The Frontier

Adam Brown argues the most important unanswered question in science is how the human brain achieves high sample efficiency and general capabilities with far less data than modern LLMs. His meta-level take is that neuroscience needs a technological power-up to answer it.

Brown's personal hunch is that AI has neglected complex, developmentally staged loss functions. Evolution encodes a specific learning curriculum through many different loss functions, which could be the key to the brain's efficiency.

Brown suggests the cortex might be an omnidirectional inference engine, predicting any subset of variables from any other subset, unlike LLMs which are natively optimized only for next-token prediction.

Brown outlines Steve Byrnes' theory that the brain's learning subsystem learns to predict the innate responses of a separate steering subsystem, wiring abstract concepts like 'spider' to primitive reflexes like flinching and enabling generalization.

Single-cell atlas data shows many more diverse and bespoke cell types in subcortical steering regions like the hypothalamus than in the cortex. Brown interprets this as evidence that evolution's genomic complexity is spent wiring innate reward functions, not the general learning algorithm.

Brown notes the human genome is only about 3 GB, a small fraction of which codes for the brain. This compactness is plausible if evolution mainly writes 'Python code' for specific reward functions and bootstrapping rules, not the entire learned model.

Brown says current LLM training uses a 'really dumb' form of reinforcement learning without value functions, which is surprising it works so well. In contrast, parts of the basal ganglia may implement simple model-free RL, while the cortex builds a model-based system.

A key disadvantage of biological brains is they cannot be copied or externally read, unlike digital models. Advantages include energy efficiency, collocation of memory and compute, and hardware co-designed for potential stochastic, sampling-based inference.

Brown states that creating a competent, misaligned agent like a 'paperclip maximizer' likely requires only minimal innate drives for curiosity and exploration, not the full suite of human social instincts. This is an alignment concern.

Brown advocates for massively scaling up neuroscience to get a 'ground truth,' specifically by driving down the cost of connectomics. The Welcome Trust estimated the first mouse brain connectome would cost billions; E11 Bio aims to reduce it to tens of millions.

Brown describes a moonshot idea of 'behavior cloning' or brain-regularized AI, where models are trained not just on labels but also to predict internal brain activity patterns. This could shape representations and improve generalization, but requires scalable brain scanning tech.

On automated theorem proving, Brown says RL from formal verification, as in Lean, will automate the mechanical parts of math. The harder challenge is automating the conceptual creativity of conjecturing interesting new theorems, which might require a loss function for explanatory power.