By self-modifying the software. Currently the model harnesses only allow the model to modify its own prompt (which could be considered a really weak kind of learning), but theoretically, a model could design and train its own replacement and run that, continuously improving itself. I’m not sure if LLMs will be able to do that but the static hardware has nothing to do with it (since the bits on the harddrive aren’t static).
this is profoundly false. AI not only can learn, it is built entirely from learning. The field is called machine learning after all.
Not only that... AI is NOT only learning during the training phase... LLMs learn in real time the minute you talk to it. It learns something and saves those learnings in a context window or somewhere else if you want it to exist beyond the context window.
All of the above runs on static hardware. Don't understand how someone can say a profoundly wrong statement and get voted up.
Correct me if I'm wrong, but if a profound insight is gathered in session 1 with user A and stored in context A1, this might be available to user A in session 2, if that still has access to context A1, but won't be available to user B in any of his/her sessions until that NN is retrained with input which includes at least some of the information from context A1.
You are wrong again in every possible count. Since you think in terms of algebra think like this: there is context SP which is context window shared between all sessions, that is called the system prompt. Then there is the context window called memory M which is shared between all sessions under a user and then finally there context CW (current window) which is the context comprised of the queries from the current chat session. Total context = SP + CW + M.
M is the context window that doesn’t require retraining that allows the LLM to “learn” in the same way humans do. This is the usual set up. But nothing prevents someone from adding a GM (general memory) shared between all users. Under this set up the LLM and harness fits and is virtually identical to how humans learn at a high level.
But this is besides the point because even if none of this was done. Just a context window or just training is in itself a form of learning. There was no action taken in your algebraic example where learning did not occur.
Also with the stochastic parrot thing. If you say just the right thing to the right human and the right time, they'll very predictibly say their favorite movie/book quote or song lyric, like some sort of parrot.
And also, if you put an actual parrot in a room full of programmers, it might learn a few word of programming jargon, but it never will become a useful coding partner the way LLMs are.
An LLM will tell you how a song feels, even if it has literally no way to experience music. Because it's not thoughts or feelings that you get from an LLM. We take a massive amount of information, compress it into a large graph, then explore sections of the graph via prompts. That's what the stochastic parrot means. And that doesn't compare with how humans think. It's just a completely different architecture
AI can not.
For those disagreeing: please explain how a static hardware can learn.