People like to focus on the differences between the brain and artificial neural networks. I myself believe the only thing that truly matters is that you can form complex functions with the common neuron element. This is achieved via linking lots them together, and by each having a property known as non-linearity. These two things ensure that with neurons you can just about approximate any linear or non-linear function or behaviour. This means you can simulate inside your network pretty much any reality within this universe, its causation and the effects.
The deeper your network the more complex the reality you can "understand". Understand just means simulate and run inputs to get outputs in a way that matches the real phenomenon. When someone is said to be "smart", it means they possess a set of rules and functions that can very accurately predict a reality.
You mention scale, and while its true the number of neuron elements the brain has is larger than any LLM, its also true the brain is more sparse, meaning much less of the neurons are active at the same time. For a more fair comparison, you can also remove the motor cortex from the discussion, and talk just about the networks that reason. I believe the scale is comparable.In essence, I think it doesn't matter that the brain has a whole bunch of chemistry added into it that artificial neural networks don't. The underlying deep non-linear function mapping capability is the same, and I believe this depth is, in both cases, comparable.
tuyiown|4 months ago
Maybe we've just reach the ability the replicate the function of an artificially powered dead brain that would be randomly stimulated and nothing more. Is this really a path to intelligence ?
luisml77|4 months ago