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Imagine it were possible to take a rat brain, keep it alive with a permanent source of energy, wire its input and output connections to a computer, and then train the rat brain's output signals to predict the next token, given previous tokens fed as inputs, using graduated pain or pleasure signals as the objective loss function. All the neuron-neuron connections in that rain brain would eventually serve one, and only one, goal: predicting an accurate probability distribution over the next possible token, given previous tokens. The number of neuron-neuron connections in this "rat-brain-powered LLM" would be comparable to that of today's state-of-the-art LLMs.

This is less far-fetched than it sounds. Search for "organic deep neural networks" online.

Networks of rat neurons have in fact been trained to fly planes, in simulators, among other things.



Human brain organelles are in use right now by a Swiss company.


Thanks. Yeah, I've heard there are a bunch of efforts like that, but as far as I know, all are very early stage.

I do wonder if the most energy-efficient way to scale up AI models is by implementing them in organic substrates.




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