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Some Recent Advances in Google Brain

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Some Recent Advances in Google Brain

  • 講者紀懷新 博士 (Google Research, Brain Team)
    邀請人:古倫維
  • 時間2022-07-13 (Wed.) 14:00 – 16:00
  • 地點資訊所新館106演講廳
摘要
Deep learning have fundamentally changed how we solve problems across a wide variety of fields.  What other new and difficult problems can we apply deep learning to?  Our insights are 3-fold: (1) sequential models are important for a wide variety of problems in ranking and recommendation as well as large language models; (2) we can use ML to optimize ML models and systems including Chip Design; (3) Large Language Models are enabling foundational capabilities including natural conversations with users (LaMDA project) and basic reasoning.

In this talk, I will discuss these insights and how they have been realized in several Google Brain projects, including:
1. Neural Recommendations: Magical Experiences with Neural Modeling/RL 2. AutoML: Design ML with ML 3. Transforming Chip Design: Fast & Generalizable Chip Placement with RL 4. LaMDA Chatbot: Safe, Grounded, & High-Quality Dialog Applications 5. Teaching Machines to Reason: Language Model Reasoning with Prompting
Deep learning have fundamentally changed how we solve problems across a wide variety of fields.  What other new and difficult problems can we apply deep learning to?  Our insights are 3-fold: (1) sequential models are important for a wide variety of problems in ranking and recommendation as well as large language models; (2) we can use ML to optimize ML models and systems including Chip Design; (3) Large Language Models are enabling foundational capabilities including natural conversations with users (LaMDA project) and basic reasoning.

In this talk, I will discuss these insights and how they have been realized in several Google Brain projects, including:
1. Neural Recommendations: Magical Experiences with Neural Modeling/RL 2. AutoML: Design ML with ML 3. Transforming Chip Design: Fast & Generalizable Chip Placement with RL 4. LaMDA Chatbot: Safe, Grounded, & High-Quality Dialog Applications 5. Teaching Machines to Reason: Language Model Reasoning with Prompting
 
BIO
Ed H. Chi is a Distinguished Scientist at Google, leading several machine learning research teams focusing on neural modeling, reinforcement learning, dialog systems, reliable/robust machine learning, and recommendation systems in Google Brain team. His team has delivered significant improvements for YouTube, News, Ads, Google Play Store at Google with >600 product improvements since 2013. With 39 patents and >180 research articles, he is also known for research on user behavior in web and social media.
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 team 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. Recognized as an ACM Distinguished Scientist and elected into the CHI Academy, he recently received a 20-year Test of Time award for research in information visualization. He has been featured and quoted in the press, including the Economist, Time Magazine, LA Times, and the Associated Press. An avid swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo.