Generally Intelligent
Technical discussions with deep learning researchers who study how to build intelligence. Made for researchers, by researchers.
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Episode 37: Rylan Schaeffer, Stanford: On 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 Lla
Episode 36: Ari Morcos, DatologyAI: On 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-
Episode 35: Percy Liang, Stanford: On 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, modular, and robust, and how they shift the way people interact with AI—although he’s been working on language models for long b
Episode 34: Seth Lazar, Australian National University: On 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 political philosophy with AI, introducing much-needed rigor to the question of what will make for a good and just AI future. Ge
Episode 33: Tri Dao, Stanford: On 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 systems, currently focused on efficient training and long-range context. About Generally Intelligent We started Generally
Episode 32: Jamie Simon, UC Berkeley: On 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 fundamental understanding of deep neural networks so they can be designed from first-principles. In this episode, we discuss revers