Linked Big Data Platform for AI Industrial Solutions

林清詠博士(Dr. Ching-Yung Lin)

Chief Scientist, IBM Watson Lab, USA

摘要

IBM System G is the first complete software stack for all aspects of Linked Big Data. There are three major aspects of graphs -- graph storage & retrieval, graph topological analysis, and graphical models. Graph database is a tool for efficiently managing large-scale graph data, especially important for contextual and relationship analysis. Graph analytics is important for finding the important vertices or edges that are more central, that are clustered, or that form abnormal patterns. Graphical models are essential to artificial intelligence, information reasoning and predictive analysis, which requires combining many factors to create actionable insights. I will introduce and demo our industrial solutions that were created based on this System G platform, especially on the solutions used in the banks and in the governments.

Short Bio

Ching Yung Lin is the Chief Scientist in IBM T. J. Watson Research Center. He is also an Adjunct Professor in Columbia University since 2005 and in NYU since 2014. His research interest is mainly on fundamental research of multimodality signal understanding, network computing, and computational social & cognitive sciences, and applied research on security, commerce, and collaboration. Since 2011, Lin has been leading a team of more than 40 Ph.D. researchers in worldwide IBM Research Labs and more than 20 professors and researchers in 9 universities. He is the Principle Investigator of projects on Graph Computing and Social Cognitive Analytics. Ching-Yung is an author of 160+ publications and 19 issued patents. His team recently won the Best Paper Award in BigData 2013, Best Paper Award in CIKM 2012, and Best Theme Paper Award in ICIS 2011. He is a Fellow of IEEE.

Artificial Intelligence : The Next Step of Big Data” – An Industrial Perspective

王雲專家(Yun Wang)

工業技術研究院巨量資訊科技中心

摘要

World Economic Forum identified ‘The Fourth Industrial Revolution’ as its world global main theme and ‘Artificial Intelligence and Robotics’ was viewed as its core technology. White House published a series of reports on Artificial Intelligence around its impacts, challenges and a strategic plan in the 4th quarter of 2016. In the same time, industry responded with head-to-head competitive products, services and platforms such as Amazon’s Echo speaker, Google’s Google Home speaker, Microsoft’s Cortana personal assistant, IBM’s Watson cognitive computing. In this talk, we will follow an industrial perspective to examine the trends and progression of this 3rd AI wave in terms of various applications as well as technologies and platforms. At the end, we shall have a question and discussion session on topics with more interest or not covered.

Short Bio

Yun Wang joined Industrial Technology Research Institute (ITRI) of Taiwan in 2013, after his 28 years of service with IBM in software development and research. Before Mr. Wang’s retirement from IBM, Mr. Wang was serving as an IBM Fellow and CTO of IBM Research - China, guiding strategy and research in Internet of Things, Cloud Computing, Social Software, Business Analytics, and various Information Management initiatives.

王雲專家現任職於工業技術研究院，從事軟體產業 技術的研究和開發。2013年回台之前，曾服務於IBM軟體研發部門二十餘年並且獲得IBM 院士的榮譽。從早期 參與 IBM DB2數據庫的研究和產品開發一直到任職於 IBM 中國研究院首席技術官帶領物聯網、雲計算、社交軟體、大數據、業務分析和優化等領域的研發。

Visions and Challenges for Developing Practical Big Data Techniques and Innovative Applications in Taiwan

引 言 人：曾新穆教授／國立交通大學資訊工程系

Panelist：王雲專家／工業技術研究院巨量資訊科技中心

陳添福副主任／國家高速網路與計算中心

李育杰教授／國立交通大學應用數學系

劉嘉凱執行長／DSP智庫驅動公司

Major topics

●What are the big opportunities for Taiwan in developing Big Data Technologies and Innovative Applications?

● What are the biggest key challenges for Taiwan (in Big Data fields, especially for governmental units though not limited)?

● What are the major gaps between technology development and realization of Big Data applications? And how to reduce such gaps?

● How to promote and realize successful collaborations between Industry and Academia in Taiwan?

● What major future directions (including technology and application dimensions) should by develop in Taiwan?