[clooper]

What are the dimension of the input and output spaces involved in this idealization? In the case of a neural network there is no idealization. The network is software, it's a number. It's inputs and outputs are all bounded and can be expressed as a table of bounded tuples.

[mewpmewp2]

You could pick a very large number depending on a reasonable processing capability a human has, which represents all the significant physical interactions on a human body over a certain amount of time. Then take the output over a certain amount of time, being all movements of the body.

If you wanted to focus on thoughts alone, you might want to skip few layers/systems, to give input directly to whatever causes thoughts to happen.

All particles and their interactions could also be represented as numbers. But it just depends on what level we do this, and at what level what kind of complex logic is required.

[clooper]

Ok so give me some concrete number.

[ccozan]

I think the OP is right. All the input to a human brain can be expressed as numbers, at any given time a specific radiation, vibration, or chemical reaction is hitting our "sensors" and by the law of physics this is just numbers ( in terms of differentiation, brain does not know absolute values ).

Our output ( mechanical and vibrations ) is also fully quantifiable, thus numbers.

One giant lookup table.

[clooper]

Provide some concrete numbers for solar radiation then as a lookup table. You guys are confusing abstraction and idealization with what it means to be a thinking person. There is no such abstraction and idealization happening with software. The software is really just a number, there is no idealization or abstraction happening when I claim that GPT is a sequence of bits representing a numerical function.