Recorded 07 November 2024. Kevin Ellis of Cornell University presents "Probabilistic Thinking in Language and Code" at IPAM's Naturalistic Approaches to Artificial Intelligence Workshop. Abstract: I will present work that tries to bridge Bayesian models of cognition with LLMs, treating both informal (natural) language and formal (programming) languages as candidate languages-of-thought for humanlike internal representations. First, I will define a class of Bayesian models that wrap around LLMs. Then, I will show ways in which the resulting models are more humanlike than either a raw LLM, or a conventional Bayesian cognitive model. Last, I will show engineering results suggesting how wake-sleep learning could fine-tune language models to be more effective at inductive reasoning by amortizing probabilistic inference. Learn more online at: https://www.ipam.ucla.edu/programs/workshops/workshop-iii-naturalistic-approaches-to-artificial-intelligence/?tab=overview