計、互動設計將結果優化之外，還包括了使用者研究、需求釐清、文案優化等等技巧，如果能夠將使用者經驗設計相關方法結合資料視覺化技巧，能夠再一次降低數據解讀的門檻，讓更多人了解其價值意涵，也讓更多人被數據所感動。(字串長度:883)---->許多人都了解資料視覺化的優點，例如可將資料抽象化、讓資訊降維等等，讓數據以更直覺的方式呈現在使用者面前，提升閱讀者的吸收意願。然而，我們依然可以舉出許多例子，即使是視覺化後的數據，依然不容易讓人讀懂其價值，也難以勾起人們理解的意願。  『使用者經驗（UX）』設計的最終目標是『Don’t Make Me Think』，也就是打造一段順暢的閱讀過程，讓使用者能夠更輕鬆理解背後的寶貴價值，而這之中除了透過視覺設計、互動設計將結果優化之外，還包括了使用者研究、需求釐清、文案優化等等技巧，如果能夠將使用者經驗設計相關方法結合資料視覺化技巧，能夠再一次降低數據解讀的門檻，讓更多人了解其價值意涵，也讓更多人被數據所感動。(字串長度:877)'),(309,'2017-09-07 12:15:16',21,'me_desc---->aha於2007年畢業於台大物理所，專長：奈米光學模擬，2009年進入中研院資訊所，主要研究：participantory sensing(參與式感測)，研究如何透過個人裝置感測搜集資料，並整合拼湊完整巨觀環境變化，2013年入行，初任職為商業分析小組，著重於透過資料探索找出商機與企劃方向．2015年成立並加入創新研發小組，主要負責各式新技術評估與導入，同時規劃與評估相關商業模式的落地與執行，參與數位旗艦分行與玉山小i 隨身金融顧問等專案。 aha 專長於組裝各式語言與各大平台服務，打造最小可行產品原型。同時關心社群與社會，如：Govcash 政治獻金研究是透過g0v的政治獻金數位化的成果；2014年為台灣資料科學愛好者年會R 課程講師．現正沈浸於台灣金融法規、具有台灣特色Fintech、AI、blockchain應用中。<br><br>(字串長度:934)---->aha於2007年畢業於台大物理所，專長：奈米光學模擬，2009年進入中研院資訊所，主要研究：participantory sensing(參與式感測)，研究如何透過個人裝置感測搜集資料，並整合拼湊完整巨觀環境變化，2013年入行，初任職為商業分析小組，著重於透過資料探索找出商機與企劃方向．2015年成立並加入創新研發小組，主要負責各式新技術評估與導入，同時規劃與評估相關商業模式的落地與執行，參與數位旗艦分行與玉山小i 隨身金融顧問等專案。 aha 專長於組裝各式語言與各大平台服務，打造最小可行產品原型。同時關心社群與社會，如：Govcash 政治獻金研究是透過g0v的政治獻金數位化的成果；2014年為台灣資料科學愛好者年會R 課程講師．現正沈浸於台灣金融法規、具有台灣特色Fintech、AI、blockchain應用中。  (字串長度:928)'),(310,'2017-09-07 12:15:18',23,'me_desc---->溫在弘博士於2009年任職於國立台灣大學地理環境資源學系，並兼任台大人口與性別研究中心、統計碩士學位學程以及傳染病防治研究與教育中心等教研工作。研究興趣是地理過程的數理建模與空間分析，著重於都市環境人口移動導致的傳染病疫情、連續犯罪等的地理擴散現象。近年獲得科技部的學術獎勵，包括：｢優秀年輕學者研究計畫｣（2014-2016）、｢吳大猷先生紀念獎｣<br><br>(2015)以及｢補助大專校院延攬特殊優秀人才措施｣ (2016)等。(字串長度:586)---->溫在弘博士於2009年任職於國立台灣大學地理環境資源學系，並兼任台大人口與性別研究中心、統計碩士學位學程以及傳染病防治研究與教育中心等教研工作。研究興趣是地理過程的數理建模與空間分析，著重於都市環境人口移動導致的傳染病疫情、連續犯罪等的地理擴散現象。近年獲得科技部的學術獎勵，包括：｢優秀年輕學者研究計畫｣（2014-2016）、｢吳大猷先生紀念獎｣  (2015)以及｢補助大專校院延攬特殊優秀人才措施｣ (2016)等。(字串長度:580)'),(311,'2017-09-07 12:15:19',24,'desc---->「高雄火災風險地圖2.0」係由智庫驅動與高雄市政府消防局共同推動之資料英雄專案，本專案透過隨機森林模型找出火災關鍵因子，進而在消防資源有限的情況下優化巡視家訪建物之效率。建物火災風險預測是典型的非均衡學習，過程中我們利用 BalanceCascade 等方法處理非平衡學習，並測試不同分類器在 bootstrapping 中的表現，最後模型在預測建物火災機率準確率可達 88%、召回率亦有 78% 之表現。<br><br>根據內政部消防署公布火災統計資料，105 年火災發生次數1,856 次，火災發生類型以建築物火災1,424次最多，造成火災死亡人數約占年度火災死亡人數的86%。鑑此，降低當地建築物火災事故是各縣市政府消防局的難題之一。對抗火災的最佳策略是採取預防性介入，防患於未然，本次專案與高雄市政府消防局合作，選定鳳山區作為標的，從建築物火災角度出發，彙整建築物及人口特性資料，建構火災風險評估模型，設法找出影響建築物火災關鍵因子，面對當前人力、資源有限的挑戰下，優化現有防火診斷、宣導策略。<br><br>前期研究僅依小樣本火警報案紀錄與火災地特徵、或著力於森林火災等空間分析；於建物火災方面則較少著墨。本專案在未失火建物佔多數樣本的情況下，非均衡資料對訓練模型是一大挑戰。在此我們選用了 BalanceCasecade 方法，以 kNN 分類器為依據將未失火建物拆成與失火建物數目一致之子資料集，並透過數個小的 ensemble 隨機森林模型預測建物之火災發生機率。另外，我們也藉由深度學習模型預測建物如發生火災之預期損失。<br><br>此專案將非均衡學習實際應用到政府部門，並且實驗不同的分類器下 BalanceCascade 表現之特性。研究後我們也進一步將結果彙整給高雄市政府消防局供往後使用。過程中發現諸多地方政府在跨部會的資料倉儲管理之缺失，我們也加以從「公私協力」角度反饋給地方政府，希望藉由專案的互動交流意見，能夠進一步讓資料倉儲成為政府開放資料之基石。(字串長度:2260)---->「高雄火災風險地圖2.0」係由智庫驅動與高雄市政府消防局共同推動之資料英雄專案，本專案透過隨機森林模型找出火災關鍵因子，進而在消防資源有限的情況下優化巡視家訪建物之效率。建物火災風險預測是典型的非均衡學習，過程中我們利用 BalanceCascade 等方法處理非平衡學習，並測試不同分類器在 bootstrapping 中的表現，最後模型在預測建物火災機率準確率可達 88%、召回率亦有 78% 之表現。  根據內政部消防署公布火災統計資料，105 年火災發生次數1,856 次，火災發生類型以建築物火災1,424次最多，造成火災死亡人數約占年度火災死亡人數的86%。鑑此，降低當地建築物火災事故是各縣市政府消防局的難題之一。對抗火災的最佳策略是採取預防性介入，防患於未然，本次專案與高雄市政府消防局合作，選定鳳山區作為標的，從建築物火災角度出發，彙整建築物及人口特性資料，建構火災風險評估模型，設法找出影響建築物火災關鍵因子，面對當前人力、資源有限的挑戰下，優化現有防火診斷、宣導策略。  前期研究僅依小樣本火警報案紀錄與火災地特徵、或著力於森林火災等空間分析；於建物火災方面則較少著墨。本專案在未失火建物佔多數樣本的情況下，非均衡資料對訓練模型是一大挑戰。在此我們選用了 BalanceCasecade 方法，以 kNN 分類器為依據將未失火建物拆成與失火建物數目一致之子資料集，並透過數個小的 ensemble 隨機森林模型預測建物之火災發生機率。另外，我們也藉由深度學習模型預測建物如發生火災之預期損失。  此專案將非均衡學習實際應用到政府部門，並且實驗不同的分類器下 BalanceCascade 表現之特性。研究後我們也進一步將結果彙整給高雄市政府消防局供往後使用。過程中發現諸多地方政府在跨部會的資料倉儲管理之缺失，我們也加以從「公私協力」角度反饋給地方政府，希望藉由專案的互動交流意見，能夠進一步讓資料倉儲成為政府開放資料之基石。(字串長度:2242)'),(312,'2017-09-07 12:15:19',25,'desc---->The approaches in feature extraction can be divided into two categories: model-centric and data-driven. The model-centric approach relies on human heuristics to develop a computer model to extract features from data. These models were engineered by scientists and then validated via empirical studies. A major shortcoming of the model-centric approach is that unusual circumstances that a model does not take into consideration during its design can render the engineered features less effective.<br><br>Contrast to the model-centric approach, which dictates representations independent of data, the data-driven approach learns representations from data.<br><br>Example data-driven algorithms are multilayer perceptron (MLP) and convolutional neural network (CNN), which belong to the general category of neural network and deep learning.<br><br>In this talk I will first explain why my team at Google in 2006 embarked on the data-drive approach.<br><br>In 2008, we funded the ImageNet project at Stanford, and subsequently in 2011 filed two data-driven patents, one on data-driven feature extraction and the other on data-driven object recognition.<br><br>We parallelized five widely used machine learning algorithms including SVMs, PFP, LDA, Spectral Clustering, and CNN, and open-sourced all these algorithms.<br><br>Recently at HTC, we announced DeepQ Open AI platform, which features these scalable algorithms with enhancements.<br><br>In particular, I will explain how we have made configuring a distributed system to run CNN both simple and cost effective.<br><br><br><br>In 2012, the world was convinced that the data-driven approach to be effective, after Alexnet achieved breakthrough accuracy in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition.<br><br>This talk will walk through subsequent enhancements on Alexnet in three perspectives: representation ability, optimization ability, and generalization ability.<br><br>Unfortunately, most applications face the problem of small data. I will share our experiences with transfer learning and GANs, both positive and negative ones. This talk concludes with a list of open research issues. (字串長度:2174)---->The approaches in feature extraction can be divided into two categories: model-centric and data-driven. The model-centric approach relies on human heuristics to develop a computer model to extract features from data. These models were engineered by scientists and then validated via empirical studies. A major shortcoming of the model-centric approach is that unusual circumstances that a model does not take into consideration during its design can render the engineered features less effective.  Contrast to the model-centric approach, which dictates representations independent of data, the data-driven approach learns representations from data.  Example data-driven algorithms are multilayer perceptron (MLP) and convolutional neural network (CNN), which belong to the general category of neural network and deep learning.  In this talk I will first explain why my team at Google in 2006 embarked on the data-drive approach.  In 2008, we funded the ImageNet project at Stanford, and subsequently in 2011 filed two data-driven patents, one on data-driven feature extraction and the other on data-driven object recognition.  We parallelized five widely used machine learning algorithms including SVMs, PFP, LDA, Spectral Clustering, and CNN, and open-sourced all these algorithms.  Recently at HTC, we announced DeepQ Open AI platform, which features these scalable algorithms with enhancements.  In particular, I will explain how we have made configuring a distributed system to run CNN both simple and cost effective.    In 2012, the world was convinced that the data-driven approach to be effective, after Alexnet achieved breakthrough accuracy in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) competition.  This talk will walk through subsequent enhancements on Alexnet in three perspectives: representation ability, optimization ability, and generalization ability.  