Percy Liang is an associate professor of computer science and statistics at Stanford. These days, he’s interested in understanding how foundation models work, how to make them more efficient, modular, and robust, and how they shift the way peop

Episode 34: Seth Lazar, Australian National University: On legitimate power, moral nuance, and the political philosophy of AI

Mar 12th, 2024 1h 55m

Seth Lazar is a professor of philosophy at the Australian National University, where he leads the Machine Intelligence and Normative Theory (MINT) Lab. His unique perspective bridges moral and political philosophy with AI, introducing much-need

Episode 33: Tri Dao, Stanford: On FlashAttention and sparsity, quantization, and efficient inference

Aug 9th, 2023 1h 20m

Tri Dao is a PhD student at Stanford, co-advised by Stefano Ermon and Chris Re. He’ll be joining Princeton as an assistant professor next year. He works at the intersection of machine learning and systems, currently focused on efficient trainin

Episode 32: Jamie Simon, UC Berkeley: On theoretical principles for how neural networks learn and generalize

Jun 22nd, 2023 1h 1m

Jamie Simon is a 4th year Ph.D. student at UC Berkeley advised by Mike DeWeese, and also a Research Fellow with us at Generally Intelligent. He uses tools from theoretical physics to build fundamental understanding of deep neural networks so th

Episode 31: Bill Thompson, UC Berkeley, on how cultural evolution shapes knowledge acquisition

Mar 29th, 2023 1h 15m

Bill Thompson is a cognitive scientist and an assistant professor at UC Berkeley. He runs an experimental cognition laboratory where he and his students conduct research on human language and cognition using large-scale behavioral experiments,

Episode 30: Ben Eysenbach, CMU, on designing simpler and more principled RL algorithms

Mar 23rd, 2023 1h 45m

Ben Eysenbach is a PhD student from CMU and a student researcher at Google Brain. He is co-advised by Sergey Levine and Ruslan Salakhutdinov and his research focuses on developing RL algorithms that get state-of-the-art performance while being

Episode 29: Jim Fan, NVIDIA, on foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant

Mar 9th, 2023 1h 26m

Jim Fan is a research scientist at NVIDIA and got his PhD at Stanford under Fei-Fei Li. Jim is interested in building generally capable autonomous agents, and he recently published MineDojo, a massively multiscale benchmarking suite built on Mi

Episode 28: Sergey Levine, UC Berkeley, on the bottlenecks to generalization in reinforcement learning, why simulation is doomed to succeed, and how to pick good research problems

Mar 1st, 2023 1h 34m

Sergey Levine, an assistant professor of EECS at UC Berkeley, is one of the pioneers of modern deep reinforcement learning. His research focuses on developing general-purpose algorithms for autonomous agents to learn how to solve any task. In t

Episode 27: Noam Brown, FAIR, on achieving human-level performance in poker and Diplomacy, and the power of spending compute at inference time

Feb 9th, 2023 1h 44m

Noam Brown is a research scientist at FAIR. During his Ph.D. at CMU, he made the first AI to defeat top humans in No Limit Texas Hold 'Em poker. More recently, he was part of the team that built CICERO which achieved human-level performance in

Episode 26: Sugandha Sharma, MIT, on biologically inspired neural architectures, how memories can be implemented, and control theory

Jan 17th, 2023 1h 44m

Sugandha Sharma is a Ph.D. candidate at MIT advised by Prof. Ila Fiete and Prof. Josh Tenenbaum. She explores the computational and theoretical principles underlying higher cognition in the brain by constructing neuro-inspired models and mathem

Episode 25: Nicklas Hansen, UCSD, on long-horizon planning and why algorithms don't drive research progress

Dec 16th, 2022 1h 49m

Nicklas Hansen is a Ph.D. student at UC San Diego advised by Prof Xiaolong Wang and Prof Hao Su. He is also a student researcher at Meta AI. Nicklas' research interests involve developing machine learning systems, specifically neural agents, th

Episode 24: Jack Parker-Holder, DeepMind, on open-endedness, evolving agents and environments, online adaptation, and offline learning

Dec 6th, 2022 1h 56m

Jack Parker-Holder recently joined DeepMind after his Ph.D. with Stephen Roberts at Oxford. Jack is interested in using reinforcement learning to train generally capable agents, especially via an open-ended learning process where environments c

Episode 23: Celeste Kidd, UC Berkeley, on attention and curiosity, how we form beliefs, and where certainty comes from

