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by Philadelphia 1045 days ago
It also can’t learn. Once the training is done, the network is set in stone.
3 comments

Technically it can do in-context learning (and really well, too), but that's not persisted into the network.
And that just seems like an engineering problem. Not something that is considered intractable.
It's easy to say that, but "surely it must be possible to connect an llm in such a way that it becomes intelligent" (tell me if I'm misinterpreting) is not a demonstration of anything. It's basically restating the view from the 50s that with computers having been invented, an intelligent computer is a short way off.
What do you mean by "learn"?

The network has learned human patterns of language, knowledge and information processing. If you want to update that, you can re-train it on a regular basis, and re-play its sensory/action history to "restore" its state.

If you mean "learn from experience", (1) a lot of that is pointless because it's already learned from the experiences of millions of humans through their writing and (2) LLMs can "learn" when you explain consequences.

In theory they could learn by having their discussions fed back to them in the future, and it does seem that this occurs.

Now, there is no continuous learning in the human/animal sense. Of course it is thought that even humans have to sleep and re-weight their networks so short term knowledge is converted to long term knowledge.

Makes me wonder why we don’t see deployed models that keep learning during inference.
Microsoft tay has entered the chat
The curse of dimensionality and exploding/vanishing gradients are why incremental learning is still so rare.