[benterix]

> Who is to say our brains aren't just very high parameterized biological floating point machines? That is the true Occam's Razor here, as uncomfortable as that might make people.

I believe it's quite possible that what is happening during training is in certain ways similar to what is happening to a child learning the world, although there are many practical differences (and I don't even mean the difference between human neurons and the ones in a neural network).

Is there anything to feel uncomfortable about? It's been a long time since people started discussing the concept of "a self doesn't exist, we're just X" where X was the newest concept popular during that time. I'm 100% sure LLMs are not the last one.

(BTW as for LLMs themselves, there are still two big engineering problems to solve: quite small context windows and hallucinations. The first requires a lot of money to solve, the second needs special approaches and a lot of trial and error to solve, and even then the last 1% might be almost impossible to get working reliably.)

[strangescript]

I am not convinced hallucinations is a solvable problem in a single self contained model. In one hand we approach these things with the idea we are building a real intelligence modeled after our assumptions of our own brain. In the other we want them to not have any of the failings of the things they are being modeled after.

Humans mis-remember and make up things all the time, completely unintentionally. It could be a fundamental flaw in large neural networks. Impressive data compression and ability to generalize, but impossible to make "perfect".

If AI becomes cheap and fast enough, its likely a simple council of models will be enough to alleviate 99% of the problem here.

[benterix]

Yes, I agree the council approach is the most reasonable option. But while you're right about mistakes being inherently human, there is a huge difference in both quantity and quality. Humans often err in details, LLMs can make false claims in a very confident way.