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There will be a scientific theory of deep learning
Deep learning works extraordinarily well. And we still largely don't know why.A new paper from Jamie Simon, Daniel Kunin, and 12 co-authors argues that a scientific theory of deep learning is…
Malleable software and human agency with Geoffrey Litt
Geoffrey Litt is a design engineer at Notion working on malleable software: computing environments where anyone can adapt their software to meet their needs and their lives. Before joining Notion, he…
From lawless spaces to true liberty: rethinking AI's role in society
Welcome back to Generally Intelligent! We’re excited to relaunch our podcast—still featuring thoughtful conversations on building AI, but now with an expanded lens on its economic, societal,…
Rylan Schaeffer, Stanford: Investigating emergent abilities and challenging dominant research ideas
Rylan Schaeffer is a PhD student at Stanford studying the engineering, science, and mathematics of intelligence. He authored the paper “Are Emergent Abilities of Large Language Models a Mirage?”, as…
Ari Morcos, DatologyAI: Leveraging data to democratize model training
Ari Morcos is the CEO of DatologyAI, which makes training deep learning models more performant and efficient by intervening on training data. He was at FAIR and DeepMind before that, where he worked…
Percy Liang, Stanford: The paradigm shift and societal effects of foundation models
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,…
Seth Lazar, Australian National University: Legitimate power, moral nuance, and the political philosophy of AI
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…
Tri Dao, Stanford: FlashAttention and sparsity, quantization, and efficient inference
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…
Jamie Simon, UC Berkeley: Theoretical principles for how neural networks learn and generalize
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…
Bill Thompson, UC Berkeley: How cultural evolution shapes knowledge acquisition
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…
Ben Eysenbach, CMU: Designing simpler and more principled RL algorithms
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…
Jim Fan, NVIDIA: Foundation models for embodied agents, scaling data, and why prompt engineering will become irrelevant
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…
Sergey Levine, UC Berkeley: The bottlenecks to generalization in reinforcement learning, why simulation is doomed to succeed, and how to pick good research problems
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…
Noam Brown, FAIR: Achieving human-level performance in poker and Diplomacy, and the power of spending compute at inference time
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…
Sugandha Sharma, MIT: Biologically inspired neural architectures, how memories can be implemented, and control theory
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…
Nicklas Hansen, UCSD: Long-horizon planning and why algorithms don't drive research progress
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…
Jack Parker-Holder, DeepMind: Open-endedness, evolving agents and environments, online adaptation, and offline learning
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…
Celeste Kidd, UC Berkeley: Attention and curiosity, how we form beliefs, and where certainty comes from
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…
Archit Sharma, Stanford: Unsupervised and autonomous reinforcement learning
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…
Chelsea Finn, Stanford: The biggest bottlenecks in robotics and reinforcement learning
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…
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Generally Intelligent has published 40 episodes since December 2020, covering topics in Technology.
Generally Intelligent is currently highly active with new episodes every 2 months. Average episode length is 1h 23m.
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