AAAI-21 / IAAI-21 / EAAI-21 Invited Speaker Program

AAAI-21 is pleased to present the following series of distinguished speakers:

 

February 4

February 6

February 7

 

AAAI-21 will also feature a series of Invited Panels

For complete information about this program, please refer to https://aaai.org/Conferences/AAAI-21/aaai-21-panels/.


 

Sanjeev Arora

Sanjeev Arora

Princeton University

AAAI-21 Invited Talk

Title: Opening the Black Box of Deep Learning (+ Takeaways for AI)

AAAI Talk Videohttps://slideslive.com/38952434

Deep learning’s successes have led to dramatic progress in some classic AI tasks. Can it take us all the way to full AI? We argue that the current black box view of deep learning will prove a big hurdle. Via a tour d’horizon of recent theoretical analyses of deep learning in some concrete settings, we illustrate how the black box view can miss out on special phenomena going on during training that are not captured by the training objective. We argue that understanding such phenomena via mathematical understanding will be crucial for enabling the full range of AI applications.

Sanjeev Arora is Charles C. Fitzmorris Professor of Computer Science at Princeton University. He has received Packard Fellowship (1997), Simons Investigator Award (2012), Gödel Prize (2001 and 2010), ACM Prize in Computing (2012), and the Fulkerson Prize (2012). He is a Fellow of the AAAS and Member of NAS.

Regina Barzilay

Regina Barzilay

Massachusetts Institute of Technology

AAAI-21 Invited Talk

Recipient of the 2021 AAAI Squirrel Award for for Artificial Intelligence for the Benefit of Humanity

Title: A Tale of Two Translations

AAAI Talk Video: https://slideslive.com/38952398

In this talk, I will describe our experience in developing and deploying machine learning methods in two areas of healthcare: cancer diagnosis and drug design. The talk will focus on the algorithms that enable these functionalities, while highlighting unsolved technical challenges. In addition, I will share lessons learned during this outreach and conclude with recommendations to the AI community on how to magnify our collective impact on healthcare.

Regina Barzilay is a professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Her research interests are in natural language processing. Currently, Prof. Barzilay is focused on bringing the power of machine learning to oncology. In collaboration with physicians and her students, she is devising deep learning models that utilize imaging, free text, and structured data to identify trends that affect early diagnosis, treatment, and disease prevention. Prof. Barzilay is poised to play a leading role in creating new models that advance the capacity of computers to harness the power of human language data.

Regina Barzilay is a recipient of various awards including the MacArthur Fellowship, NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards in top NLP conferences. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. Most recently, in 2020, she is the first recipient of the AAAI Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity.

Prof. Barzilay received her MS and BS from Ben-Gurion University of the Negev. She received her PhD in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.

Daphne Koller

Daphne Koller

insitro, Inc. and Enageli, co-founder and Board Member

AAAI-21 Invited Talk

Title: Digital Learning Coming to Life

AAAI Talk Video: https://slideslive.com/38952404

The global pandemic that engulfed the world in 2020 gave rise to an immediate imperative to transition many of our activities to a digital format. One of the more significant of these activities was the sudden transition to the use of online platforms for providing education. This online transition caught the vast majority of academic institutions unprepared, and most defaulted to an approach of “let’s do what we’ve always done, just over video”. This paradigm led to a considerable decline in student satisfaction and achievement. In this talk, I will argue that this challenge is also an unprecedented opportunity that can help us transform the way in which we teach. By building innovative digital learning platforms that implement best practices from teaching and learning, we can enable active learning and collaboration, measure and enhance student engagement, and increase student success. In fact, some of these interventions are considerably easier to deploy on a digital platform than they are in traditional classrooms. Moreover, this new way of teaching can be more inclusive than traditional approaches, and disproportionately benefit students from less advantaged backgrounds. Finally, digital learning opens the door to the collection of massive amounts of data about teaching and learning, which will enable us to develop and deploy human-driven or AI-driven interventions that can further enhance student engagement and success.

Daphne Koller is CEO and Founder of insitro, a machine-learning enabled drug discovery company, and the co-founder of the online education platforms Engageli and Coursera. Daphne was the Rajeev Motwani Professor of Computer Science at Stanford University, the co-CEO and President of Coursera and Chief Computing Officer of Calico. Daphne was recognized as one of TIME Magazine’s 100 most influential people in 2012. She received the MacArthur Foundation Fellowship in 2004 and the ACM Prize in Computing in 2008. She was inducted into the National Academy of Engineering in 2011 and elected a fellow of the American Association for Artificial Intelligence in 2004, the American Academy of Arts and Sciences in 2014 and of the International Society of Computational Biology in 2017.

