Thoughts on thinking

My thoughts are that it's key that humans know they will still get credit for their contributions.

E.g. imagine it was the case that you could write a blog post, with some insight, in some niche field – but you know that traffic isn't going to get directed to your site. Instead, an LLM will ingest it, and use the material when people ask about the topic, without giving credit. If you know that will happen, it's not a good incentive to write the post in the first place. You might think, "what's the point".

Related to this topic - computers have been superhuman at chess for 2 decades; yet good chess humans still get credit, recognition, and I would guess, satisfaction, from achieving the level they get to. Although, obviously the LLM situation is on a whole other level.

I guess the main (valid) concern is that LLMs get so good at thought that humans just don't come up with ideas as good as them... And can't execute their ideas as well as them... And then what... (Although that doesn't seem to be the case currently.)

> I guess the main (valid) concern is that LLMs get so good at thought

I don't think that's a valid concern, because LLMs can't think. They are generating tokens one at a time. They're calculating the most likely token to appear based on the arrangements of tokens that were seen in their training data. There is no thinking, there is no reasoning. If they they seem like they're doing these things, it's because they are producing text that is based on unknown humans who actually did these things once.

> LLMs can't think. They are generating tokens one at a time

Huh? They are generating tokens one at a time - sure that's true. But who's shown that predicting tokens one at a time precludes thinking?

It's been shown that the models plan ahead, i.e. think more than just one token forward. [1]

How do you explain the world models that have been detected in LLMs? E.g. OthelloGPT [2] is just given sequences of games to train on, but it has been shown that the model learns to have an internal representation of the game. Same with ChessGPT [3].

For tasks like this, (and with words), real thought is required to predict the next token well; e.g. if you don't understand chess to the level of Magnus Carlsen, how are you going to predict Magnus Carlsen's next move...

...You wouldn't be able to, even just from looking at his previous games; you'd have to actually understand chess, and think about what would be a good move, (and in his style).

[1] https://www.anthropic.com/research/tracing-thoughts-language...

[2] https://www.neelnanda.io/mechanistic-interpretability/othell...

[3] https://adamkarvonen.github.io/machine_learning/2024/01/03/c...

Yes, let's cite the most biased possible source: the company that's selling you the thing, which is banking on a runway funded on keeping the hype train going as long as possible...