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  • Upcoming
  • Master Forum | Engineering Responsible AI

Master Forum | Engineering Responsible AI

April 28, 2021 Upcoming

Topic:?Engineering Responsible AI

Speaker:?Harry Shum

Date:?April 28, 2021

Time:4:15 pm - 5:30 pm

Venue:?W201, Administration Building

Language:? English

?

Speaker:

Harry Shum, Ph.D.

  • Foreign Member, National Academy of Engineering, U.S.A
  • International Fellow, Royal Academy of Engineering, U.K.
  • Former?Executive?Vice?President?of?Microsoft

Dr. Harry?Shum?was executive vice president of Microsoft’s Artificial Intelligence (AI) and Research group. He was responsible for driving the company’s overall AI strategy and forward-looking research and development efforts spanning infrastructure, services, apps and agents. He oversaw AI-focused product groups including Bing. He also led Microsoft Research, one of the world’s premier computer science research organizations, and its integration with the engineering teams across the company. He is a member of the Governing Board of Shenzhen Institute of Artificial Intelligence and Robotics for Society.

?

Abstract

Recent achievements and advancements in AI have outpaced what anyone would have thought imaginable even five to ten years ago. For instance, we are fast approaching human parity across many tasks of AI — speech, vision and natural language, on many benchmark date sets. But many practitioners building AI technology and deploying AI products have not always thought through the societal implications such as fairness and transparency. Recently a number of principles have been proposed to help companies and countries navigate the complexities and implications of AI. But principles alone are no longer enough—industry, academia and government need to take actions now to move from principles to practices. In this talk, Dr. Harry?Shum will share some examples of what they have been practicing in Microsoft AI and Research from doing research in explainable and interpretable AI, to creating tools for bias error analysis in machine learning, to debiasing word embedding learnt from the web.

?

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