Its a complex adaptive system, right? Isn't that the whole idea? We know how each part of the system works by itself. We know all inputs, and can measure outputs.

I still (even if I actually understood the math) cannot tell you 'If you prompt 'x', the model will return 'y' with 100% confidence.

▲

sirwhinesalot 2 days ago | parent [-]

> If you prompt 'x', the model will return 'y' with 100% confidence.

We can do this for smaller models. Which means it's a problem of scale/computing power rather than a fundamental limitation. The situation with the human brain is completely different. We know neurons exchange information and how that works, and we have a pretty good understanding of the architecture of parts of the brain like the visual cortex, but we have no idea of the architecture as a whole.

We know the architecture of an LLM. We know how the data flows. We know what it is the individual neurons are learning (cuts and bends of a plane in a hyperdimensional space). We know how the weights are learned (backpropagation). We know the "algorithm" the LLM as a whole is approximating (List<Token> -> Token). Yes there are emergent properties we don't understand but the same is true of a spam filter.

Comparing this to our lack of understanding of the human brain and discussing how these things might be "conscious" is silly.

	▲	ninetyninenine 2 days ago \| parent [-]
		>Comparing this to our lack of understanding of the human brain and discussing how these things might be "conscious" is silly. Don't call my claim silly. I'm sick of your attitude. Why can't you have a civil discussion? Literally we don't know. You can't make a claim that it's silly when you can't even define what consciousness is. You don't know how human brains work, you don't know how consciousness forms, you don't know how emergence in LLMs work. So your claim here is logically just made up out of thin air. Sure we "understand" LLMs from the curve fitting perspective. But the entirety of why we use LLMs and what we use it for arises from the emergence which is what we don't understand. Curve fitting is like 1% of the LLM, it is the emergent properties we completely don't get (99%) and take advantage of on a daily basis. Curve fitting is just a high level concept that allows us to construct the algorithm which is the actual thing that does the hard work of wiring up the atomic units of the network. >Yes there are emergent properties we don't understand but the same is true of a spam filter. Yeah and so? Your statement proves nothing. It just illustrates a contrast in sentiment. The spam filter is a trivial thing, the human brain is not. We don't understand the spam filter. And this is the most interesting part of it all is that the SAME scaling problem that prevents us from understanding the spam filter can be characterized as the reason that prevents us from understanding BOTH the LLM and the human brain. Your statement doesn't change anything. It's just using sentiment to try to re-characterize a problem in a different light.