Keynote Speakers

Ed Chi

Tuesday, August 8th, 8:00-9:30 AM

Keynote Address — The LLM (Large Language Model) Revolution: Implications from Chatbots and Tool-use to Reasoning 

Abstract: Deep learning is a shock to our field in many ways, yet still many of us were surprised at the incredible performance of Large Language Models (LLMs). LLM uses new deep learning techniques with massively large data sets to understand, predict, summarize, and generate new content. LLMs like ChatGPT and Bard have seen a dramatic increase in their capabilities—generating text that is nearly indistinguishable from human-written text, translating languages with amazing accuracy, and answering your questions in an informative way. This has led to a number of exciting research directions for chatbots, tool-use, and reasoning: 

– Chatbots: LLM chatbots that are more engaging and informative than traditional chatbots. First, LLMs can understand the context of a conversation better than ever before, allowing them to provide more relevant and helpful responses. Second, LLMs enable more engaging conversations than traditional chatbots, because they can understand the nuances of human language and respond in a more natural way. For example, LLMs can make jokes, ask questions, and provide feedback. Finally, because LLM chatbots can hold conversations on a wide range of topics, they can eventually learn and adapt to the user’s individual preferences. 

– Tool-use, Retrieval Augmentation and Multi-modality: LLMs are also being used to create tools that help us with everyday tasks. For example, LLMs can be used to generate code, write emails, and even create presentations. Beyond human-like responses in Chatbots, later LLM innovators realized LLM’s ability to incorporate tool-use, including calling search and recommendation engines, which means that they could effectively become human assistants in synthesizing summaries from web search and recommendation results. Tool-use integration has also enabled multimodal capabilities, which means that the chatbot can produce text, speech, images, and video.

– Reasoning: LLMs are also being used to develop new AI systems that can reason and solve problems. Using Chain-of-Thought approaches, we have shown LLM’s ability to break down problems, and then use logical reasoning to solve each of these smaller problems, and then combine the solutions to reach the final answer. LLMs can answer common-sense questions by using their knowledge of the world to reason about the problem, and then use their language skills to generate text that is both creative and informative. 

In this talk, I will cover recent advances in these 3 major areas, attempting to draw connections between them, and paint a picture of where major advances might still come from. While the LLM revolution is still in its early stages, it has the potential to revolutionize the way we interact with AI, and make a significant impact on our lives.

Bio: Ed H. Chi is a Distinguished Scientist at Google DeepMind, leading machine learning research teams working on large language models (LaMDA/Bard), neural recommendations, and reliable machine learning. With 39 patents and ~200 research articles, he is also known for research on user behavior in web and social media.  As the Research Platform Lead, at Google he helped launched Bard, a conversational AI experiment, and delivered significant improvements for YouTube, News, Ads, Google Play Store with >660 product improvements since 2013. 

Prior to Google, he was Area Manager and Principal Scientist at Xerox Palo Alto Research Center‘s Augmented Social Cognition Group in researching how social computing systems help groups of people to remember, think and reason. Ed earned his 3 degrees (B.S., M.S., and Ph.D.) in 6.5 years from University of Minnesota. Inducted as an ACM Fellow and into the CHI Academy, he also 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 golfer, swimmer, photographer and snowboarder in his spare time, he also has a blackbelt in Taekwondo.

Eric Horvitz

Wednesday, August 9th, 8:00-9:30 AM

Keynote Address — People and Machines: Pathways to Deeper Human-AI Synergy

Bio: Eric Horvitz serves as Microsoft’s Chief Scientific Officer. He spearheads company-wide initiatives, navigating opportunities and challenges at the confluence of scientific frontiers, technology, and society, including strategic efforts in AI, medicine, and the biosciences.  He has pursued in his research principles and applications of AI amidst the complexities of the open world, with efforts on harnessing probability and utility in machine learning and reasoning, models of bounded rationality, and principles and mechanisms for supporting human-AI interaction and complementarity. His efforts and collaborations have led to fielded systems in healthcare, transportation, ecommerce, operating systems, and aerospace.  He received the Feigenbaum Prize and Allen Newell Prize for contributions in AI. He received the CHI Academy honor for innovations at the intersection of AI and human-computer interaction. He has been elected to the National Academy of Engineering (NAE) and is a fellow of the Association of Computing Machinery (ACM) and Association for the Advancement of AI (AAAI).  He serves on the President’s Council of Advisors on Science and Technology (PCAST). He founded and chairs Microsoft’s Aether committee, established the One Hundred Year Study on AI, and co-founded and serves as board chair of the Partnership on AI

Eric Horvitz photographed at Microsoft Building 99 in Redmond, Wash. on March 30, 2017. Photo by Dan DeLong

Mihaela van der Schaar

Thursday, August 10th, 8:00-9:30 AM

Keynote Address — Time: The next frontier for machine learning

Bio: Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambridge Centre for AI in Medicine (CCAIM).

Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.
Mihaela is personally credited as inventor on 35 USA patents (the majority of which are listed here), many of which are still frequently cited and adopted in standards. She has made over 45 contributions to international standards for which she received 3 ISO Awards. In 2019, a Nesta report determined that Mihaela was the most-cited female AI researcher in the U.K.