Unfortunately, most applications face the problem of small data. I will share our experiences with transfer learning and GANs, both positive and negative ones. This talk concludes with a list of open research issues. (字串長度:2108)```me_desc---->Edward Chang currently serves as the President of Research and Healthcare (DeepQ) at HTC. Ed\'s most notable work is co-leading the DeepQ project (with Prof. CK Peng at Harvard), working with a team of physicians, scientists, and engineers to design and develop mobile wireless diagnostic instruments. Such instruments can help consumers make their own reliable health diagnoses anywhere at any time. The project entered the Tricorder XPRIZE competition in 2013 with 310 other entrants and was awarded second place in April 2017 with 1M USD prize. The deep architecture that powers DeepQ is also applied to power Vivepaper, an AR product Ed\'s team launched in 2016 to support immersive augmented reality experiences.<br><br>Prior to his HTC post, Ed was a director of Google Research for 6.5 years, leading research and development in several areas including scalable machine learning, indoor localization, social networking and search integration, and Web search (spam fighting). His contributions in parallel machine learning algorithms and data-driven deep learning (US patents 8798375 and 9547914) are recognized through several keynote invitations and the developed open-source codes have been collectively downloaded over 30,000 times. His work on IMU calibration/fusion (US patents 8362949, 9135802, 9295027, 9383202, and 9625290) with project X was first deployed via Google Indoor Maps (see XINX paper and ASIST/ACM SIGIR/ICADL keynotes) and is now widely used on mobile phones and VR/AR devices. Ed\'s team also developed the Google Q&amp;A system (codename Confucius), which was launched in over 60 countries.<br><br>Prior to Google, Ed was a full professor of Electrical Engineering at the University of California, Santa Barbara (UCSB). He joined UCSB in 1999 after receiving his PhD from Stanford University. He is a recipient of the NSF Career Award and Google Innovation Award. He is also an IEEE Fellow for his contributions to scalable machine learning.(字串長度:1969)---->Edward Chang currently serves as the President of Research and Healthcare (DeepQ) at HTC. Ed\'s most notable work is co-leading the DeepQ project (with Prof. CK Peng at Harvard), working with a team of physicians, scientists, and engineers to design and develop mobile wireless diagnostic instruments. Such instruments can help consumers make their own reliable health diagnoses anywhere at any time. The project entered the Tricorder XPRIZE competition in 2013 with 310 other entrants and was awarded second place in April 2017 with 1M USD prize. The deep architecture that powers DeepQ is also applied to power Vivepaper, an AR product Ed\'s team launched in 2016 to support immersive augmented reality experiences.  Prior to his HTC post, Ed was a director of Google Research for 6.5 years, leading research and development in several areas including scalable machine learning, indoor localization, social networking and search integration, and Web search (spam fighting). His contributions in parallel machine learning algorithms and data-driven deep learning (US patents 8798375 and 9547914) are recognized through several keynote invitations and the developed open-source codes have been collectively downloaded over 30,000 times. His work on IMU calibration/fusion (US patents 8362949, 9135802, 9295027, 9383202, and 9625290) with project X was first deployed via Google Indoor Maps (see XINX paper and ASIST/ACM SIGIR/ICADL keynotes) and is now widely used on mobile phones and VR/AR devices. Ed\'s team also developed the Google Q&A system (codename Confucius), which was launched in over 60 countries.  Prior to Google, Ed was a full professor of Electrical Engineering at the University of California, Santa Barbara (UCSB). He joined UCSB in 1999 after receiving his PhD from Stanford University. He is a recipient of the NSF Career Award and Google Innovation Award. He is also an IEEE Fellow for his contributions to scalable machine learning.(字串長度:1953)'),(313,'2017-09-07 12:15:22',31,'me_desc---->東華應數副教授，今年在台灣<br><br>Python 年會專業課程、TWSIAM 會前工作坊 PyDay、台大黑客松資料科學學程、Taipei.py 、南港高中及花蓮.py Omega Zone 開設資料科學、影像處理的工作坊或課程。 創辦花蓮.py 及實做數學，定期、不定期組織各項活動。(字串長度:322)---->東華應數副教授，今年在台灣  Python 年會專業課程、TWSIAM 會前工作坊 PyDay、台大黑客松資料科學學程、Taipei.py 、南港高中及花蓮.py Omega Zone 開設資料科學、影像處理的工作坊或課程。 創辦花蓮.py 及實做數學，定期、不定期組織各項活動。(字串長度:316)'),(314,'2017-09-07 12:15:33',40,'desc---->This is a statistician\'s view of the development of Data Science. In 2002, Professor Ryan Rifkin, from a mathematical perspective, entitled his PhD thesis as &quot;Everything old is new again.&quot;<br><br>Statistics has a shorter history comparing with mathematics and other natural sciences. In this talk, we will go over some classical statistical papers, and try to connect their thoughts with modern data science.<br><br>From their thoughts, we wish to relax some barriers as learning and using statistics in such a modern era. (字串長度:533)---->This is a statistician\'s view of the development of Data Science. In 2002, Professor Ryan Rifkin, from a mathematical perspective, entitled his PhD thesis as \"Everything old is new again.\"  Statistics has a shorter history comparing with mathematics and other natural sciences. In this talk, we will go over some classical statistical papers, and try to connect their thoughts with modern data science.  From their thoughts, we wish to relax some barriers as learning and using statistics in such a modern era. (字串長度:511)'),(315,'2017-09-07 12:15:38',46,'me_desc---->Ed H. Chi is a Research Scientist at Google, leading a team focused on recommendation systems, machine learning, and social interaction research. He has launched significant improvements of recommenders for YouTube, Google Play Store and Google+. With over 35 patents and over 100 research articles, he is known for research on Web and online social systems, and the effects of social signals on user behavior.<br><br> Prior to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center\'s Augmented Social Cognition<br><br>Group, where he led the group in understanding how social systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press, and has won awards for both teaching and research. He has a<br><br>In his spare time, Ed is an avid photographer and snowboarder.(字串長度:1079)---->Ed H. Chi is a Research Scientist at Google, leading a team focused on recommendation systems, machine learning, and social interaction research. He has launched significant improvements of recommenders for YouTube, Google Play Store and Google+. With over 35 patents and over 100 research articles, he is known for research on Web and online social systems, and the effects of social signals on user behavior.   Prior to Google, he was the Area Manager and a Principal Scientist at Palo Alto Research Center\'s Augmented Social Cognition  Group, where he led the group in understanding how social systems help groups of people to remember, think and reason. Ed completed his three degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota, and has been doing research on software systems since 1993. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press, and has won awards for both teaching and research. He has a  In his spare time, Ed is an avid photographer and snowboarder.(字串長度:1061)'),(316,'2017-09-07 12:15:39',48,'desc---->理財不再是有錢人的特權！過去理財一直都是高端資產客戶能享有的服務，僅有少部分的理專會提供所謂&quot;mass&quot;客戶的理財規劃。在大數據及資料科學的發展下，為了服務更多的&quot;mass&quot;客戶，基於演算法的理財服務漸漸浮出水面，透過了解客戶屬性、投資風險及目標規劃出個人化的投資組合，並在較低投資門檻下完成理財服務。未來投資及理財不再是夢想。(字串長度:488)---->理財不再是有錢人的特權！過去理財一直都是高端資產客戶能享有的服務，僅有少部分的理專會提供所謂\"mass\"客戶的理財規劃。在大數據及資料科學的發展下，為了服務更多的\"mass\"客戶，基於演算法的理財服務漸漸浮出水面，透過了解客戶屬性、投資風險及目標規劃出個人化的投資組合，並在較低投資門檻下完成理財服務。未來投資及理財不再是夢想。(字串長度:468)'),(317,'2017-09-07 12:15:40',51,'desc---->1. 高薪人才需求: 談談中國的中高階人才需求，不可不知的市場現況<br><br>2. 搶錢搶糧搶未來: 學習沒有懶人包，AI 教育的重要性<br><br>3. 動手學 AI: 從算法著手，從應用回眸<br><br>4. 手撕演算法: 把技術貢獻給全世界，力量留給臺灣<br><br>5. 加碼不小心分享: 超低調手刻 AI 程式交易演算法經驗談 (字串長度:386)---->1. 高薪人才需求: 談談中國的中高階人才需求，不可不知的市場現況  2. 搶錢搶糧搶未來: 學習沒有懶人包，AI 教育的重要性  3. 動手學 AI: 從算法著手，從應用回眸  4. 手撕演算法: 把技術貢獻給全世界，力量留給臺灣  5. 加碼不小心分享: 超低調手刻 AI 程式交易演算法經驗談 (字串長度:362)'),(318,'2017-09-07 12:15:48',61,'desc---->隨著近年學術研究進展及相關工具成熟，各產業開始關注資料科學的應用。然而要滿足實務需求，不止要有好的資料科學模型，還要顧及其對軟體系統的要求，如系統的可靠性及可擴充性。一個團隊要能持續研發模型，並有效率地導入相應的軟體生產系統，會同時需要資料科學及軟體工程的長才，在團隊的協作及科技的選擇上也會有諸多考量與取捨。<br><br>講者將分享過去開發機器學習系統的經驗與心得。討論不同性質團隊的考量：從基礎完善的大公司Google、到決策彈性且變化迅速的新創團隊MetaMind、再到被收購後配合建構較新創階段更可靠可擴展的雲端系統。並討論不同應用的需求差異：文獻檢索、醫療影像、商務應用、APP後台API、及生產自動化。 (字串長度:869)---->隨著近年學術研究進展及相關工具成熟，各產業開始關注資料科學的應用。然而要滿足實務需求，不止要有好的資料科學模型，還要顧及其對軟體系統的要求，如系統的可靠性及可擴充性。一個團隊要能持續研發模型，並有效率地導入相應的軟體生產系統，會同時需要資料科學及軟體工程的長才，在團隊的協作及科技的選擇上也會有諸多考量與取捨。  講者將分享過去開發機器學習系統的經驗與心得。討論不同性質團隊的考量：從基礎完善的大公司Google、到決策彈性且變化迅速的新創團隊MetaMind、再到被收購後配合建構較新創階段更可靠可擴展的雲端系統。並討論不同應用的需求差異：文獻檢索、醫療影像、商務應用、APP後台API、及生產自動化。 (字串長度:863)'),(319,'2017-09-07 12:15:53',67,'desc---->我們設計的手機程式「Know Addiction」，顧名思義是幫助手機使用者能夠「了解」 (Know) 自己使用手機的程度 (Addiction)，並且「預防」過度沉迷使用 (No Addiction)。<br><br>「Know Addiction」可以預測精神科醫師對手機成癮的判斷；也可以輔助提升醫師診斷的準確度。記錄的手機使用行為，還能讓研究人員開發新的演算方法，解讀人類的各種行為。全自動化的手機紀錄程式可以排除傳統紀錄的困難，可作為自我管理，亦可把資料數據藉由分享給專業人員參考。<br><br>「Know Addiction」不僅有「診斷」的功能，也有「治療」的效果。當使用手機的程度超過基準值時，雲端平台的決策系統，會自動提醒通知使用者。這套具有自動化、圖像化、自我檢測、雲端資源共享特點的系統，使「智慧型手機成癮」不但能診斷、治療、還能「自療」！(字串長度:960)---->我們設計的手機程式「Know Addiction」，顧名思義是幫助手機使用者能夠「了解」 (Know) 自己使用手機的程度 (Addiction)，並且「預防」過度沉迷使用 (No Addiction)。  「Know Addiction」可以預測精神科醫師對手機成癮的判斷；也可以輔助提升醫師診斷的準確度。記錄的手機使用行為，還能讓研究人員開發新的演算方法，解讀人類的各種行為。全自動化的手機紀錄程式可以排除傳統紀錄的困難，可作為自我管理，亦可把資料數據藉由分享給專業人員參考。  「Know Addiction」不僅有「診斷」的功能，也有「治療」的效果。當使用手機的程度超過基準值時，雲端平台的決策系統，會自動提醒通知使用者。這套具有自動化、圖像化、自我檢測、雲端資源共享特點的系統，使「智慧型手機成癮」不但能診斷、治療、還能「自療」！(字串長度:948)```me_desc---->林煜軒博士為國內少數兼具臨床、企業界資歷之精神科醫師。<br><br>近年投入網路心理學等多項跨領域研究：譯作《網路成癮：評估及治療指引手冊》榮獲國健署「優良健康讀物推介獎」、編製「智慧型手機成癮量表」，獲得各國學者要求授權翻譯為德、法、義大利、匈牙利、芬蘭、葡萄牙、土耳其、印度等多國語言版本的高度肯定。致力研發雲端服務的手機程式 (App)，獲得多項國內外專利。受邀編寫國際知名網路成癮教科書中「智慧型手機成癮」章節。現為衛生福利部「本國兒童青少年健康政策白皮書—3C產品使用」召集人。<br><br> 林醫師經常於校園、社區、職場演講，積極建立產學合作平台，深耕社區健康促進，並協助研擬相關政策；推廣近年風行各大跨國企業的「員工協助計畫」(Employee Assistant Program)。(字串長度:947)---->林煜軒博士為國內少數兼具臨床、企業界資歷之精神科醫師。  近年投入網路心理學等多項跨領域研究：譯作《網路成癮：評估及治療指引手冊》榮獲國健署「優良健康讀物推介獎」、編製「智慧型手機成癮量表」，獲得各國學者要求授權翻譯為德、法、義大利、匈牙利、芬蘭、葡萄牙、土耳其、印度等多國語言版本的高度肯定。致力研發雲端服務的手機程式 (App)，獲得多項國內外專利。受邀編寫國際知名網路成癮教科書中「智慧型手機成癮」章節。現為衛生福利部「本國兒童青少年健康政策白皮書—3C產品使用」召集人。   林醫師經常於校園、社區、職場演講，積極建立產學合作平台，深耕社區健康促進，並協助研擬相關政策；推廣近年風行各大跨國企業的「員工協助計畫」(Employee Assistant Program)。(字串長度:935)'),(320,'2017-09-07 12:15:53',68,'desc---->Image Segmentation is quite useful for image understanding, however, the computation is considerable as well.<br><br>In this talk, I\'m going to introduce how we utilize deep learning to segment human body, what problems we met and how we resolve it.(字串長度:249)---->Image Segmentation is quite useful for image understanding, however, the computation is considerable as well.  In this talk, I\'m going to introduce how we utilize deep learning to segment human body, what problems we met and how we resolve it.(字串長度:243)'),(321,'2017-09-07 12:15:54',69,'desc---->均一教育平台自詡為一個用科學和證據來解決教育問題的非營利組織，從收集了非常多的資料，到真的讓團隊感受到資料數據的好處，並且夥伴們願意主動思考如何善用資料，這中間走了超過一年。<br><br>一直到今年，過去長期累積的資料才逐漸發揮它的價值，也在組織決策和產品開發上佔據更重要的地位，除了夥伴的處理資料能力上升、資料架構逐漸成熟以外，我們漸漸發現 -- 就像產品功能開發一樣 -- 事先把資料題目定清楚是非常重要的。網路上對於這類釐清 Spec 的分享較少，均一很高興有這個機會分享自己各樣的經驗，也跟大家請教討論看如何可以善用資料發揮更大的影響力。(字串長度:778)---->均一教育平台自詡為一個用科學和證據來解決教育問題的非營利組織，從收集了非常多的資料，到真的讓團隊感受到資料數據的好處，並且夥伴們願意主動思考如何善用資料，這中間走了超過一年。  一直到今年，過去長期累積的資料才逐漸發揮它的價值，也在組織決策和產品開發上佔據更重要的地位，除了夥伴的處理資料能力上升、資料架構逐漸成熟以外，我們漸漸發現 -- 就像產品功能開發一樣 -- 事先把資料題目定清楚是非常重要的。網路上對於這類釐清 Spec 的分享較少，均一很高興有這個機會分享自己各樣的經驗，也跟大家請教討論看如何可以善用資料發揮更大的影響力。(字串長度:772)```me_desc---->現任均一教育平台「軟體團隊活化者」的角色，非常高興有機會加入均一的團隊，均一非常看重自己的文化：「誠信正直、實事求是、積極主動、以終為始 . . .」，建構在文化的基礎上，我們致力於用證據、論述和辯證找出團隊的方向和做事的方法。<br><br>我非常相信善用「資料」，我們可以更認清「事實」，而認清事實可以幫助我們做更對的決定，最終有更大的機會幫助團隊解決困難的問題。(字串長度:524)---->現任均一教育平台「軟體團隊活化者」的角色，非常高興有機會加入均一的團隊，均一非常看重自己的文化：「誠信正直、實事求是、積極主動、以終為始 . . .」，建構在文化的基礎上，我們致力於用證據、論述和辯證找出團隊的方向和做事的方法。  我非常相信善用「資料」，我們可以更認清「事實」，而認清事實可以幫助我們做更對的決定，最終有更大的機會幫助團隊解決困難的問題。(字串長度:518)'),(322,'2017-09-07 12:15:59',75,'desc---->「情緒」是社群網站上的一種特性，它可以用在不同的用途，像是偵測意見、消除模稜兩可、猜測興趣、甚至是預防犯罪以及偵測自殺傾向。因此，偵測出使用者文章中的情緒，將是改善服務以及關懷社會中，非常重要的一環。<br><br><br><br> 過去的研究在分析情緒的時候，大部分都採用正負方來分類，而且會利用計算語言專有的技術，如：字詞分析、使用情緒辭典，在這個演講中，我們將分享用一種異於傳統的分析方法，利用潛意識群眾智慧，不但可以分出更細的情緒（如：高興、悲傷、憤怒、驚訝等等），還可以輕易將方法運用到多國語言的情緒分析上。在我們過去的經驗中，我們利用情緒分析技術，運用到心理情緒的偵測，來偵查社群網路上發文者的心理狀態（躁鬱等），期待這樣的技術將可以減少許多不幸的案件、或協助研究者了解心理狀態的轉變。<br><br><br><br>從這些過去的研究中，我們將會分享一些過去的經驗、由不同的視野來探討使用者，相信這些經驗將能引起資料愛好者的興趣、且具有應用的參考價值。(字串長度:1230)---->「情緒」是社群網站上的一種特性，它可以用在不同的用途，像是偵測意見、消除模稜兩可、猜測興趣、甚至是預防犯罪以及偵測自殺傾向。因此，偵測出使用者文章中的情緒，將是改善服務以及關懷社會中，非常重要的一環。     過去的研究在分析情緒的時候，大部分都採用正負方來分類，而且會利用計算語言專有的技術，如：字詞分析、使用情緒辭典，在這個演講中，我們將分享用一種異於傳統的分析方法，利用潛意識群眾智慧，不但可以分出更細的情緒（如：高興、悲傷、憤怒、驚訝等等），還可以輕易將方法運用到多國語言的情緒分析上。在我們過去的經驗中，我們利用情緒分析技術，運用到心理情緒的偵測，來偵查社群網路上發文者的心理狀態（躁鬱等），期待這樣的技術將可以減少許多不幸的案件、或協助研究者了解心理狀態的轉變。    從這些過去的研究中，我們將會分享一些過去的經驗、由不同的視野來探討使用者，相信這些經驗將能引起資料愛好者的興趣、且具有應用的參考價值。(字串長度:1206)'),(323,'2017-09-07 12:16:02',82,'desc---->There are more and more intelligence demands on the embedded system such as smart phone, smart phone, drone….<br><br>The challenge for embedded AI is limited power budget and computing resource.<br><br>Challenges also mean opportunities.<br><br>This speech will introduce the technologies to enable AI on the embeded devices.(字串長度:327)---->There are more and more intelligence demands on the embedded system such as smart phone, smart phone, drone….  The challenge for embedded AI is limited power budget and computing resource.  Challenges also mean opportunities.  This speech will introduce the technologies to enable AI on the embeded devices.(字串長度:309)```me_desc---->TY Lin works at the office of CTO in MediaTek.<br><br>He is leading the teams to develop AI SW/HW technologies for embedde devices(字串長度:130)---->TY Lin works at the office of CTO in MediaTek.  He is leading the teams to develop AI SW/HW technologies for embedde devices(字串長度:124)'),(324,'2017-09-07 12:16:07',86,'me_desc---->Lee-Feng Chien is Managing Director and Engineering Site Director for Google Taiwan. He leads the operation and development of Google\'s engineering effort in Taiwan, and is an engineering lead of ChromeOS Product Area for APAC. He is also known for his work on Chinese search and language processing, and has researched Chinese parsing systems, language models, dictation machines, and search engineering technology for many years. Prior to Google, he was research fellow and deputy director of the Institute of information Science, Academia Sinica, Taiwan, and also jointly appointed as a professor of the Information Management Department of National Taiwan University.<br><br>He received his Ph.D. in CS&amp;IE from National Taiwan University in 1991.(字串長度:754)---->Lee-Feng Chien is Managing Director and Engineering Site Director for Google Taiwan. He leads the operation and development of Google\'s engineering effort in Taiwan, and is an engineering lead of ChromeOS Product Area for APAC. He is also known for his work on Chinese search and language processing, and has researched Chinese parsing systems, language models, dictation machines, and search engineering technology for many years. Prior to Google, he was research fellow and deputy director of the Institute of information Science, Academia Sinica, Taiwan, and also jointly appointed as a professor of the Information Management Department of National Taiwan University.  He received his Ph.D. in CS&IE from National Taiwan University in 1991.(字串長度:744)'),(325,'2017-09-07 12:16:09',87,'me_desc---->對於如何使用資料與數學解決真實的商業問題，有著高度的興趣，並且有著多年深刻的著墨。擔任過許多國內大型公司，與外國公司的資料分析，策略發展，以及商業顧問。由於，本身也超級熱愛開發程式，也常常擔任許多公司的技術顧問，或幫忙組織與建立技術團隊，或具有工程能力的資料分析團隊。<br><br>經歷： ** 共同創辦人 @Taiwan R User Group &amp; MLDM Monday ** 常年在國內推廣與推動 quantmod &amp; quantstrat 的使用與應用 ** 共同創辦人 @木刻思股份有限公司<br><br>** 金融研訓院 R &amp; quantmod &amp; quantstrat 講師 ** Microsoft Data Platform Most Valuable Professionals ** PyCon APAC 2014 Speaker：Hacking Model with Python ** PyCon APAC 2014 Tutorial Session：PlaY data ** PyCon APAC 2015 Speaker：How to build a recommendation system with python ** PyCon APAC 2015 Tutorial Session：Prob. Modeling &amp; Text Mining ** PyData SF 2016 Speaker (字串長度:1005)---->對於如何使用資料與數學解決真實的商業問題，有著高度的興趣，並且有著多年深刻的著墨。