- Human brains are estimated to have a few hundred trillion synapses. If you tried to replicate this in a neural network model with one parameter per synapse, it would be much larger than the largest models in use today.

- Conventional wisdom in form of the Chinchilla scaling law suggests that to train such a gargantuan model, you would need an even more gargantuan training corpus.

- But no human has read anywhere near as much as even relatively small Chinchilla-optimal models. In fact, rather than acquiring as much data as possible as efficiently as possible, children might rather rewatch the exact same video for the umpteenth time. When they learn arithmetic, it's from just a paltry few examples provided by the teacher in school.

- Large neural networks trained on such little training data would quickly memorize it perfectly and overfit horribly.

- Individuals with photographic memory demonstrate that human brains indeed have the memorization capacity you would expect based on synapse count, and appear to show difficulties with generalization as a side-effect.

- Speculatively, typical humans forget and generalize instead of memorizing because synaptic strengths are reduced during sleep in an analogue to regularization by weight decay.

- Therefore, maybe we should train extremely large models on little data with extremely strong weight decay to counteract memorization, and hope a large learning rate will quickly "catapult" it to a generalizing solution.

What I'm missing is a discussion of how much this would cost, even if you handle deployment by distillation into smaller, faster, less data-efficient models.

That’s an extremely steep claim with no source other than vibes. Last time I checked my biology notes, model parameters are neurons, and they cost a ton of energy to maintain. Your hypothesis is really far removed from any actual neuroscience. Also, where are those filtered datasets coming from? Do you think genetics hands them to us? There’s about zero evidence for this claim as well. I like new concepts for ML research but please do not make up theories of human cognition when you clearly have no idea about it.