[dbrueck]

I believe that this is one of the key takeaways for reasoning about LLMs and other seemingly-magical recent developments in AI:

"tasks—like writing essays—that we humans could do, but we didn’t think computers could do, are actually in some sense computationally easier than we thought."

It hurts one's pride to realize that the specialized thing they do isn't quite as special as was previously thought.

[wredue]

Computers still aren’t writing essays. They are stringing words together using copied data.

If they were writing essays, I would suggest that it wouldn’t be so ridiculously easy to pick out the obviously AI articles everywhere.

[dboreham]

> They are stringing words together using copied data.

Which is what we will eventually realize is what humans are doing too.

[wredue]

It absolutely is NOT what humans are doing.

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.

[GaggiX]

When an LLM solves a novel problem, it's also reasoning, unless you use some contrived definition of the word "reasoning" that doesn't match how the word is actually used in normal conversation. Also I fully expect the human brain to be encoded in a mathematical model.

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.

[wredue]

No. It is the AI enthusiasts that use contrived, often entirely random definitions of “reasoning”.

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.

[GaggiX]

These arguments really make no sense: "it can't reason because it can't think" very comprehensive ahah

Also if you train an AI on how numbers work, like humans do in school, you can tell it how much you want to believe that 2+2=5, it won't believe it, just like humans, it's obvious.

[lossolo]

> When an LLM solves a novel problem, it's also reasoning, unless you use some contrived definition of the word "reasoning" that doesn't match how the word is actually used in normal conversation. Also I fully expect the human brain to be encoded in a mathematical model.

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.

[GaggiX]

>then no, LLMs haven't solved a single problem.

Using your definition of a novel problem, do most people solve novel problems? If so, give me an example of a novel problem you have solved.

[GaggiX]

>They are stringing words together using copied data

Ah yes and image generators are just rearranging stolen pixels.