What Do Ancient Brains and Future AIs Have in Common? with Read Montague

What do honeybee brains have in common with human brains — and with the AI that beat the world’s best Go player? What simple algorithm has been hiding inside brains for 100s of millions of years? When babies throw food from a high chair again and again, are they being mischievous or are they running physics experiments? And what does any of this have to do with whether dopamine is more than a pleasure molecule, or whether there are laws of physics we have yet to discover, or whether large language models are going to get outdated in favor of a new approach? Join Eagleman with computational neuroscientist Read Montague to explore how very old systems in our brains map onto the future of AI.

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Montague, P.R., Eagleman, D.M., McClure, S.M., Berns, G.S. (2002) Reinforcement Learning. In: Encyclopedia of Cognitive Science, MacMillan Press: London.

Schultz W, Dayan P, Montague PR. A neural substrate of prediction and reward. Science. 1997 Mar 14;275(5306):1593-9.

Montague PR, Dayan P, Person C, Sejnowski TJ. Bee foraging in uncertain environments using predictive hebbian learning. Nature. 1995 Oct 26;377(6551):725-8.

Montague PR, Dayan P, Sejnowski TJ. A framework for mesencephalic dopamine systems based on predictive Hebbian learning. Journal of neuroscience. 1996 Mar 1;16(5):1936-47.

Silver D, Sutton RS. Welcome to the era of experience. Google AI. 2025 Apr;1:11.

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