Uploaded April 2025 | Updated September 2026, 1 week ago

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Kevin Ellis (Cornell) and Zenna Tavares (BASIS) argue that the next wave of AI needs to learn like humans do: building abstract models from small amounts of data through active exploration, not just passive pattern matching at scale.

The conversation centers on their joint work comparing two fundamentally different ways of solving problems. Induction searches for an explicit program -- something you could write in Python -- that transforms inputs to outputs. Transduction skips the program and directly predicts the answer, the way a neural network would. On the Abstraction and Reasoning Corpus (ARC), these approaches turn out to be complementary: some problems yield to systematic symbolic search, others to neural intuition. The ensemble is stronger than either alone, and the reasons connect to findings in cognitive science about when explicit reasoning helps versus hurts.

Kevin explains how his DreamCoder work pioneered a wake-sleep cycle for program synthesis: dream up programs, run them to see what they do, learn the inverse mapping, then wake up and let real-world failures adjust the distribution of dreams. The modern version replaces explicit symbolic libraries with in-context learning over LLM-generated code, keeping the same iterative refinement loop.

Zenna introduces his Autumn system for synthesizing the source code of interactive environments from observed behavior -- a form of computational science where the model must also infer hidden state it cannot directly observe. Both researchers converge on the idea that abstraction is the key unsolved problem: real intelligence requires knowing what to ignore, not just what to represent. Zenna frames this through resource rationality -- choosing the right level of abstraction given your computational budget and expected tasks.

The discussion closes with Project MARA, their joint effort to build interactive benchmarks that go beyond ARC's static puzzles, requiring agents to actively explore and build world models from scratch.

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REFERENCES:
Paper:
[00:00:25] DreamCoder: Growing Generalizable, Interpretable Knowledge with Wake-Sleep Bayesian Program Learning
arxiv.org/abs/2006.08381
[00:01:10] Mind Your Step: Active Search over Compositional Spaces
arxiv.org/abs/2410.21333
[00:06:05] Bayesian inference in the cognitive sciences
psycnet.apa.org/record/2008-06911-003
[00:13:00] Induction and Transduction
arxiv.org/abs/2411.02272
[00:23:15] Neurosymbolic AI: The 3rd Wave
arxiv.org/abs/2012.05876
[00:38:35] On the Measure of Intelligence (ARC)
arxiv.org/abs/1911.01547
[00:39:20] Causal Reactive Programs (Autumn)
zenna.org/publications/autumn2022.pdf
[00:42:50] MuZero
arxiv.org/pdf/1911.08265
[00:43:20] VisualPredicator
arxiv.org/abs/2410.23156
Book:
[00:48:55] Bayesian Models of Cognition
https://mitpress.mit.edu/9780262049412/bayesian-models-of-cognition/
Essay:
[00:49:30] The Bitter Lesson
incompleteideas.net/IncIdeas/BitterLesson.html
Project:
[01:11:55] Project MARA
basis.ai/blog/mara

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LINKS:
Full Transcript: app.rescript.info/share/e0a208e545cabae728a3d72f76fcd310
Download PDF transcript: app.rescript.info/api/public/sessions/f47975e800b064d9/pdf