Dr. Kai-Fu Lee

Dr. Kai-Fu Lee

Sinoventures, China

AAAI-21/IAAI-21 Joint Invited Talk

Title: AI Infusion Investment Outlook

IAAI/AAAI Talk Video: https://slideslive.com/38952432

With over 35 years of researching, developing, building and now investing in AI, in this presentation Dr. Kai-Fu Lee will givie his unique perspectives on how AI is now moving from the age of major scientific discovery since deep learning breakthrough, into the age of commercial implementations. He will deep dive into how business world would expect material economic benefits ushering in the new phase infusing AI into traditional industries. Under the “AI Infusion” outlook, he would elaborate how businesses may adopt AI to achieve productivity enhancements and digital transformation with insights, real world examples, and implications to the research community.

Dr. Kai-Fu Lee is a former artificial intelligence researcher, former head of Google China and Microsoft Research Asia, and a long time investor in Chinese tech startups. Currently Chairman and CEO of Sinovation Ventures, he is one of the world’s leading AI expert in particular the fields of commercialization and application of AI.

Tuomas Sandholm

Tuomas Sandholm

Carnegie Mellon University, Optimized Markets, Inc., Strategy Robot, Inc., Strategic Machine, Inc.

IAAI-21 Robert S. Engelmore Memorial Award Lecture

Title: What Can and Should Humans Contribute to Superhuman AIs?

AAAI Talk Video: https://slideslive.com/38952399

I will discuss what humans can and should contribute to superhuman AIs—not general ones intended to be like humans, but ones for specific applications that make the world a better place. I will discuss how the application should drive research. I will present extensive experiences from having fielded superhuman AIs for combinatorial markets, organ exchanges, and imperfect-information game settings. I will discuss inventing and scoping novel AI applications. I will discuss how humans should supply the value framework while leaving policy optimization and combinatorics for AI. I will cover a framework that separates those ends and means, and conducts future-aware optimization in very-large-scale dynamic problems in a scalable way. I will wonder about the future of science when theorems (not just proofs) and empirical theories need to be so long that they are beyond human comprehension. I will discuss human overconfidence in humans over AI. I will discuss what explainability could be and why in many AI applications it should not be required. Finally, I will suggest flipping ethics around from an ex post discussion activity to a system-design discipline that I coin pre-design ethics.

Tuomas Sandholm is Angel Jordan University Professor of Computer Science at Carnegie Mellon University and a serial entrepreneur. His research focuses on the convergence of artificial intelligence, economics, and operations research. He is Co-Director of CMU AI. He is Founder and Director of the Electronic Marketplaces Laboratory.

In parallel with his academic career, he was Founder, Chairman, first CEO, and CTO/Chief Scientist of CombineNet, Inc. from 1997 until its acquisition in 2010. During this period the company commercialized over 800 of the world’s largest-scale generalized combinatorial multi-attribute auctions, with over $60 billion in total spend and over $6 billion in generated savings. He is Founder and CEO of Optimized Markets, Inc., which is bringing a new optimization-powered paradigm to advertising campaign sales, scheduling, and pricing in linear and nonlinear TV, display, streaming, and cross-media advertising.

Since 2010, his algorithms have been running the national kidney exchange for UNOS, where they make the kidney exchange transplant plan for 80% of U.S. transplant centers together each week. He also co-invented never-ending altruist-donor-initiated chains, which have become the main modality of kidney exchange worldwide and have led to around 10,000 life-saving transplants. He invented liver lobe and multi-organ exchanges, and the first liver-kidney swap took place in 2019.

He has developed the leading algorithms and pipelines for several general game classes. The team he leads is the multi-time world champion in AI-vs-AI heads-up no-limit Texas hold’em, the main benchmark and decades-open challenge problem for application-independent algorithms for imperfect-information games. Their AI Libratus became the first and only AI to beat top humans at that game. Then their AI Pluribus became the first and only AI to beat top humans at the multi-player game. That is the first superhuman milestone in any game beyond two-player zero-sum games. He is Founder and CEO of Strategic Machine, Inc., which provides solutions for strategic reasoning in business and gaming applications. He is Founder and CEO of Strategy Robot, Inc., which focuses on defense, intelligence, and other government applications.

