Unless you're using some mystical conception of "reason", nothing about being able to "reason based on prior data points" translates to "we very clearly are not a series of weights for probable next tokens".

And in fact LLMs can very well "reason based on prior data points". That's what a chat session is. It's just that this is transient for cost reasons.

>We very obviously are not just a series of weights for probable next tokens.

How exactly? Except via handwaving? I refer to the "brain as prediction machine theory" which is the dominant one atm.

>you can even ask an LLM and it will tell you our brains work differently to it

It will just tell me platitudes based on weights of the millions of books and articles and such on its training. Kind of like what a human would tell me.

>and that’s not even including the possibility that we have a soul or any other spiritual substrait.

That's good, because I wasn't including it either.

Our brains work differently, yes. What evidence do you have that our brains are not functionally equivalent to a series of weights being used to predict the next token?

I'm not claiming that to be the case, merely pointing out that you don't appear to have a reasonable claim to the contrary.

> not even including the possibility that we have a soul or any other spiritual substrait.

If we're going to veer off into mysticism then the LLM discussion is also going to get a lot weirder. Perhaps we ought to stick to a materialist scientific approach?

Its really just a matter of degrees. There are 1 million, 1 million, 1 trillion parameter LLMs... and you keep scaling those parameters and you eventually get to humans. But it's still probable next tokens (decisions) based on previous tokens (experience).

>We are much more than weights which output probable next tokens. You are a fool if you think otherwise. Are we conscious beings? Who knows, but we’re more than a neural network outputting tokens.

That's just stating a claim though. Why is that so?

Mine is reffering to the "brain as prediction machine" establised theory. Plus on all we know for the brain's operation (neurons, connections, firings, etc).

>There are parts of our brains which are understood (kinda) and there are parts which aren’t. Some parts are neural networks, yes. Are all?

What parts aren't? Can those parts still be algorithmically described and modelled as some information exchange/processing?

>but the training humans get is coupled with the pain and embarrassment of mistakes

Those are versions of negative feedback. We can do similar things to neural networks (including human preference feedback, penalties, and low scores).

>the ability to learn while training (since we never stop training, really)

I already covered that: "The main difference is the training part and that it's always-on."

We do have NNs that are continuously training and updating weights (even in production).

For big LLMs it's impractical because of the cost, otherwise totally doable. In fact, a chat session kind of does that too, but it's transient.

They're not artificial intelligence neural networks.

They're biological neural networks. Brains are made of neurons (which Do The Thing... mysteriously, somehow. Papers are inconclusive!) , Glia Cells (which support the neurons), and also several other tissues for (obvious?) things like blood vessels, which you need to power the whole thing, and other such management hardware.

Bioneurons are a bit more powerful than what artificial intelligence folks call 'neurons' these days. They have built in computation and learning capabilities. For some of them, you need hundreds of AI neurons to simulate their function even partially. And there's still bits people don't quite get about them.

But weights and prediction? That's the next emergence level up, we're not talking about hardware there. That said, the biological mechanisms aren't fully elucidated, so I bet there's still some surprises there.