Can AI Foundation Models Actually Explain How the Brain Works?
Tech giants and academic labs are pouring millions into training AI foundation models on massive amounts of brain data. While these models get good at predicting brain activity, top scientists are locked in a debate over whether accurate predictions actually mean we understand the brain, or if we are just building another black box that we still have to figure out.
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The Brain Data Rush
Researchers are applying the recipe behind large language models to neuroscience, training neural networks on vast brain recordings. Meta, Stanford, and startups have released or are building brain foundation models, hoping that scale will make them smarter.
The Great Debate
Optimists argue that models reproducing brain activity can act as stand-ins for living subjects and help us discover new biological insights. Skeptics counter that fitting brain data does not guarantee the model works like a real brain, nor does it grant mechanistic understanding. Scaling laws for brain data also remain murky compared to language models.
The Path Forward
Some suggest combining data-driven models with task-trained models, while others argue that true intelligence requires modeling the whole body and physical interactions, reminding the field that scaling data might not be a silver bullet.
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