擔任過許多國內大型公司，與外國公司的資料分析，策略發展，以及商業顧問。由於，本身也超級熱愛開發程式，也常常擔任許多公司的技術顧問，或幫忙組織與建立技術團隊，或具有工程能力的資料分析團隊。  經歷： ** 共同創辦人 @Taiwan R User Group & MLDM Monday ** 常年在國內推廣與推動 quantmod & quantstrat 的使用與應用 ** 共同創辦人 @木刻思股份有限公司  ** 金融研訓院 R & quantmod & quantstrat 講師 ** Microsoft Data Platform Most Valuable Professionals ** PyCon APAC 2014 Speaker：Hacking Model with Python ** PyCon APAC 2014 Tutorial Session：PlaY data ** PyCon APAC 2015 Speaker：How to build a recommendation system with python ** PyCon APAC 2015 Tutorial Session：Prob. Modeling & Text Mining ** PyData SF 2016 Speaker (字串長度:973)'),(326,'2017-09-07 12:16:13',91,'me_desc---->Enjoy Information and Knowledge.<br><br> Been a<br><br>technical evangelist in Microsoft. (字串長度:90)---->Enjoy Information and Knowledge.   Been a  technical evangelist in Microsoft. (字串長度:78)'),(327,'2017-09-07 12:16:20',96,'desc---->What will happen to a young data scientist who just graduated from school after entering an industry? What is the difference between experiments and applications to solve real world problems using data science?<br><br>In this talk, I will share my journey from a new machine learning and data mining graduated student to a senior data engineer (Or sometimes, the data scientist). How to detect and define problems? How to understand the domain knowledge and combine it into data models? How to apply data science to solve them? How to ensure the data correctness during ETL process? How to evaluate the performance? Is there any better solution? Etc. Becoming a data science unicorn for one single person is difficult. How about a group of talents? To build a successful data product, cooperation with people from different functions is needed. In the end, I will share the experience working with different departments and how we achieve better result by leveraging knowledge from every domain. (字串長度:996)---->What will happen to a young data scientist who just graduated from school after entering an industry? What is the difference between experiments and applications to solve real world problems using data science?  In this talk, I will share my journey from a new machine learning and data mining graduated student to a senior data engineer (Or sometimes, the data scientist). How to detect and define problems? How to understand the domain knowledge and combine it into data models? How to apply data science to solve them? How to ensure the data correctness during ETL process? How to evaluate the performance? Is there any better solution? Etc. Becoming a data science unicorn for one single person is difficult. How about a group of talents? To build a successful data product, cooperation with people from different functions is needed. In the end, I will share the experience working with different departments and how we achieve better result by leveraging knowledge from every domain. (字串長度:990)'),(328,'2017-09-07 12:16:21',100,'desc---->The Cancer Moonshot aims to accelerate cancer research, make more therapies available to more patients, early detection and precision prevention.<br><br>This presentation will discuss the Cancer Moonshot and how emerging technologies, such as cloud computing and artificial intelligence, will contribute to this initiative.(字串長度:323)---->The Cancer Moonshot aims to accelerate cancer research, make more therapies available to more patients, early detection and precision prevention.  This presentation will discuss the Cancer Moonshot and how emerging technologies, such as cloud computing and artificial intelligence, will contribute to this initiative.(字串長度:317)```me_desc---->Dr. Steve Tsang is a Senior Biomedical Informaticist with Attain, LLC, working at the National Cancer Institute Center for Biomedical Informatics and Information Technology (NCI-CBIIT) on the evaluation of the NCI Cloud Resources.<br><br>He was trained at Johns Hopkins University and completed his dissertation on protein structural bioinformatics at the National Center for Biotechnology Information (NCBI, NLM).<br><br>Prior to joining NCI, he was a computational scientist at the Naval Medical Research Center (NMRC) and the National Institute of Allergy and Infectious Diseases (NIAID).<br><br>Dr. Tsang is currently an Adjunct Associate Professor in the Biology and Biotechnology Departments at the University of Maryland University College, and an adjunct faculty member with the Biology Department at Montgomery College. (字串長度:829)---->Dr. Steve Tsang is a Senior Biomedical Informaticist with Attain, LLC, working at the National Cancer Institute Center for Biomedical Informatics and Information Technology (NCI-CBIIT) on the evaluation of the NCI Cloud Resources.  He was trained at Johns Hopkins University and completed his dissertation on protein structural bioinformatics at the National Center for Biotechnology Information (NCBI, NLM).  Prior to joining NCI, he was a computational scientist at the Naval Medical Research Center (NMRC) and the National Institute of Allergy and Infectious Diseases (NIAID).  Dr. Tsang is currently an Adjunct Associate Professor in the Biology and Biotechnology Departments at the University of Maryland University College, and an adjunct faculty member with the Biology Department at Montgomery College. (字串長度:811)'),(329,'2017-09-08 02:56:26',3,'company---->騰學廣告(字串長度:12)---->TenMax騰學廣告科技(字串長度:24)'),(330,'2017-09-08 08:20:32',16,'conf---->DSK(字串長度:3)---->DS(字串長度:2)'),(331,'2017-09-08 08:20:32',17,'conf---->DS(字串長度:2)---->DSK(字串長度:3)'),(332,'2017-09-08 08:20:34',25,'conf---->AIK(字串長度:3)---->DS(字串長度:2)'),(333,'2017-09-08 08:20:34',27,'conf---->DS(字串長度:2)---->AIK(字串長度:3)'),(334,'2017-09-08 08:20:42',85,'conf---->DS(字串長度:2)---->AIK(字串長度:3)'),(335,'2017-09-08 08:20:42',86,'conf---->AIK(字串長度:3)---->DS(字串長度:2)'),(336,'2017-09-08 08:20:43',100,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(337,'2017-09-08 08:41:04',4,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(338,'2017-09-08 08:41:05',9,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(339,'2017-09-08 08:41:05',12,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(340,'2017-09-08 08:41:06',17,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(341,'2017-09-08 08:41:07',25,'conf---->AIK(字串長度:3)---->AI(字串長度:2)'),(342,'2017-09-08 08:41:07',26,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(343,'2017-09-08 08:41:07',30,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(344,'2017-09-08 08:41:07',31,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(345,'2017-09-08 08:41:08',37,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(346,'2017-09-08 08:41:09',41,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(347,'2017-09-08 08:41:09',42,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(348,'2017-09-08 08:41:09',43,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(349,'2017-09-08 08:41:09',44,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(350,'2017-09-08 08:41:09',47,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(351,'2017-09-08 08:41:09',48,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(352,'2017-09-08 08:41:09',49,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(353,'2017-09-08 08:41:10',50,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(354,'2017-09-08 08:41:10',51,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(355,'2017-09-08 08:41:10',52,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(356,'2017-09-08 08:41:10',53,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(357,'2017-09-08 08:41:10',55,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(358,'2017-09-08 08:41:10',57,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(359,'2017-09-08 08:41:10',58,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(360,'2017-09-08 08:41:11',61,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(361,'2017-09-08 08:41:12',63,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(362,'2017-09-08 