Nov 22nd, 2022 1h 52m

Celeste Kidd is a professor of psychology at UC Berkeley. Her lab studies the processes involved in knowledge acquisition; essentially, how we form our beliefs over time and what allows us to select a subset of all the information we encounter

Episode 22: Archit Sharma, Stanford, on unsupervised and autonomous reinforcement learning

Nov 17th, 2022 1h 38m

Archit Sharma is a Ph.D. student at Stanford advised by Chelsea Finn. His recent work is focused on autonomous deep reinforcement learning—that is, getting real world robots to learn to deal with unseen situations without human interventions. P

Episode 21: Chelsea Finn, Stanford, on the biggest bottlenecks in robotics and reinforcement learning

Nov 3rd, 2022 40m 7s

Chelsea Finn is an Assistant Professor at Stanford and part of the Google Brain team. She's interested in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction at scale. In this episo

Episode 20: Hattie Zhou, Mila, on supermasks, iterative learning, and fortuitous forgetting

Oct 14th, 2022 1h 47m

Hattie Zhou is a Ph.D. student at Mila working with Hugo Larochelle and Aaron Courville. Her research focuses on understanding how and why neural networks work, starting with deconstructing why lottery tickets work and most recently exploring h

Episode 19: Minqi Jiang, UCL, on environment and curriculum design for general RL agents

Jul 19th, 2022 1h 53m

Minqi Jiang is a Ph.D. student at UCL and FAIR, advised by Tim Rocktäschel and Edward Grefenstette. Minqi is interested in how simulators can enable AI agents to learn useful behaviors that generalize to new settings. He is especially focused o

Episode 18: Oleh Rybkin, UPenn, on exploration and planning with world models

Jul 11th, 2022 2h

Oleh Rybkin is a Ph.D. student at the University of Pennsylvania and a student researcher at Google. He is advised by Kostas Daniilidis and Sergey Levine. Oleh's research focus is on reinforcement learning, particularly unsupervised and model-b

Episode 17: Andrew Lampinen, DeepMind, on symbolic behavior, mental time travel, and insights from psychology

Feb 28th, 2022 1h 59m

Andrew Lampinen is a Research Scientist at DeepMind. He previously completed his Ph.D. in cognitive psychology at Stanford. In this episode, we discuss generalization and transfer learning, how to think about language and symbols, what AI can l

Episode 16: Yilun Du, MIT, on energy-based models, implicit functions, and modularity

Dec 21st, 2021 1h 24m

Yilun Du is a graduate student at MIT advised by Professors Leslie Kaelbling, Tomas Lozano-Perez, and Josh Tenenbaum. He's interested in building robots that can understand the world like humans and construct world representations that enable t

Episode 15: Martín Arjovsky, INRIA, on benchmarks for robustness and geometric information theory

Oct 15th, 2021 1h 26m

Martín Arjovsky did his Ph.D. at NYU with Leon Bottou. Some of his well-known works include the Wasserstein GAN and a paradigm called Invariant Risk Minimization. In this episode, we discuss out-of-distribution generalization, geometric informa

Episode 14: Yash Sharma, MPI-IS, on generalizability, causality, and disentanglement

Sep 24th, 2021 1h 26m

Yash Sharma is a Ph.D. student at the International Max Planck Research School for Intelligent Systems. He previously studied electrical engineering at Cooper Union and has spent time at Borealis AI and IBM Research. Yash’s early work was on ad

Episode 13: Jonathan Frankle, MIT, on the lottery ticket hypothesis and the science of deep learning

Sep 10th, 2021 1h 20m

Jonathan Frankle (Google Scholar) (Website) is finishing his PhD at MIT, advised by Michael Carbin. His main research interest is using experimental methods to understand the behavior of neural networks. His current work focuses on finding spar

Episode 12: Jacob Steinhardt, UC Berkeley, on machine learning safety, alignment and measurement

Jun 18th, 2021 59m 39s

Jacob Steinhardt (Google Scholar) (Website) is an assistant professor at UC Berkeley. His main research interest is in designing machine learning systems that are reliable and aligned with human values. Some of his specific research direction

Episode 11: Vincent Sitzmann, MIT, on neural scene representations for computer vision and more general AI

May 20th, 2021 1h 10m

Vincent Sitzmann (Google Scholar) (Website) is a postdoc at MIT. His work is on neural scene representations in computer vision. Ultimately, he wants to make representations that AI agents can use to solve the same visual tasks humans solve re