[seydor]

sophisticated is not a scientific word. they are complex and complicated, and the voltage dynamics across their elaborate membrane takes a lot of computers to simulate. But we don't really know what it is doing or if it is particularly sophisticated. Nature has found a lot of complex solutions to simple problems because it does not know better. We don't know how well it did with intelligence

[xvilka]

We already do know[1] the a single neuron has the same level of complexity as multilayered digital "neural network".

[1] https://www.youtube.com/watch?v=hmtQPrH-gC4

[seydor]

There are different studies proposing 2 or 3 layer network for representing the input-firing curve of neurons (Usually hippocampal). Of course, neural networks are abritrary approximators so the size of the network determines the fidelity of the reproduction. But it 's not clear what the firing does and what amount of complexity in the firing code is reduntant or useful for making AI systems

[uoaei]

What is clear however is the evident power savings in implementing cultured neural networks vs digital ones for a given network capacity.

[p1esk]

Even that is not clear. A model like GPT-4 can read an entire book in seconds, and produce an intelligent answer about its content [1]. A human would need at least several hours to perform the same task.

[1] https://www.anthropic.com/index/100k-context-windows

[uoaei]

> for a given network capacity

You'd be hard-pressed to find an expert who believes any of the current crop of LLMs have a similar capacity to human brains.

[p1esk]

Unlike the capacity of human brains the capacity of ML models has been growing very fast in the last 10 years. The number of tasks AI cannot do is shrinking fast.