There will be a scientific theory of deep learning

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 emerging, and coins a name for the emerging field: learning mechanics. We sat down with Jamie and Dan on Generally Intelligent to talk about what a physics of deep learning would actually look like, why now, and what's left to figure out. 00:03:05 Learning mechanics as the physics to mechanistic interpretability's biology 00:04:13 Why deep learning needs a theory 00:07:07 Why deep learning is uniquely hard to engineer 00:12:11 How a week in the woods became a paper 00:25:59 The barrier to theory isn't opacity, but complexity 00:36:26 Deep learning's first gas law 00:47:22 Why more particles makes the problem easier 00:56:22 The discretization hypothesis 01:01:50 The strongest signal that a compact theory exists 01:05:07 The Platonic Representation Hypothesis 01:15:41 Why learning mechanics and mech interp need each other 01:25:29 Theory as safety infrastructure Read the paper [https://arxiv.org/abs/2604.21691] Transcript and links [https://ideas.imbue.com/p/learning-mechanics] Learning Mechanics website [https://learningmechanics.pub/] Full transcript: https://imbueai.substack.com/p/geoffrey-litt Generally Intelligent is a podcast by ⁠⁠Imbue⁠⁠ [imbue.com], a research company building toward a future where AI agents are open and accountable to their users, so people have more power in the digital world. * Website [https://imbue.com/⁠⁠⁠] * Substack [https://ideas.imbue.com/⁠⁠] * ⁠X⁠ [x.com/imbue_ai⁠⁠⁠] * LinkedIn [https://www.linkedin.com/company/imbue_ai/⁠⁠⁠] * YouTube [https://www.youtube.com/@imbue_ai/⁠]

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 was a researcher at the independent lab, Ink & Switch [https://www.inkandswitch.com/], where he explored the future of computing. He did his PhD at MIT on programming interfaces. Most of his work circles around a very simple but powerful question: how can everyday people shape the software they use like clay so that humans can have more power and agency in the world? In this conversation, Geoffrey and Kanjun discuss: * Technical, economic, and infrastructural barriers to malleable software * Inventing new UI components for the AI age * Principles for agent-human collaboration * How AI affects the creative process * ...and more! Full transcript: https://imbueai.substack.com/p/geoffrey-litt Generally Intelligent is a podcast by ⁠Imbue⁠ [imbue.com], a research company building toward a future where AI agents are open and accountable to their users, so people have more power in the digital world. Website: ⁠https://imbue.com/⁠ [https://imbue.com/] Substack: ⁠https://ideas.imbue.com/ [https://imbueai.substack.com/] LinkedIn: ⁠https://www.linkedin.com/company/imbue_ai/⁠ [https://www.linkedin.com/company/imbue_ai/] X: ⁠@imbue_ai⁠ [x.com/imbue_ai] YouTube: ⁠https://www.youtube.com/@imbue_ai/ [https://www.youtube.com/@imbue_ai/]

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, political, and human impacts. Matt Boulos [boulos.ca] leads policy and safety at Imbue, where he shapes the responsible development of AI coding tools that make software creation broadly accessible. His work centers on understanding what technological power means for individual liberty and advocates for the legal and institutional frameworks we need to protect our freedom. Matt is a lawyer, computer scientist, and founder. A full transcript is available on our Substack: https://imbueai.substack.com/matt-boulos/ Highlights: * AI’s four core challenges * Governing lawless digital spaces * Why abundance is not enough without liberty * Freedom as deep enablement and deep protection * The role of technologists in shaping society Generally Intelligent is a podcast by Imbue [imbue.com], an independent research company developing a better way to build personal software. Our mission is to empower humans in the age of AI by creating powerful computing tools controlled by individuals. Website: https://imbue.com/ [https://imbue.com/] Substack: https://imbueai.substack.com/ [https://imbueai.substack.com/] LinkedIn: https://www.linkedin.com/company/imbue_ai/ [https://www.linkedin.com/company/imbue_ai/] X: @imbue_ai [x.com/imbue_ai] Bluesky: https://bsky.app/profile/imbue-ai.bsky.social [https://bsky.app/profile/imbue-ai.bsky.social] YouTube: https://www.youtube.com/@imbue_ai/ [https://www.youtube.com/@imbue_ai/]

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 well as other interesting refutations in the field that we’ll talk about today. He previously interned at Meta on the Llama team, and at Google DeepMind. Generally Intelligent is a podcast by Imbue where we interview researchers about their behind-the-scenes ideas, opinions, and intuitions that are hard to share in papers and talks. About Imbue Imbue is an independent research company developing AI agents that mirror the fundamentals of human-like intelligence and that can learn to safely solve problems in the real world. We started Imbue because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one. We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research. Our research is focused on agents for digital environments (ex: browser, desktop, documents), using RL, large language models, and self supervised learning. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research. Website: https://imbue.com [https://imbue.com] LinkedIn: https://www.linkedin.com/company/imbue_ai/ [https://www.linkedin.com/company/imbue-ai/] Twitter/X: @imbue_ai [x.com/imbue_ai]

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 on a variety of topics, including how training data leads to useful representations, lottery ticket hypothesis, and self-supervised learning. His work has been honored with Outstanding Paper awards at both NeurIPS and ICLR. Generally Intelligent is a podcast by Imbue where we interview researchers about their behind-the-scenes ideas, opinions, and intuitions that are hard to share in papers and talks. About Imbue Imbue is an independent research company developing AI agents that mirror the fundamentals of human-like intelligence and that can learn to safely solve problems in the real world. We started Imbue because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one. We have enough funding to freely pursue our research goals over the next decade, and our backers include Y Combinator, researchers from OpenAI, Astera Institute, and a number of private individuals who care about effective altruism and scientific research. Our research is focused on agents for digital environments (ex: browser, desktop, documents), using RL, large language models, and self supervised learning. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research. Website: ⁠https://imbue.com/⁠ [https://imbue.com/] LinkedIn: ⁠https://www.linkedin.com/company/imbue-ai/⁠ [https://www.linkedin.com/company/imbue-ai/] Twitter: @imbue_ai [x.com/imbue_ai]