Among his honors are the Minsky Medal, Computers and Thought Award, inaugural ACM Autonomous Agents Research Award, CMU’s Allen Newell Award for Research Excellence, Sloan Fellowship, NSF Career Award, Carnegie Science Center Award for Excellence, Edelman Laureateship, and Goldman Sachs 100 Most Intriguing Entrepreneurs. He is Fellow of the ACM, AAAI, and INFORMS. He holds an honorary doctorate from the University of Zurich.

Latanya Sweeney

Latanya Sweeney

Harvard University

AAAI-21 Invited Talk

Title: How AI and Technology Design will Dictate Our Civic Future

AAAI Talk Video: https://slideslive.com/38952433

Data-driven algorithms and technology designers are the new policymakers. No one elected them, and most people do not know their names, but the decisions they make dictate the code by which we conduct our daily lives and govern our country. Challenges to the privacy of our personal data were the first wave of this change; as technology progresses, every demographic value and every law has come up for grabs and will likely be redefined by what technology does or does not enable. How will it all fit together or fall apart?

Latanya Sweeney pioneered the field known as data privacy, launched the area known as algorithmic fairness, and her work is cited in government regulations worldwide. She was CTO of the FTC and earned her PhD in CS from MIT in 2001, being the first black woman to do so. latanyasweeney.org

Luke Zettlemoyer

Luke Zettlemoyer

University of Washington and Facebook

AAAI-21 Invited Talk

Title: Recent Advances in Language Model Pretraining

AAAI Talk Video: https://slideslive.com/38952401

In recent years, large language models pretrained on vast quantities of raw text have revolutionized NLP. Existing methods, based on variations of causal or masked languages models, now provide the de facto methods for every NLP task. In this talk, I will discuss recent work on language model pretraining, from ELMo, GPT, and BERT to more recent models. I will aim for broad coverage of the overall trends but provide more details on the models we have been developing recently at Facebook AI and the University of Washington. These include in particular pre-training methods for sequence-to-sequence models, such as BART, mBART, and MARGE, which provide some of the most generally applicable approaches to date.

Luke Zettlemoyer is a Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington, and a Research Scientist at Facebook. His research focuses on empirical methods for natural language semantics, and involves designing machine learning algorithms, introducing new tasks and datasets, and, most recently, studying how to best develop self-supervision signals for pre-training. Honors include multiple paper awards, a PECASE award, and an Allen Distinguished Investigator Award. Luke received his PhD from MIT and was a postdoc at the University of Edinburgh.

Michael Wooldridge (EAAI-21)

Michael Wooldridge (EAAI-21)

University of Oxford and Alan Turing Institute, London

2021 AAAI/EAAI-21 Outstanding Educator Award Lecture

This talk will be presented in the EAAI-21 Symposium, February 6, at 6:30 AM PST. For complete information about this program, please refer to https://aaai.org/Conferences/AAAI-21/eaai-21/.

Title: Talking to the Public about AI

EAAI Talk Video: https://slideslive.com/38951738

Since everything went crazy in AI, around 2012, I, like many other members of our community, have frequently found myself put in the position of having to talk about our field to a non-specialist audience. I’ve been interviewed on TV and radio, and spoken to endless university committees, government committees, and industrial conferences. More recently, following the publication of my two popular science books (the Ladybird Expert Guide to AI [2018], and The Road to Conscious Machines [2020]), I’ve even begun speaking at a literary festivals (believe me, I never expected to be doing this as a PhD student studying multiagent systems back in 1989). In this talk, I will relate these experiences, the mistakes I made, and what I learned from them – how our field is perceived, what people fear, hope, and expect from it, and how best to communicate excitement about the very real progress we’ve made recently with a realistic understanding of where we are and where we are going.

Michael Wooldridge is a Professor of Computer Science and Head of Department of Computer Science at the University of Oxford, and a programme director for AI at the Alan Turing Institute. He is a Fellow of the ACM, the Association for the Advancement of AI (AAAI), and the European Association for AI (EurAI). From 2014-16, he was President of the European Association for AI, and from 2015-17 he was President of the International Joint Conference on AI (IJCAI). As well as more than 400 technical articles on AI, he has published two popular science introductions to the field: The Ladybird Expert Guide to AI (2018), and The Road to Conscious Machines (Pelican, 2020).

This site is protected by copyright and trademark laws under US and International law. All rights reserved. Copyright © 1995–2021 AAAI