08:41:12',64,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(363,'2017-09-08 08:41:13',67,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(364,'2017-09-08 08:41:13',69,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(365,'2017-09-08 08:41:13',70,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(366,'2017-09-08 08:41:13',72,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(367,'2017-09-08 08:41:14',74,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(368,'2017-09-08 08:41:14',77,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(369,'2017-09-08 08:41:14',78,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(370,'2017-09-08 08:41:14',80,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(371,'2017-09-08 08:41:14',81,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(372,'2017-09-08 08:41:15',83,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(373,'2017-09-08 08:41:15',84,'conf---->DS(字串長度:2)---->AIK(字串長度:3)'),(374,'2017-09-08 08:41:15',86,'conf---->AIK(字串長度:3)---->DS(字串長度:2)'),(375,'2017-09-08 08:41:16',89,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(376,'2017-09-08 08:41:16',90,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(377,'2017-09-08 08:41:16',92,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(378,'2017-09-08 08:41:16',97,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(379,'2017-09-08 08:41:17',98,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(380,'2017-09-08 08:41:18',99,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(381,'2017-09-08 08:41:18',101,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(382,'2017-09-08 08:41:18',102,'conf---->DS(字串長度:2)---->AI(字串長度:2)'),(383,'2017-09-12 02:51:52',3,'talk_title---->Data Pipeline Matters -- 以 Tracking Pixel 為例(字串長度:50)---->Data Pipeline Matters－以 Tracking Pixel 為例(字串長度:49)```company---->TenMax騰學廣告科技(字串長度:24)---->TenMax 騰學廣告科技(字串長度:25)'),(384,'2017-09-12 02:51:54',11,'talk_title---->AI、大數據、法資訊學--法律與判決中的資料科學(字串長度:64)---->AI、大數據、法資訊學－法律與判決中的資料科學(字串長度:65)'),(385,'2017-09-12 02:51:54',13,'talk_title---->Challenges and opportunities of confounding in data sciences: a case study of smoking and lung cancer mortality 干擾機制對資料科學帶來的挑戰與契機：以抽煙及肺癌存活的因果關係為例(字串長度:211)---->干擾機制對資料科學帶來的挑戰與契機：以抽煙及肺癌存活的因果關係為例(字串長度:99)'),(386,'2017-09-12 02:51:57',15,'talk_title---->政府數據分析與Google Sprint體驗設計(字串長度:46)---->政府數據分析與 Google Sprint 體驗設計(字串長度:48)'),(387,'2017-09-12 02:51:58',16,'talk_title---->Detecting rumors and fake news online(字串長度:37)---->Detecting Rumors and Fake News Online(字串長度:37)'),(388,'2017-09-12 02:52:03',22,'talk_title---->文字分析在財務上的應用-以股價預測為例(字串長度:55)---->文字分析在財務上的應用－以股價預測為例(字串長度:57)'),(389,'2017-09-12 02:52:04',23,'talk_title---->剖繪人類移動空間行為的地理科學 (中文)；Geospatial Intelligence: Profiling human moving behavior (English)(字串長度:123)---->剖繪人類移動空間行為的地理科學(字串長度:45)'),(390,'2017-09-12 02:52:04',24,'talk_title---->非均衡學習建物火災風險模型—以資料英雄計畫「高雄火災風險地圖2.0」為例(字串長度:102)---->非均衡學習建物火災風險模型－以資料英雄計畫「高雄火災風險地圖2.0」為例(字串長度:102)'),(391,'2017-09-12 02:52:05',27,'talk_title---->KKV data game - 17.06 1st place winner solution (字串長度:48)---->KKV Data Game: 17.06 1st Place Winner Solution (字串長度:47)'),(392,'2017-09-12 02:52:05',30,'talk_title---->電腦有freestyle嗎？談音樂分析與創作的人工智慧(字串長度:63)---->電腦有 freestyle 嗎？談音樂分析與創作的人工智慧(字串長度:65)'),(393,'2017-09-12 02:52:07',39,'talk_title---->AlphaGo - 深度學習與強化學習的勝利(字串長度:46)---->AlphaGo－深度學習與強化學習的勝利(字串長度:46)'),(394,'2017-09-12 02:52:08',40,'talk_title---->Learning and Using Statistics Liberally -- Exploratory and Elaborate Ways(字串長度:73)---->Learning and Using Statistics Liberally－Exploratory and Elaborate Ways(字串長度:72)'),(395,'2017-09-12 02:52:09',42,'talk_title---->Computational Design Thinking - 數據取向的產品設計思維與案例(字串長度:74)---->Computational Design Thinking－數據取向的產品設計思維與案例(字串長度:74)'),(396,'2017-09-12 02:52:09',43,'talk_title---->我在kaggle數海獅(字串長度:21)---->我在 Kaggle 數海獅(字串長度:23)'),(397,'2017-09-12 02:52:09',45,'talk_title---->深度學習與Kaggle實戰(字串長度:27)---->深度學習與 Kaggle 實戰(字串長度:29)'),(398,'2017-09-12 02:52:09',48,'talk_title---->理財?機器人?理財機器人?(字串長度:33)---->理財？機器人？理財機器人？(字串長度:39)'),(399,'2017-09-12 02:52:13',53,'talk_title---->台灣PM2.5短期趨勢預測(字串長度:29)---->台灣 PM2.5 短期趨勢預測(字串長度:31)'),(400,'2017-09-12 02:52:15',55,'talk_title---->善用Machine Learning及AWS serverless架構分析惡意檔案(字串長度:63)---->善用 Machine Learning 及 AWS serverless 架構分析惡意檔案(字串長度:67)'),(401,'2017-09-12 02:52:23',66,'talk_title---->Challenges ahead: being a unicorn in data science through problem solving(字串長度:73)---->Challenges Ahead: Being A Unicorn in Data Science through Problem Solving(字串長度:73)'),(402,'2017-09-12 02:52:24',67,'company---->國衛院群體健康科學研究所(字串長度:36)---->國家衛生研究院/台大醫院(字串長度:34)```me_desc---->林煜軒博士為國內少數兼具臨床、企業界資歷之精神科醫師。  近年投入網路心理學等多項跨領域研究：譯作《網路成癮：評估及治療指引手冊》榮獲國健署「優良健康讀物推介獎」、編製「智慧型手機成癮量表」，獲得各國學者要求授權翻譯為德、法、義大利、匈牙利、芬蘭、葡萄牙、土耳其、印度等多國語言版本的高度肯定。致力研發雲端服務的手機程式 (App)，獲得多項國內外專利。受邀編寫國際知名網路成癮教科書中「智慧型手機成癮」章節。現為衛生福利部「本國兒童青少年健康政策白皮書—3C產品使用」召集人。   林醫師經常於校園、社區、職場演講，積極建立產學合作平台，深耕社區健康促進，並協助研擬相關政策；推廣近年風行各大跨國企業的「員工協助計畫」(Employee Assistant Program)。(字串長度:935)---->林煜軒博士為國內少數兼具臨床、企業界資歷之精神科醫師。現職為國家衛生研究院助研究員級主治醫師、台大醫院主治醫師、台大醫學系助理教授。曾任輝瑞大藥廠(Pfizer)醫藥學術顧問、台北市電腦公會顧問。致力研發雲端服務的App「Know Addiction」，獲得多項國內外專利。受邀編寫國際知名網路成癮教科書中「智慧型手機成癮」章節。為衛生福利部「本國兒童青少年健康政策白皮書—3C 產品使用」召集人。(字串長度:541)'),(403,'2017-09-12 02:52:26',69,'talk_title---->均一教育平台，如何 -- 尤其在教育科技領域 -- 定好一個「資料題目」？(字串長度:95)---->均一教育平台，如何－尤其在教育科技領域－定好一個「資料題目」？(字串長度:93)'),(404,'2017-09-12 02:52:28',70,'talk_title---->不會寫程式的人友善上手機器學習-淺談Azure Machine Learning(字串長度:74)---->不會寫程式的人友善上手機器學習－淺談 Azure Machine Learning(字串長度:77)'),(405,'2017-09-12 02:52:34',77,'talk_title---->由同場 wush wu 遞交(字串長度:24)---->從時空的方向整合企業資料與開放資料(字串長度:51)```desc---->由同場 wush wu 遞交(字串長度:24)---->企業要運用開放資料之前，須先將企業資料與開放資料整合。而大部分與開放資料的資料整合，是透過時空的方向做整合。本次演講中，我們以實例說明時空資料的分類，並且提出實務上可行的方法來整合各種類別的時空資料。(字串長度:300)'),(406,'2017-09-12 02:52:35',78,'talk_title---->認知神經科學x人工智慧(字串長度:31)---->認知神經科學 x人工智慧(字串長度:32)'),(407,'2017-09-12 02:52:38',81,'talk_title---->ASAP比價嗶嗶鳥爬蟲的秘辛與其他電商資料分析案例分享(字串長度:73)---->ASAP 比價嗶嗶鳥爬蟲的秘辛與其他電商資料分析案例分享(字串長度:74)'),(408,'2017-09-12 02:52:38',82,'talk_title---->AI technologies for embedded devices(字串長度:36)---->AI Technologies for Embedded Devices(字串長度:36)'),(409,'2017-09-12 02:52:39',87,'talk_title---->Quantitative Trading from Zero to One :  from rule-based to artificial intelligence(字串長度:83)---->Quantitative Trading from Zero to One: From Rule-based to Artificial Intelligence(字串長度:81)'),(410,'2017-09-12 02:52:39',91,'talk_title---->運用Spark與電腦視覺科技協助瀕臨絕種的雪豹(字串長度:59)---->運用 Spark 與電腦視覺科技協助瀕臨絕種的雪豹(字串長度:61)'),(411,'2017-09-12 02:52:40',96,'talk_title---->The Journey To Become A Data Scientist - From School To Industry(字串長度:64)---->The Journey To Become A Data Scientist－From School To Industry(字串長度:64)'),(412,'2017-09-12 02:52:40',98,'talk_title---->Data Science on the Field — 資料科學如何影響運動產業？認識賽伯計量學(字串長度:90)---->Data Science on the Field－資料科學如何影響運動產業？認識賽伯計量學(字串長度:88)'),(413,'2017-09-12 02:52:41',101,'talk_title---->AlphaGo - 深度學習與強化學習的勝利(字串長度:46)---->AlphaGo－深度學習與強化學習的勝利(字串長度:46)'),(414,'2017-09-12 02:52:41',103,'talk_title---->Agri-Weather：阿龜的微氣候資料科學(字串長度:45)---->Agri-Weather: 阿龜的微氣候資料科學(字串長度:44)'),(415,'2017-09-13 03:39:33',79,'talk_title---->TBA(字串長度:3)---->協助製造業邁向智慧製造之路(字串長度:39)```company---->中央研究院資訊科學研究所(字串長度:36)---->中研院 / 資訊所(字串長度:21)```desc---->TBA(字串長度:3)---->近年來深度學習在學界及部分行業的應用上十分火熱, 然而台灣在這一波席捲全世界的人工智慧浪潮中, 到底能扮演什麼樣的角色以及有怎樣的機會? 我將與大家分享幾個在與製造業合作的過程中常見的問題、我們所看到的機會、遇到的挫折與困境以及一些對未來的展望。(字串長度:357)'),(416,'2017-09-14 02:19:52',79,'talk_title---->協助製造業邁向智慧製造之路(字串長度:39)---->兩位跨域者的深度學習之路(字串長度:36)'),(417,'2017-09-14 02:19:53',94,'talk_title---->轉換領域進入資料科學的心路歷程(字串長度:45)---->兩位跨域者的深度學習之路(字串長度:36)'),(418,'2017-09-14 02:24:28',118,'talk_title---->同 Jeff 郭建甫(字串長度:18)---->Project Mynah 九官鳥計畫：AI 幫你接電話(字串長度:50)```desc---->同 Jeff 郭建甫(字串長度:18)---->whoscall 如何從既有的累積出發再次創新，推出貼近人性需求的新產品。如何從來電辨識與過濾的軟體，走向電話線裡的人工智慧服務; 如何從消費者端的產品，找出商業上的痛點; 如何克服科技上的突兀，找到人性上的解法。此場演講中，將第一次公開分享 gogolook 在內部創新的新專案：Mynah，一個讓 AI 幫你接電話服務。