Which is what we will eventually realize is what humans are doing too.
When humans write, they are serializing thoughts. Humans (well, most of us. Certainly not AI enthusiasts), are reasoning and thinking.
When AI writes, it is following a mathematical pathway to string words together that it has seen together before in the given context.
And if it wasn't obvious, an LLM can string together two words that it had never seen together in the training dataset, it really shows how people tend to simplify the extremely complex dynamics by which these models operate.
AIs do not “think” in any capacity and are therefore incapable of reasoning. However, if you wish to take “thinking” out of the definition, where we allow an AI to try its hand at “novel (for it)” problems, then AIs fail the test horrifically. I agree, they will probably spit something out and sound confident, but sounding confident is not being correct, and AIs tend to not be correct when something truly new to them is thrown at them. AIs spit out straight incorrect answers (colloquially called “hallucinations” so that AI enthusiasts can downplay the fact that it is factually wrong) for things that an AI is heavily trained on.
If we train an AI on what a number is. But then we slap it with 2+2 =5 long enough, it will eventually start to incorrectly state that 2+2=5. Humans, however, due to their capacity to actually think and reason, can confidently tell you, no matter how much you beat them over the head, that 2+2 is 4 because that’s how numbers work.
Even if we somehow get a human to state that 2+2=5 as an actual thought pattern, they would be capable of reasoning out the problems the moment we start asking “what about 2+3?” Where an AI might make the connection, but there no forward thinking won’t resolve the issue.
Depends on what your definition of a novel problem is. If it's some variation of a problem that has already been seen in some form in the training data, then yes. But if you mean a truly novel problem—one that humans haven't solved in any form before (like the Millennium Problems, a cancer cure, new physics theories, etc.)—then no, LLMs haven't solved a single problem.
> And if it wasn't obvious, an LLM can string together two words that it had never seen together in the training dataset, it really shows how people tend to simplify the extremely complex dynamics by which these models operate.
Well, for anyone who knows how latent space and attention work in transformer models, it's pretty obvious that they can be used together. But I guess for someone who doesn't know the internals, this could seem like magic or reasoning.
