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.

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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.

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Episode 37: Rylan Schaeffer, Stanford: On investigating emergent abilities and challenging dominant research ideas

Generally Intelligent

Episode 37: Rylan Schaeffer, Stanford: On investigating emergent abilities and challenging dominant research ideas