Keynotes

Title: Knowledge is Power: Symbolic Knowledge Distillation, Commonsense Morality, and Multimodal Script Knowledge

Abstract: Scale appears to be the winning recipe in today’s AI leaderboards. And yet, extreme-scale neural models are still brittle to make errors that are often nonsensical and even counterintuitive. In this talk, I will argue for the importance of knowledge, especially commonsense knowledge, and demonstrate how smaller models developed in academia can still have an edge over larger industry-scale models, if powered with knowledge. First, I will introduce “symbolic knowledge distillation”, a new framework to distill larger neural language models into smaller commonsense models, which leads to a machine-authored KB that wins, for the first time, over a human-authored KB in all criteria: scale, accuracy, and diversity. Next, I will present an experimental conceptual framework toward computational social norms and commonsense morality, so that neural language models can learn to reason that “helping a friend” is generally a good thing to do, but “helping a friend spread fake news” is not. Finally, I will discuss an approach to multimodal script knowledge demonstrating the power of complex raw data, which leads to new SOTA performances on a dozen leaderboards that require grounded, temporal, and causal commonsense reasoning.

Bio: Yejin Choi is Brett Helsel Professor at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and a senior research manager at AI2 overseeing the project Mosaic. Her research investigates a wide range of problems including commonsense knowledge and reasoning, neuro-symbolic integration, multimodal representation learning, and AI for social good. She is a co-recipient of the ACL Test of Time award in 2021, the CVPR Longuet-Higgins Prize in 2021, a NeurIPS Outstanding Paper Award in 2021, the AAAI Outstanding Paper Award in 2020, the Borg Early Career Award in 2018, the inaugural Alexa Prize Challenge in 2017, IEEE AI’s 10 to Watch in 2016, and the ICCV Marr Prize in 2013.

Title: Graph Neural Network Research at AWS AI

Abstract: In the course of just a few years, Graph Neural Networks (GNNs) have emerged as the prominent supervised learning approach that brings the power of deep representation learning to graph and relational data. An ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations. As a result, GNNs are quickly moving from the realm of academic research involving small graphs to powering commercial applications and very large graphs. This talk will provide an overview of some of the research that AWS AI has been doing to facilitate this transition, which includes developing the Deep Graph Library (DGL)—an open-source framework for writing and training GNN-based models, improving the computational efficiency and scaling of GNN model training for extremely large graphs, developing novel GNN-based solutions for different applications, and making it easy for developers to train and use GNN models by integrating graph-based ML techniques in graph databases.

Bio: George Karypis is a Senior Principal Scientist at AWS AI and a Distinguished McKnight University Professor and an ADC Chair of Digital Technology at the Department of Computer Science & Engineering at the University of Minnesota. His research interests span the areas of data mining, machine learning, high performance computing, information retrieval, collaborative filtering, bioinformatics, cheminformatics, and scientific computing. His research has resulted in the development of software libraries for serial and parallel graph partitioning (METIS and ParMETIS), hypergraph partitioning (hMETIS), for parallel Cholesky factorization (PSPASES), for collaborative filtering-based recommendation algorithms (SUGGEST), clustering high dimensional datasets (CLUTO), finding frequent patterns in diverse datasets (PAFI), and for protein secondary structure prediction (YASSPP). He has coauthored over 300 papers on these topics and two books (“Introduction to Protein Structure Prediction: Methods and Algorithms” (Wiley, 2010) and “Introduction to Parallel Computing” (Publ. Addison Wesley, 2003, 2^nd edition)). In addition, he is serving on the program committees of many conferences and workshops on these topics, and on the editorial boards of the IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Knowledge Discovery from Data, Data Mining and Knowledge Discovery, Social Network Analysis and Data Mining Journal, International Journal of Data Mining and Bioinformatics, the journal on Current Proteomics, Advances in Bioinformatics, and Biomedicine and Biotechnology. He is a Fellow of the IEEE.

Title: Ethics Challenges in AI

Abstract: In the first part we cover five current AI issues through examples: (1) discrimination (e.g., search, facial recognition, justice, sharing economy, etc.); (2) physiognomy (e.g., personality predictions based in facial biometrics); (3) unfair feedback loops (e.g., exposure and popularity biases); (4) stupidity (e.g., lack of semantic and context understanding) and (5) indiscriminate use of computing resources (e.g., large language models). These examples do have a personal bias but set the context for the second part where we address four generic challenges: (1) too many principles (e.g., principles vs. techniques), (2) cultural differences (e.g., Christian vs. Muslim vs. Ubuntu); (3) regulation (e.g., privacy, antitrust) and (4) our cognitive biases. We finish discussing what we can do to address these challenges in the near future.

Bio: Ricardo Baeza-Yates is Director of Research at the Institute for Experiential AI of Northeastern University. He is also part-time professor at Universitat Pompeu Fabra in Barcelona and Universidad de Chile in Santiago. Before, he was VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California, from 2006 to 2016. He is co-author of the best-seller Modern Information Retrieval textbook published by Addison-Wesley in 1999 and 2011 (2nd ed), which won the ASIST 2012 Book of the Year award. From 2002 to 2004 he was elected to the Board of Governors of the IEEE Computer Society and between 2012 and 2016 was elected for the ACM Council. Since 2010 he has been a founding member of the Chilean Academy of Engineering. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions. He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989, and his areas of expertise are web search and data mining, information retrieval, bias on AI, data science and algorithms in general.