(字串長度:422)'),(419,'2017-09-14 07:45:31',1,'website---->http://ma@cs.ucdavis.edu(字串長度:24)---->http://www.cs.ucdavis.edu/~ma(字串長度:29)'),(420,'2017-09-14 09:56:26',31,'position---->魏澤人(字串長度:9)---->副教授(字串長度:9)'),(421,'2017-09-14 09:56:35',61,'position---->無(字串長度:3)---->創業家(字串長度:9)'),(422,'2017-09-14 09:56:36',72,'position---->Peter Yeh(字串長度:9)---->(字串長度:0)'),(423,'2017-09-15 06:44:23',36,'position---->教授(字串長度:6)---->副教授(字串長度:9)'),(424,'2017-09-15 06:44:25',50,'name2---->NNNN(字串長度:4)---->周世恩(字串長度:9)'),(425,'2017-09-15 06:44:25',58,'position---->老師(字串長度:6)---->副教授(字串長度:9)'),(426,'2017-09-15 06:44:27',79,'company---->中研院 / 資訊所(字串長度:21)---->中央研究院資訊科學研究所(字串長度:36)'),(427,'2017-09-15 06:44:29',111,'position---->教授/Professor(字串長度:16)---->教授(字串長度:6)'),(428,'2017-09-15 06:44:29',112,'company---->D4SG/EMOC(字串長度:9)---->D4SG / EMOC(字串長度:11)'),(429,'2017-09-15 06:44:30',113,'company---->逢甲大學(字串長度:12)---->逢甲大學資工系(字串長度:21)'),(430,'2017-09-15 06:44:30',114,'company---->台大經濟系(字串長度:15)---->台灣大學經濟系(字串長度:21)'),(431,'2017-09-15 06:44:30',121,'company---->香港中文大學, The Chinese University of Hong Kong(字串長度:55)---->香港中文大學(字串長度:18)'),(432,'2017-09-15 06:44:31',123,'company---->清大資工(字串長度:12)---->清華大學資訊工程系(字串長度:27)'),(433,'2017-09-19 03:52:31',28,'talk_title---->實現資料價值的阻礙不在技術(字串長度:39)---->用深度學習解決醫療尚未滿足的需求(字串長度:48)```desc---->發展 Data Science，最困難的往往不是Data，而是Science，也就如何透過有趣且重要的問題來引導資料的收集、分析，逐步建構背後的知識與行動。本次將分享在這個過程中可以上手的方法及對應的案例。(字串長度:261)---->Deep learning在醫療領域有很大的潛力，有機會為過去的一些難題提供新希望，然而要能創造出市場價值必須要從臨床情境出發，聚焦在真正值得被解決的問題，常見的盲點便是誤把技術上的突破與臨床效用畫上等號。本演講適合給有志於開發醫療機器學習應用的臨床人員與工程人員參考。(字串長度:379)```me_desc---->吳沛燊，臺大醫院復健科總醫師，均一教育平台 Data Scientist，協助建立data driven的文化，透過各種管道自學以逐步累積實戰經驗。(字串長度:170)---->吳沛燊醫師，臺大醫院復健科，均一教育平台 Data Scientist，協助建立 data driven 的文化，2016年於首屆NTU LIVE以「針對吞嚥困難病人設計的整合偵測服務」獲獎，2016年首屆台電黑客松以「生活區機能與用電關係」獲獎，2017年台灣醫學資訊學會「火爆區塊鏈、醫療新關鍵」黑客松以「區塊鏈解決病患補助與捐款問題」獲獎。擔任多家公司的外部顧問，平日喜好自主學習，主張讓「做中學，著手解決手上問題」遍地開花(字串長度:549)'),(434,'2017-09-19 03:52:32',48,'me_desc---->張智傑博士，於2012年取得博士學位，目前任職於富邦創新科技辦公室，主要負責富邦集團大數據/資料科學/人工智慧發展整合及規劃。(字串長度:177)---->張智傑博士，2009~2012於成大取得管理科學博士學位，並於2012~2014在中研院資創中心擔任博士後研究員。2015年加入富邦集團，目前任職於富邦創新科技辦公室，主要負責富邦集團(金融、電信及電商)大數據/資料科學/人工智慧創新發展及策略應用。(字串長度:323)'),(435,'2017-09-19 06:50:37',129,'talk_title---->Realtime- Cognitive IoT using DeepLearning and Online Learning on top of ApacheSpark and Spark enabled DL frameworks like DeepLearning4J, ApacheSystemML and TensorFlow in Spark(字串長度:176)---->Realtime-Cognitive IoT using Deep Learning and Online Learning(字串長度:62)'),(436,'2017-09-21 11:53:55',79,'desc---->近年來深度學習在學界及部分行業的應用上十分火熱, 然而台灣在這一波席捲全世界的人工智慧浪潮中, 到底能扮演什麼樣的角色以及有怎樣的機會? 我將與大家分享幾個在與製造業合作的過程中常見的問題、我們所看到的機會、遇到的挫折與困境以及一些對未來的展望。(字串長度:357)---->不是本科系畢業的，想踏入資料科學領域該怎麼做? \r 深度學習好像很厲害，要怎麼學習?\r \r 兩位跨域者將與大家分享在資料洞察實驗室的深度學習之路!\r 從土木、心理進入到資料科學世界，從 Excel, Matlab，變為用 R, Python 處理各式各樣的機器學習、深度學習問題。透過自身的專案經驗分享，\r 提供想進入資料科學領域的朋友們一些經驗分享，包括\r \r 1. R vs. Python 我到底該學那個?\r 一位先學 R，另一位學 Python，當 R 遇上 Python 會碰出甚麼火花呢?\r 2. 我只有基本的機器學習概念，要怎麼完成一個機器學習的專案?\r 分享我們在機器學習專案中，從資料中洞察出許多有趣的真相\r 3. 深度學習很火紅，有甚麼東西能夠應用?\r 介紹深度學習的基本原理，以及許多最新的應用\r \r 資料科學的領域除了基本的技術與觀念，創造力與想像力也非常重要，希望透過這些經驗的分享，讓許多不同領域的朋友與我們一起加入資料科學的世界!(字串長度:1082)'),(437,'2017-09-21 11:53:56',94,'desc---->從一開始只會拉 excel，到現在需要用 R, Python 處理各式各樣的資料科學問題，希望透過自身的專案經驗分享，提供想進入資料科學領域的朋友們一些建議，包括該選擇哪個程式語言，如何從無到有建立一個機器學習的問題，深度學習好像很火紅，那是甚麼?希望透過這些經驗分享，讓想進入資料科學的朋友們能夠更快進入狀況，與我們一同加入資料科學的世界!(字串長度:475)---->不是本科系畢業的，想踏入資料科學領域該怎麼做? \r 深度學習好像很厲害，要怎麼學習?\r \r 兩位跨域者將與大家分享在資料洞察實驗室的深度學習之路!\r 從土木、心理進入到資料科學世界，從 Excel, Matlab，變為用 R, Python 處理各式各樣的機器學習、深度學習問題。透過自身的專案經驗分享，\r 提供想進入資料科學領域的朋友們一些經驗分享，包括\r \r 1. R vs. Python 我到底該學那個?\r 一位先學 R，另一位學 Python，當 R 遇上 Python 會碰出甚麼火花呢?\r 2. 我只有基本的機器學習概念，要怎麼完成一個機器學習的專案?\r 分享我們在機器學習專案中，從資料中洞察出許多有趣的真相\r 3. 深度學習很火紅，有甚麼東西能夠應用?\r 介紹深度學習的基本原理，以及許多最新的應用\r \r 資料科學的領域除了基本的技術與觀念，創造力與想像力也非常重要，希望透過這些經驗的分享，讓許多不同領域的朋友與我們一起加入資料科學的世界!(字串長度:1082)'),(438,'2017-09-22 10:07:26',5,'talk_title---->TBA(字串長度:3)---->社群大數據中的江湖事(字串長度:30)```desc---->TBA(字串長度:3)---->有人的地方，就有江湖；在江湖，就免不了有麻煩事；麻煩事，就需要社群網路分析，既作江湖人，便履江湖土，守江湖道，管江湖事。這場盛會將和大家交流分享，進階版的社群大數據之輿情觀測與分析應用。(字串長度:279)'),(439,'2017-09-22 10:07:28',33,'me_desc---->Yu-Shuen Wang is an Associate professor of the Department of Computer Science at National Chiao-Tung University. He received his PhD degree from Visual System Laboratory, National Cheng Kung University, Tainan, Taiwan, ROC, in 2010. His research interests include Computer Graphics, Visual Media Retargeting, Image Manipulation, Focus+Context Visualization and Skeletonization. (字串長度:378)---->Yu-Shuen Wang is an Associate professor of the Department of Computer Science at National Chiao-Tung University. He received his PhD degree from Visual System Laboratory, National Cheng Kung University, Tainan, Taiwan, ROC, in 2010. His research interests include Computer Graphics, Image Manipulation, Focus+Context Visualization and Map Visualization. (字串長度:354)'),(440,'2017-09-22 10:07:28',35,'desc----> Large-scale network data analysis has emerged into one of the most important tools in th big data regime for almost all scientists and practitioners. Among all characteristics and properties, community and centrality are two major components for a detailed understanding of a large-scale network. Unfortunately, traditional methods are either computationally infeasible for large-scale network, or without statistical verification and inference. This talk introduces a SOP for accurate and efficient analysis of large-scale network data. It consists of four main steps. First, a screening stage is proposed to roughly partition the whole network into communities via complement graph coloring. Then a likelihood-based statistical test is introduced to test for the significance of the detected communities. Once these significant communities are detected, another likelihood-based statistical test is introduced to check for the focus centrality of each community. Finally, a metaheuristic swarm intelligence based (SIB) method is proposed to fine tune the range of each community from its original circular setting. Our proposed SOP is demonstrated in several real-life data, showing how this method can provide extra suggestions from the data.(字串長度:1246)---->Large-scale network data analysis has emerged into one of the most important tools in th big data regime for almost all scientists and practitioners. Among all characteristics and properties, community and centrality are two major components for a detailed understanding of a large-scale network. Unfortunately, traditional methods are either computationally infeasible for large-scale network, or without statistical verification and inference. This talk introduces a SOP for accurate and efficient analysis of large-scale network data. It consists of four main steps. First, a screening stage is proposed to roughly partition the whole network into communities via complement graph coloring. Then a likelihood-based statistical test is introduced to test for the significance of the detected communities. Once these significant communities are detected, another likelihood-based statistical test is introduced to check for the focus centrality of each community. Finally, a metaheuristic swarm intelligence based (SIB) method is proposed to fine tune the range of each community from its original circular setting. Our proposed SOP is demonstrated in several real-life data, showing how this method can provide extra suggestions from the data.(字串長度:1245)```me_desc---->Frederick Kin Hing Phoa, Ph.D: He has been an Associate Research Fellow of the Institute of Statistical Science at Academia Sinica (ISSAS) since 2013. He received his B.S. Physical Chemistry, B.S. Applied Mathematics, M.S. Statistics and Ph.D. Statistics from University of California at Los Angeles (UCLA) in 2001, 2002, 2006 and 2009 respectively. He was an Assistant Research Fellow of the ISSAS in 2009-2013. He conducted the Excellent Young Researcher Research Project supported by Ministry of Science and Technology (MOST) during 2013-2016. He received the Career Development Award from Academia Sinica in 2014. He received the Ta-You Wu Memorial Award (Young Researcher Award) from MOST. He received the best paper award in the World Congress of Engineering in 2015. He received an International Cost-Share Exchange Scheme funded by MOST and Royal Society of UK during 2016-2018. In the past eight years (2009-2017), he has published 44 papers in the areas of design and analysis of experiments, network data analysis, nature-inspired metaheuristic optimization and many others. In addition, he has given 88 invited talks in the international conferences and 59 seminar talks in the universities around the world. He organized two conferences of experimental design and analysis (CEDA) in 2014 and 2016. His international collaborations cover research groups in USA, UK, Canada, Japan, China, Hong Kong, and many other countries.(字串長度:1436)---->Frederick Kin Hing Phoa, Ph.D: He has been an Associate Research Fellow of the Institute of Statistical Science at Academia Sinica (ISSAS) since 2013. He received his B.S. Physical Chemistry, B.S. Applied Mathematics, M.S. Statistics and Ph.D. Statistics from University of California at Los Angeles (UCLA) in 2001, 2002, 2006 and 2009 respectively. He was an Assistant Research Fellow of the ISSAS in 2009-2013. His research interests include design and analysis of experiments, network data analysis, nature-inspired metaheuristic optimization, big data analytics and many others. He conducted the Excellent Young Researcher Research Project supported by Ministry of Science and Technology (MOST) during 2013-2016. He received the Career Development Award from Academia Sinica in 2014. He received the Ta-You Wu Memorial Award (Young Researcher Award) from MOST. He received the best paper award in the World Congress of Engineering in 2015. He received an International Cost-Share Exchange Scheme funded by MOST and Royal Society of UK during 2016-2018. He organized two conferences of experimental design and analysis (CEDA) in 2014 and 2016. His international collaborations cover research groups in USA, UK, Canada, Japan, China, Hong Kong, and many other countries.(字串長度:1272)'),(441,'2017-09-22 10:07:28',41,'me_desc---->杜書儒在長庚大學醫學影像暨放射科學系擔任副教授。在美國Indiana 州West Lafayette 的Purdue University拿到物理學博士學位，博士論文是利用蒙地卡羅與平行高速電腦運算的方法研究中子星的能量問題。之後在Texas 州Houston的 The University of Texas MD Anderson Cancer Center 擔任博士後研究員。研究方向與興趣包括近代醫學物理、醫學影像特徵分析、機器學習、電腦斷層掃描、磁共振影像、與放射治療。教授課程包括近代醫學物理專題與特論、放射物理學、放射物理學實驗、應用數學、機器學習、醫學影像特論、與進階近代物理。平常的喜好包括美食、游泳、與爬山。(字串長度:734)---->杜書儒在長庚大學醫學影像暨放射科學系擔任副教授。在美國Indiana 州West Lafayette 的Purdue University拿到物理學博士學位，博士論文是利用蒙地卡羅與平行高速電腦運算的方法研究中子星的能量問題。之後在Texas 州Houston的 The University of Texas MD Anderson Cancer Center 擔任博士後研究員。研究方向與興趣包括近代醫學物理、醫學影像特徵分析、機器學習、電腦斷層掃描、磁共振影像、與放射治療。教授課程包括近代醫學物理專題與特論、放射物理學、放射物理學實驗、應用數學、機器學習、醫學影像特論、與進階近代物理。平常的喜好包括吃美食、游泳、與爬山。(字串長度:737)'),(442,'2017-09-22 10:07:28',42,'position---->助理教授(字串長度:12)---->教授(字串長度:6)```desc---->「設計思考」是當代設計領域的一門顯學，其提供了各種的設計方法幫助設計師以一個較為客觀及理性的流程推動需求分析、產品發展、測試驗證等各個設計活動。然而，多數的設計思考方法本身仍由設計師的經驗進行主觀分析及判斷。本課程的主題「運算式的設計思考(Computational Design Thinking」在強調如何架構及運用科學化的工具，獲取產品相關的數據，協助設計師可以更即時、客觀的掌握產品與用戶之間的互動關係，以檢視及評估設計成效，進行設計的優化。透過講者自身發展產品的案例解析，分享數據協助產品設計之經驗與心得。(字串長度:714)---->「設計思考」是當代設計領域的一門顯學，其提供了各種的設計方法幫助設計師以一個較為客觀及理性的流程推動需求分析、產品發展、測試驗證等各個設計活動。然而，多數的設計思考方法本身仍由設計師的經驗進行主觀分析及判斷。本次分享的主題「運算式的設計思考(Computational Design Thinking」在強調如何架構及運用科學化的工具，獲取產品相關的數據，協助設計師可以更即時、客觀的掌握產品與用戶之間的互動關係，以檢視及評估設計成效，進行設計的優化。透過講者自身發展產品的案例解析，分享數據協助產品設計之經驗與心得。(字串長度:717)'),(443,'2017-09-22 10:07:30',72,'talk_title---->My Health Keeper(字串長度:16)---->Try Again, Fail Better.  深度學習，從0到1的成長過程。(字串長度:66)```company---->講師(字串長度:6)---->台大CS+X讀書會(字串長度:19)```position---->(字串長度:0)---->共同創辦人(字串長度:15)```desc---->In this talk, we will introduce some deep learning methods we use to track the daily nutrition intake. We will focus on the application and algorithm parts.(字串長度:156)---->本次的演講適合正在思索如何將機器學習的方法用在日常生活中的朋友，以及在實作過程中面對各種困難的朋友。分享將著重在AAAD+的架構上。期待能和大家一起交流，相信每一個人都可以將人工智慧與資料科學的方法，帶到生活中，協助我們解決問題！  Design: 我們如何從生活出發，找到我們想做的題目，發現問題後，找到深度學習中圖像處理技術，解決所面對的問題。 Algorithm: 深度學習中各種圖像處理的數學方法介紹。 Architeture: 我們如何使用程式工具，將深度學習的方法實作出來。 Application: 分享演算法與程式工具的應用。本次將分享我們在藝術領域與健康領域的應用。(字串長度:766)```me_desc---->我希望能將資料科學與人工智慧的工具，應用在解決是常生活的問題上。(字串長度:96)---->葉宇心，台灣大學地理環境資源學系畢業。在從事社會創新實驗的過程中，進入了機器學習與資料科學的世界，應用了圖像處理的技術，嘗試解決我們所遇到的問題，同時也參與了讀書會社群的草創，研讀許多機器學習與資料科學背後的數學方法，一步步克服遭遇的困難與恐懼。我相信數學方法與程式工具，可以協助我們解決許多關心的議題，使我們的生活更豐富，期待和大家分享這些過程，以及我們的應用！(字串長度:546)'),(444,'2017-09-22 10:07:31',94,'desc---->不是本科系畢業的，想踏入資料科學領域該怎麼做? \r\n 深度學習好像很厲害，要怎麼學習?\r\n \r\n 兩位跨域者將與大家分享在資料洞察實驗室的深度學習之路!\r\n 從土木、心理進入到資料科學世界，從 Excel, Matlab，變為用 R, Python 處理各式各樣的機器學習、深度學習問題。透過自身的專案經驗分享，\r\n 提供想進入資料科學領域的朋友們一些經驗分享，包括\r\n \r\n 1. R vs. Python 我到底該學那個?\r\n 一位先學 R，另一位學 Python，當 R 遇上 Python 會碰出甚麼火花呢?\r\n 2. 我只有基本的機器學習概念，要怎麼完成一個機器學習的專案?\r\n 分享我們在機器學習專案中，從資料中洞察出許多有趣的真相\r\n 3. 深度學習很火紅，有甚麼東西能夠應用?\r\n 介紹深度學習的基本原理，以及許多最新的應用\r\n \r\n 資料科學的領域除了基本的技術與觀念，創造力與想像力也非常重要，希望透過這些經驗的分享，讓許多不同領域的朋友與我們一起加入資料科學的世界!(字串長度:1096)---->不是本科系畢業的，想踏入資料科學領域該怎麼做? \r 深度學習好像很厲害，要怎麼學習?\r \r 兩位跨域者將與大家分享在資料洞察實驗室的深度學習之路!\r 從土木、心理進入到資料科學世界，從 Excel, Matlab，變為用 R, Python 處理各式各樣的機器學習、深度學習問題。透過自身的專案經驗分享，\r 提供想進入資料科學領域的朋友們一些經驗分享，包括\r \r 1. R vs. Python 我到底該學那個?\r 一位先學 R，另一位學 Python，當 R 遇上 Python 會碰出甚麼火花呢?\r 2. 我只有基本的機器學習概念，要怎麼完成一個機器學習的專案?\r 分享我們在機器學習專案中，從資料中洞察出許多有趣的真相\r 3. 深度學習很火紅，有甚麼東西能夠應用?\r 介紹深度學習的基本原理，以及許多最新的應用\r \r 資料科學的領域除了基本的技術與觀念，創造力與想像力也非常重要，希望透過這些經驗的分享，讓許多不同領域的朋友與我們一起加入資料科學的世界!(字串長度:1082)'),(445,'2017-09-22 10:07:33',117,'talk_title---->待確認(字串長度:9)---->人工智慧與供應鏈管理(字串長度:30)'),(446,'2017-09-26 02:46:27',29,'desc---->補(字串長度:3)---->(字串長度:0)```me_desc---->補(字串長度:3)---->(字串長度:0)'),(447,'2017-09-26 02:46:29',47,'desc---->TBD(字串長度:3)---->(字串長度:0)'),(448,'2017-09-26 02:46:29',49,'me_desc---->（請先延用去年的資料好嗎，謝謝）(字串長度:48)---->(字串長度:0)'),(449,'2017-09-26 02:46:30',63,'desc---->Coming soon(字串長度:11)---->(字串長度:0)```me_desc---->Coming soon(字串長度:11)---->(字串長度:0)'),(450,'2017-09-26 02:46:31',77,'me_desc---->後補。(字串長度:9)---->(字串長度:0)'),(451,'2017-09-26 02:46:32',99,'desc---->(Provided later)(字串長度:16)---->(字串長度:0)```me_desc---->(Provided later)(字串長度:16)---->(字串長度:0)'),(452,'2017-09-26 02:46:32',105,'company---->台灣大學電機工程系(字串長度:27)---->國立台灣大學(字串長度:18)```desc---->TBA(字串長度:3)---->凱吉哥真的是凱吉哥？人臉偵測與辨識從古至今一直是電腦視覺領域中非常熱門的題目，而解析人臉的第一步即為找出各部件 (眼睛、鼻子、嘴巴) 的位置，再更進一步解析其外型，利用數個關鍵點來代表各種不同的外型，這些關鍵點無論在哪個臉部部件中一般被統稱為\"臉部特徵點\" (facial landmarks)。臉部特徵點的應用繁多，從頭部姿勢辨識、虛擬化妝、至臉部外型交換，今年 iPhone X 甚至利用 FaceID 臉部辨識技術來進行安全認證，解除手持式裝置的安全鎖。講者將在本演講中，介紹近幾年如何利用機器學習與深度學習的方式在影像中尋找臉部特徵點的位置，進而介紹如何利用機器學習模型來改變人臉的外型。(字串長度:808)'),(453,'2017-09-26 02:46:33',117,'desc---->待確認(字串長度:9)---->(字串長度:0)'),(454,'2017-09-26 02:46:33',121,'desc---->TBD(字串長度:3)---->(字串長度:0)'),(455,'2017-09-26 02:46:34',127,'desc---->TBA(字串長度:3)---->(字串長度:0)'),(456,'2017-09-29 03:57:48',15,'me_desc---->喜歡體驗設計、愛好說故事、一星期看七本書(字串長度:60)---->擅長以體驗設計挖掘人類洞見，運用數據分析支撐決策依據(字串長度:78)'),(457,'2017-09-29 03:57:49',29,'desc---->(字串長度:0)---->以巨觀生態學資料分析為例，探討由資料分析到理論模式建立，發展新的巨觀生態學理論的過程。(字串長度:129)```me_desc---->(字串長度:0)---->中央研究院生物多樣性研究中心 副研究員， 臺灣大學生態與演化研究所兼任 副教授 臺灣大學氣候變遷與永續發展學位學程、台灣師範大學 合聘副教授  美國康乃爾大學動物行為學博士，英國劍橋大學博士後研究、訪問學者。 主要研究領域為演化生物學、社會行為學與生態學，專長結合數學模式、理論與實 際生物進行整合性研究。2010 年回臺進入中研院服務後，也開始應用演化生物學了解巨觀生態學(macroecology)問題，例如環境變異程度對物種分布範圍與界線的影響。目前 以臺灣的冠羽畫眉、尼泊爾埋葬蟲，以及肯亞的灰頭織巢鳥(grey-capped social weaver)，作為主要的野外研究題材。榮獲獎勵包含2012年獲中研院前瞻計畫、2014 年中央研究院年輕學者研究著作獎、2016年吳大猷先生紀念獎與2017中研院深耕計畫。 (字串長度:941)'),(458,'2017-09-29 03:57:50',49,'position---->教授 / Smilepoll.tw 平台主持人(字串長度:37)---->政治學教授 / smilepoll.tw 平台主持人(字串長度:46)```me_desc---->(字串長度:0)---->我是網路調查資料平台smilepoll.tw的主持人、Facebook粉絲頁「資料吼」的發起人，也是中山大學政研所的教授。我從事的是個人層次政治現象的觀察與研究，使用的資料多是民意調查資料。我的研究興趣在方法論、國家認同、政黨認同、政治偏好與行為。我在教學與研究上使用R，並儘量用最淺白的語言幫助社科學子認識這個語言工具。(字串長度:441)'),(459,'2017-09-29 03:57:52',77,'me_desc---->(字串長度:0)---->李慕約 muyueh@muyueh.com 李慕約公司的共同創辦人之一。  為什麼沒辦法用資料？其他專家覺得資料不夠多，機器不夠大，或演算法不夠先進。  我覺得瓶頸在人吸取資料的速度很慢，像是用吸管吸乾水庫。  李慕約公司在解決吸管的問題，目標是幫助人類更有效的看到資料、跟資料互動。為此運用包含：資料視覺化、機器人自動化、機器學習 / 人工智慧等技術。  李慕約公司的客戶包含：WHO、三大電信、銀行以及現任總統。受 Google、The World Bank 邀請授課，並獲多項國際獎項肯定。 (字串長度:635)```website---->(字串長度:0)---->http://muyueh.com(字串長度:17)'),(460,'2017-09-29 03:57:55',121,'talk_title---->Trends in eLearning and Education Analytics(字串長度:43)---->How Do People Prefer To Learn? eLearning Trends and Predictions 2017 人們喜歡如何學習？２０１７年電子學習趨勢與預測(字串長度:138)```desc---->(字串長度:0)---->E-learning is now indispensable as a mean for education. In particular, with the advent of Massive Open Online Course (MOOC), University Open Online Course (UOOC), Small Personal Online Course (SPOC), flipped classroom, etc., these novel online courses are providing quality education to the masses, and are fueling a transformative change with the use of technology. In this talk, I plan to chart out the current trends in e-learning, including learning habits of users, popularities of courses, and its impact on career development. We will also examine some learning analytics technologies that are being used to support e-learning. With these technologies, we will be able to help the educators to deliver better learning experiences in e-learning platforms, e.g., personalizing the learning sequence for different learners, tracking people’s attention, etc.  以電子學習作為教育的手段是不可或缺的。 特別是隨著大眾開放在線課程（MOOC），大學開放在線課程（UOOC），小型個人在線課程（SPOC），翻譯教室等的出現，這些新穎的網絡課程正在向群眾提供優質教育，並通過技術推動了變革。這個演講將會介紹當前電子學習的趨勢，包括用戶的學習習慣，課程的流行度以及對職業發展的貢獻。我們還探討一些用於支持電子學習的數據分析技術。 借助這些技術，我們將能夠幫助教育者在電子學習平台上提供更好的學習體驗，例如個性化不同學習者的學習順序，跟踪用戶的注意力等。(字串長度:1561)'),(461,'2017-10-11 04:45:25',42,'talk_title---->Computational Design Thinking－數據取向的產品設計思維與案例(字串長度:74)---->Computational Design Thinking - 數據取向的產品設計思維與案例(字串長度:74)```position---->教授(字串長度:6)---->助理教授(字串長度:12)'),(462,'2017-10-11 04:45:25',47,'talk_title---->TBD(字串長度:3)---->人工智慧運算系統的現況與展望(字串長度:42)```desc---->(字串長度:0)---->類神經網路的訓練與推論，近幾年已成為平行運算裡最重要的成長契機。由繪圖晶片 GPU 領軍的跳躍性成長，其背後有著二十年的積累。依據資料中心需求設計的 Google TPU，以及可程式化的 FPGA 晶片，同時也加速了產業的變革與進展。行動運算的進步，更促成了人工智慧終端的開發。在這個演講裡，我會試著探討這些方向的共通性與差異性，並介紹我們自行開發之高效能運算架構與影像處理之算法，以協助聽眾理解、預期未來之運算趨勢。(字串長度:583)'),(463,'2017-10-11 04:45:29',99,'desc---->(字串長度:0)---->如果說 bot 是人類實現 AI 應用最直接也是最生活化的管道，那麼 bot framework 就是讓這些 AI 應用得以實現的最重要武器。  Bot (aka Chatbot) 在社群網路、行動裝置全面走進人類生活的今日扮演著「產業智能化」的一個關鍵角色 — 透過自然語意的理解，它成為產業及其用戶溝通的智能客服；在收集用戶的對話記錄、瀏覽行為之後，它得以在對話中推薦最適合的商品；串接了 speech, image/video, 以及各種人機介面的 cognitive services 之後，它的應用甚至可以延伸到如智慧家庭、商辦、工廠、醫院… 等場域。  Bottender 是我們提出、建造的一個開源 bot 建置平台，它提供了模組化的開發環境讓開發者得以快速地串接各種群通訊平台以及網路/雲端架構，而極富彈性的事件處理 APIs 讓複雜的 bot 邏輯得以輕易地實現，並且串接各式各樣的 AI 技術，至於它的各種 session store 機制則讓 bot 可以在各種情境下進行優化，最後，它也提供了整合測試的環境讓系統情境測試自動化得以實現。(字串長度:1148)```me_desc---->(字串長度:0)---->黃鐘揚(Ric Huang)有著不安於室、不甘平凡的個性，除了念博班的時候就參與了一段矽谷科技公司的創業歷程之外，剛回國任教的幾年應該是要乖乖寫論文的時候卻又滿腔熱血的替台大創立的創意創業學程以及 NTU Garage 等，而才剛卸下各種創創的職務就揪集了一群台大校友創立了優拓資訊股份有限公司。  優拓資訊集結了資料科學家、系統工程師、商業分析師等等人才，目前專注在提供智能商務機器人的完整服務，本次 AI/資料科學年會預計分享他們自行開發的中文語意理解引擎以及支援多平台、多功能的開源 bot framework 計畫。(字串長度:698)'),(464,'2017-10-11 04:54:48',64,'talk_title---->人工智慧的現在與未來(字串長度:30)---->人工智慧第三波革命(字串長度:27)'),(465,'2017-10-11 04:56:14',64,'desc---->本演講將闡述人工智慧目前與未來做得到與做不到的事情 (字串長度:76)---->人工智慧演進至今已經邁入第三波。本演講將闡述第三波人工智慧在看什麼樣的問題？尋求什麼樣的技術？產生什麼樣的影響？講者也將討論未來人工智慧的走向，以及何謂目前人工智慧的藍海。(字串長度:258)'),(466,'2017-10-12 05:34:25',134,'talk_title---->(字串長度:0)---->Structure Learning in Deep Learning(字串長度:35)```desc---->(字串長度:0)---->The goal of data science is to extract insights from unstructured and complex data. This often hinges on the use of a good representation where suitable features can simplify tremendously the extraction of knowledge. Traditionally, features were handcrafted based on domain knowledge. However, recent advances in deep learning have shown that it is often better to use a deep structure in which features are automatically learned from data. This has completely revolutionized computer vision, speech recognition and natural language. That being said, feature engineering is now replaced by architecture engineering since practitioners spend enormous time adjusting the architecture and hyperparameters by trial and error. Hence there is a need for techniques to automatically learn the structure and hyperparameters of networks.  In this talk, I will show how to automatically learn the structure of a special class of deep neural networks known as sum-product networks from streaming data.  This will be demonstrated in variety of domains where it is unclear what architecture might work well.(字串長度:1094)'),(467,'2017-10-17 00:05:01',27,'talk_title---->KKV Data Game: 17.06 1st Place Winner Solution (字串長度:47)---->從資料競賽看應用實務 - KKV data game 17.06 1st place winner solution(字串長度:78)```desc---->分享自己在今年 (2017) 6月參加 kkv data game 獲得第1名的心路歷程。內容包含解題套路，算法架構，以及在實踐過程所面臨的各種困難與最後的解法。(字串長度:193)---->分享自己在今年 (2017) 6月參加 kkv data game 獲得第1名的心路歷程。內容包含解題套路，算法架構，以及在實踐過程所面臨的各種困難與最後的解法。且同時藉由資料比賽的過程，結合自己在將資料科學應用在實務面時的經驗，嘗試提供應注意的細節。(字串長度:328)'),(468,'2017-10-31 03:27:24',16,'desc---->Social platforms are an ideal place for spreading rumors and fake news. As more people seek information and read news online, automatically debunking such false claims has become an urgent problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand their propagation processes. In detecting rumors and fake news in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early detection. I will also highlight the latest detection studies with deep learning techniques.   Short Bio Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review\'s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:1552)---->Social platforms are an ideal place for spreading rumors and fake news. As more people seek information and read news online, automatically debunking such false claims has become an urgent problem. Recent years have seen great advances in data-driven rumor research. This talk will review some of its major developments, including how a comprehensive set of user, structural, linguistic, and temporal features help us better understand their propagation processes. In detecting rumors and fake news in the wild, time becomes a critical factor. This talk will present how the significance of features changes by time and which features are prominent for early detection. I will also highlight the latest detection studies with deep learning techniques.   Short Bio Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:1554)```me_desc---->Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review\'s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:788)---->Meeyoung Cha is an associate professor at School of Computing in KAIST. Her research interests are in the analysis of complex network systems including online social networks with emphasis the spread of information, moods, and user influence. She received the best paper awards at ACM IMC 2007 for analyzing long-tail videos in YouTube and at ICWSM 2012 for studying social conventions in Twitter. Her research has been published in leading journals and conferences including PLoS One, Information Sciences, IJCAI, WWW, and ICWSM, and has been featured at the popular media outlets including the New York Times websites, Harvard Business Review’s research blog, the Washington Post, the New Scientist. Dr. Cha has worked at Facebook\'s Data Science Team as a Visiting Professor for a year.(字串長度:790)'),(469,'2017-10-31 08:33:30',136,'desc---->(字串長度:0)---->多媒體訊號涵蓋聲音、影像、視訊、三維圖檔及文字等，在處理上有不同的需求，因應人工智慧的來臨，有越來越多的深度學習技術被引入此領域。在此演講中，我將講述三個議題。首先，我將描述如何結合視訊、音樂情緒，及機器人學來發展一套自動指揮系統。在第二個議題中，我將談論如何自YouTube上截取同一場演唱會中，由不同聽眾錄得的片段，並用自動的方式將其整合成一段較完整的曲子。我們將引入音樂劇導演的手法讓此雜湊過程更貼近人性。第三個講題是如何讓電腦自動判讀NBA球賽的戰術。 (字串長度:671)'),(470,'2017-11-01 09:07:52',15,'talk_title---->政府數據分析與 Google Sprint 體驗設計(字串長度:48)---->當政府數據分析碰撞體驗設計(字串長度:39)'),(471,'2017-11-11 01:17:30',70,'me_desc---->NingChen擅長網站、產業分析及社群經營，並相信資料可以讓世界變得不一樣。她嘗試將時間及精力花在創造一個對於初學者及專家都友善的協作環境。 曾經是雅虎媒體數據分析師，此外，做過16年選舉輿情分析，做不同溫層議題發酵觀察；此外，參與環保署開放資料污染監測系統，NGO捐款行為分析。(字串長度:395)---->NingChen擅長網站、產業分析及社群經營，並相信資料可以讓世界變得不一樣。她嘗試將時間及精力花在創造一個對於初學者及專家都友善的協作環境。\r 曾經是雅虎媒體數據分析師，此外，做過16年選舉輿情分析，做不同溫層議題發酵觀察；此外，參與環保署開放資料污染監測系統，NGO捐款行為分析。\r slideshare/github:ningchencontact\r R_laides Taipei:https://www.meetup.com/R-Ladies-Taipei/(字串長度:488)'),(472,'2017-11-11 01:17:32',86,'position---->董事總經理(字串長度:15)---->總經理(字串長度:9)');
