DeepMind's Andrew Lampinen: where AI and brains converge on similar solutions reveals invariant computational principles
AndrewLampinen · x · 2026-10-06
In a talk thread from a CiNeT / University of Osaka conference on AI & neuroscience, DeepMind researcher Andrew Lampinen argues that structural differences between LMs and brains can be useful: when such different systems arrive at similar solutions to a task, that points toward the invariant computational properties a solution must have. He illustrates this with two case studies — content effects on logical reasoning and latent learning/replay as data augmentation. The same data-augmentation framework appears in his new COBS perspective piece with Tyler Raye, proposing it as a lens for understanding hippocampal contributions to generalization. Further tests involve varying data structure or system constraints on the AI side.
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