Dr. Jeff Beck discusses how our brains might process information and learn, contrasting it with how current AI, especially language models, operate. He suggests that while AI can seem intelligent by recognizing patterns (like identifying a famous test without actually solving it), it may lack genuine understanding because it doesn't have real-world, physical experiences like humans do.
A major topic is the challenge of figuring out someone's true goals (their "reward function") separate from their understanding of the world (their "beliefs"), just by observing what they do. Beck argues this is fundamentally difficult, possibly impossible, which creates big problems for ensuring AI systems behave in ways we want (AI alignment). He emphasises that simply predicting the next word or action isn't the same as true intelligence or creativity, and highlights the importance of building systems that can genuinely create new things, much like how humans use engineering principles.
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TRANSCRIPT:
https://www.dropbox.com/scl/fi/vq91fbspln8favi7iqjd0/JEFFBECK.pdf?rlkey=wdrhuhgxo6ljiwmw690lcjcx7&dl=0
Dr. Jeff Beck:
https://www.noumenal.ai/
TOC:
1. Neural Computation Fundamentals
[00:00:00] 1.1 Bayesian Brain and Neural Computation Foundations
[00:05:10] 1.2 Theory of Mind and LLM Capabilities
[00:09:08] 1.3 Systems Engineering and Object Decomposition
[00:15:19] 1.4 Language and Shared Mental Models
2. Cognitive Processing and Language Models
[00:18:50] 2.1 Information Processing Bottleneck in Human Cognition
[00:22:54] 2.2 Language Models and Understanding Limitations
[00:25:25] 2.3 Scientific Abstraction and Markov Blankets
[00:31:01] 2.4 Mathematical Reality and Scientific Realism
3. AI Systems and Value Alignment
[00:32:15] 3.1 Neuroscience Research Methodology and Statistical Regularities
[00:34:17] 3.2 Bayesian Modeling and Free Energy Principle
[00:40:08] 3.3 AI Reward Functions and Human Value Alignment
[00:45:00] 3.4 Belief Formation Systems and AI Implementation
Extended version on patreon:
https://www.patreon.com/posts/jeff-beck-125455115
REFS:
[00:00:15] Bayesian inference in neural computation, Wei Ji Ma, Jeffrey M Beck, Peter E Latham, Alexandre Pouget
https://www.nature.com/articles/nn1790
[00:01:50] Noumenal Labs AI research initiative, Jeff Beck
https://arxiv.org/html/2502.13161v1
[00:05:25] LLM performance on theory of mind tasks, Michal Kosinski
https://arxiv.org/abs/2302.02083
[00:16:25] Bayesian Mechanics: A Physics of and by Beliefs, Maxwell J. D. Ramstead, Dalton A. R. Sakthivadivel, Conor Heins, Magnus Koudahl, Beren Millidge, Lancelot Da Costa, Brennan Klein, Karl J. Friston
https://arxiv.org/abs/2205.11543
[00:17:10] Building Human-like Communicative Intelligence: A Grounded Perspective, Marina Dubova
https://arxiv.org/abs/2201.02734
[00:20:45] Why do we live at 10 bits/s?, Markus Meister, Jieyu Zheng
https://www.sciencedirect.com/science/article/abs/pii/S0896627324008080
[00:22:25] Steven Piantadosi, Tim Scarfe
https://colala.berkeley.edu/people/piantadosi/
[00:23:35] Mad Libs, Leonard Stern and Roger Price
https://en.wikipedia.org/wiki/Mad_Libs
[00:25:25] The Brain Abstracted, Mazviita Chirimuuta
https://mitpress.mit.edu/9780262548045/the-brain-abstracted/
[00:28:30] Markov blankets in biological systems, Karl Friston
https://royalsocietypublishing.org/doi/10.1098/rsif.2017.0792
[00:31:05] Mathematical Platonism, Øystein Linnebo
https://plato.stanford.edu/entries/platonism-mathematics/
[00:32:15] Critique of Gabor patches use, Thomas Tsao
https://pmc.ncbi.nlm.nih.gov/articles/PMC9564096/
[00:35:55] MaxEnt and MaxCal principles in statistical physics, Steve Pressé et al.
https://journals.aps.org/rmp/abstract/10.1103/RevModPhys.85.1115
[00:40:10] Reward is enough, David Silver, Satinder Singh, Doina Precup, Richard S. Sutton
https://www.sciencedirect.com/science/article/pii/S0004370221000862
[00:45:40] Modeling Human Beliefs about AI Behavior for Scalable Oversight, Leon Lang and Patrick Forré
https://www.arxiv.org/pdf/2502.21262