Adam Marblestone – AI is missing something fundamental about the brain

What It Covers

Adam Marblestone explains why AI lacks fundamental brain mechanisms: evolution-encoded loss functions, omnidirectional inference, and a steering subsystem that creates specific reward signals. He argues neuroscience needs technological scaling to answer how brains achieve sample-efficient learning.

Key Questions Answered

• •Evolution's Loss Functions: The brain uses thousands of specific, genetically-encoded cost functions that activate at different developmental stages, not simple objectives like next-token prediction. Evolution compressed learning curricula into reward signals by encoding innate heuristics (spider detection, social status cues) that the cortex learns to predict, enabling generalization without explicit supervision.
• •Omnidirectional Inference vs Amortized Prediction: The cortex can predict any subset of variables from any other subset, unlike LLMs that only predict forward. This allows filling in blanks bidirectionally, predicting vision from audio or inferring causes from effects. Energy-based models may capture this better than current transformer architectures that amortize inference into feedforward passes.
• •Steering Subsystem Architecture: The hypothalamus and brainstem contain thousands of specialized cell types encoding innate behaviors, far more than cortical regions. These subcortical areas have their own primitive sensory systems (like superior colliculus for face detection) that provide reward signals the cortex learns to predict, solving how abstract concepts trigger instinctive responses.
• •Connectome Economics: Current electron microscopy costs billions per mouse brain, but optical approaches from E11 Bio could reduce costs to tens of millions. A molecularly-annotated connectome showing cell types and synapse properties across multiple mammal species would cost low billions total, trivial compared to AI training budgets approaching trillions.
• •Behavior Cloning with Neural Data: Training AI to predict both task labels and human brain activity patterns as auxiliary loss functions could improve generalization by matching how brains represent information. This regularization approach requires scaling portable brain scanning technology, currently a bottleneck compared to GPU availability for standard supervised learning.