Triangulating Intelligence: Melding Neuroscience, Psychology, and AI – Full Conference Video
HAI's fall 2020 conference, "Triangulating Intelligence: Melding Neuroscience, Psychology, and AI," focused on the latest research on cognitive science, neuroscience, vision, language, and thought informing the pursuit of artificial intelligence.
Session 1
Welcome: :03
Fei-Fei-Li, 李飞飞, Sequoia Professor, Computer Science Department; Denning Co-Director, Stanford HAI
Introduction: 2:13
Christopher Manning, Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science, Stanford University; Faculty Associate Director, Stanford HAI
Surya Ganguli, Associate Professor of Applied Physics, and by courtesy, of Neurobiology, of Electrical Engineering, and of Computer Science, Stanford University; Faculty Associate Director, Stanford HAI
AI, Psychology, and Neuroscience: The View from DeepMind: 16:06
Matthew Botvinick, Director of Neuroscience Research, DeepMind; Honorary Professor, Computational Neuroscience Unit, University College London
Self-Supervised Learning of Visual (And Other) Representations: 45:51
Dan Yamins, Assistant Professor of Psychology and Computer Science, Stanford University; Faculty Scholar, Wu Tsai Neurosciences Institute
How Not to Create a Robot's Mind: 1:13:22
Chelsea Finn, Assistant Professor of Computer Science and Electrical Engineering, Stanford University
Panel discussion: 1:36:34
Session 2
How to Allow Deep Learning on Your Data Without Revealing Your Data: Simple and Effective Approaches to Privacy: 2:29:19
Sanjeev Arora, Charles C. Fitzmorris Professor of Computer Science, Princeton University
Commonsense Intelligence: Cracking the Longstanding Challenge in AI: 2:56:38
Yejin Choi, Associate Professor, University of Washington; Senior Research Manager, Allen Institute for Artificial Intelligence
Incorporating Insights from Cognitive Science into AI: 3:21:45
Aude Oliva, MIT Director, MIT-IBM Watson AI Lab; Co-Director, MIT Quest for Intelligence
Scaling AI the Human Way: Reverse-Engineering Core Common Sense with Probabilistic Programs, Program Induction, and the Game Engine in the Head: 3:49:29
Joshua Tenenbaum, Professor of Computational Cognitive Science, MIT
Panel Discussion: 4:22:54
Session 3
Triangulating Intelligence at Stanford: 5:17:15
Michael Frank, David and Lucile Packard Professor of Human Biology, and Director, Symbolic Systems Program, Stanford University
Christopher Manning, Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science, Stanford University; Faculty Associate Director, Stanford Institute for Human-Centered Artificial Intelligence
Surya Ganguli, Associate Professor of Applied Physics, and by courtesy, of Neurobiology, of Electrical Engineering, and of Computer Science, Stanford University; Faculty Associate Director, Stanford Institute for Human-Centered Artificial Intelligence
Bill Newsome, Harman Family Provostial Professor of Neurobiology, Stanford University School of Medicine; Vincent V.C. Woo Director, Wu Tsai Neurosciences Institute
HAI’s fall 2020 conference, “Triangulating Intelligence: Melding Neuroscience, Psychology, and AI,” focused on the latest research on cognitive science, neuroscience, vision, language, and thought informing the pursuit of artificial intelligence.
Session 1
Welcome: :03
Fei-Fei-Li, 李飞飞, Sequoia Professor, Computer Science Department; Denning Co-Director, Stanford HAI
Introduction: 2:13
Christopher Manning, Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science, Stanford University; Faculty Associate Director, Stanford HAI
Surya Ganguli, Associate Professor of Applied Physics, and by courtesy, of Neurobiology, of Electrical Engineering, and of Computer Science, Stanford University; Faculty Associate Director, Stanford HAI
AI, Psychology, and Neuroscience: The View from DeepMind: 16:06
Matthew Botvinick, Director of Neuroscience Research, DeepMind; Honorary Professor, Computational Neuroscience Unit, University College London
Self-Supervised Learning of Visual (And Other) Representations: 45:51
Dan Yamins, Assistant Professor of Psychology and Computer Science, Stanford University; Faculty Scholar, Wu Tsai Neurosciences Institute
How Not to Create a Robot’s Mind: 1:13:22
Chelsea Finn, Assistant Professor of Computer Science and Electrical Engineering, Stanford University
Panel discussion: 1:36:34
Session 2
How to Allow Deep Learning on Your Data Without Revealing Your Data: Simple and Effective Approaches to Privacy: 2:29:19
Sanjeev Arora, Charles C. Fitzmorris Professor of Computer Science, Princeton University
Commonsense Intelligence: Cracking the Longstanding Challenge in AI: 2:56:38
Yejin Choi, Associate Professor, University of Washington; Senior Research Manager, Allen Institute for Artificial Intelligence
Incorporating Insights from Cognitive Science into AI: 3:21:45
Aude Oliva, MIT Director, MIT-IBM Watson AI Lab; Co-Director, MIT Quest for Intelligence
Scaling AI the Human Way: Reverse-Engineering Core Common Sense with Probabilistic Programs, Program Induction, and the Game Engine in the Head: 3:49:29
Joshua Tenenbaum, Professor of Computational Cognitive Science, MIT
Panel Discussion: 4:22:54
Session 3
Triangulating Intelligence at Stanford: 5:17:15
Michael Frank, David and Lucile Packard Professor of Human Biology, and Director, Symbolic Systems Program, Stanford University
Christopher Manning, Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science, Stanford University; Faculty Associate Director, Stanford Institute for Human-Centered Artificial Intelligence
Surya Ganguli, Associate Professor of Applied Physics, and by courtesy, of Neurobiology, of Electrical Engineering, and of Computer Science, Stanford University; Faculty Associate Director, Stanford Institute for Human-Centered Artificial Intelligence
Bill Newsome, Harman Family Provostial Professor of Neurobiology, Stanford University School of Medicine; Vincent V.C. Woo Director, Wu Tsai Neurosciences Institute
