[bulbar]

Just to be sure: The "neurons" in today's AI have nothing to do whatsoever with real neurons.

What we can do is simulate very simple brains by simulating relatively few neurons as they appear in worms. In this sense we are multiple magnitudes away where the increasing complexity implies exponential increasing difficulty.

I would think we are so far away that there will be unknown unknowns we encounter on the way.

[vkazanov]

Yes, physically absolutely nothing. But conceptually they seem to to form this very generic function from inputs to outputs that neurons also form.

[bux93]

Only if you ignore almost every input and output that neurons have.

https://www.quantamagazine.org/ai-is-nothing-like-a-brain-an... https://pmc.ncbi.nlm.nih.gov/articles/PMC9665914/

This is why making more neuromorphic NNs is still an active area of research, although they typically all focus on another extremely simplified model (spiking neural networks).

[vkazanov]

I don't ignore anything. I just refuse to accept the magical thinking around biological machines that are our brains/bodies. There are inputs, there are outputs, there is hidden function.

And it seems that, given enough input/outputs/compute, it is possible to train the necessary function.

Details of how the building bricks look like (matmul, electromagnetism or quantum effects) are not that relevant in the broader picture.

What is missing right now, is the fact that the function in question changes over time in biomachines, while our LLMs are static at inference time.

[davrosthedalek]

I mostly agree, but I see two points that might be problematic:

a) The brain might have an entropy source (then it can't be modeled as a function). Trivially to fix, and in some sense, with diffusion models starting from random numbers, AI has done so.

b) The hidden function might be not computable. I would have no idea how that would work, but I think this is what it boils down to if people say "the human brain is more than a machine".

[vkazanov]

a) enthropy can be injected as well. In fact there are hidden sources in current training.

b) well, it can be the case that, say, certain kinds of computation are either too inefficient or outright impossible within the current model.

Who knows...

[fc417fc802]

Agree and add, don't confuse the substrate for the computation. Of course it's also clear that we don't quite have a full and definite picture of what the computation consists of in the case of a biological brain as evidenced by our continued failure to accurately simulate even the simplest of organisms.

[lelanthran]

> Just to be sure: The "neurons" in today's AI have nothing to do whatsoever with real neurons.

Yeah, but it's hard to explain this to people, especially AI-pro people. Too many are convinced that all we are doing is a cut-down version of the human brain, and it's hard to explain to them that, no actually, we aren't modeling the human brain to the level of granularity you think we are.