Next token predictor is not a very good argument. A submarine does not need to be a fish to swim.

Saying it is a 'next token predictor' should not diminish what LLMs are capable of. It is incredible what can be achieved by a simple rule like this.

Look what we built with only 2 digits: 0 and 1.

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LLMs cannot, by their core design, predict next word correctly every time. That is the main thing you are missing. Another thing is that I know that I am not predicting next word based on othr words in my "trainin data", there is much deeper understanding of most subjects that I am using to "predict next word". This may not be true for everyone, I cannot know for sure, but it might be possible that there are people out there who are just biological LLMs, and in that case I am certain those people will not be able to predict next word Einstein would say either.

> If you can predict the exact next word Einstein is about to write, every time, you’re as intelligent as Einstein!

Einstein is not hold in such esteem because he was really could at spewing out text or formal proofs or theorems, but because he was able to invent something no-one else could. Some people say he advanced physics 100 years wuth his theories in relativity, whether it is true or not.

So even if you can predict what Albert is going to write next, you are still merely copying old Albert. They are amazing at processing large amounts of data, naturally, but creating something new and surprising we have yet to see evidence of (the solutions of the millennium problems seem to be brute-forcing a solution based on fuckton of data to me, but I am not a mathematician so I guess it is up in the air still).

The main criticism is, imo, that these models cannot think and have no logic. This is proven time and time again. Every day something new comes out, like counting Rs or the ”drive or walk“ test etc. These are the ”fixed“ or rather band-aided on a case by case basis, but nobody seems to be able to fox the underlying problem.

Having done my master‘s thesis on Transformer LMs, I think this is a fundamental limitation of the architecture.

These models can solve math problems and then fail to count the number of problems they solved. Not being able to count to 68 is not intelligence.

It seems you are just gatekeeping intelligence.

"Not being able to quickly reference the sum corpus of human knowledge across all fields, or contextually learn new languages in moments, or grasp the overarching architecture and modules of a million+ line codebase is not intelligence."

This would be a pretty ridiculous statement don't you think?

You are operating on a narrow, human-centric view of intelligence here for some reason... while I bet you would say an Orca is intelligent. So what drives you to place an LLM so low on the intelligence totem pole? This is clearly bias.

The fact that it is programmed is irrelevant. The fact that at it's core it is an ingestion of human data and an output of a prediction of more data doesn't declassify LLM's as intelligent. Unless you are willing to put that same lens against how humans learn. (you aren't)

Not that these distinctions are meaningful in any way, by the way, in light of raw functionalism. I am just annoyed by the clear bias.

Why would being able to quickly reference all of human knowledge be relevant at all? Intelligence is not about speed, it is about correctness, which is precisely why it's concerning that frontier models keep stumbling on things that should be piss easy to get correct.

Else by your logic the exact same model running on hardware that's half as powerful is somehow half as "intelligent"

And no, humans and other species don't learn the way that LLMs do. That's fairly obvious even if one isn't a neuroscience nerd.

It's relevant because it is another type of intelligence I was highlighting.

> Intelligence is not about speed, it is about correctness

What...there is a whole realm of correctness in the example I gave. Just because I didn't say it correctly references human knowledge, you presume correctness isn't implicit in that?

You don't know what you're talking about here. I was making philosophical and neuroscientific references that you are just blowing past for no reason. Lol

“It” is not “thinking” for christ’s sake, it’s a massive statistical regression curve fit to a massive, frozen library of text. Unless you’re thinking of “thinking mode” which still is just a template that tells the model to ask questions, which are asked one token at a time, turtles all the way down.

This may be news for some, but they actually train these things with RL now, not just pretraining on a library of text.

The thinking part is still not a “natural” or “organic” process happening during inference.

> I never understand the “only a next token predictor” argument

Yeah, that's typically the case for people who have no clue about LLMs or AI.

“Predicting the next token” is a bad argument, as it completely ignores all the emergent properties of transformer models. It’s like saying that discreet electrical impulses from sodium/ potassium transporters is the beginning and end of what neuroscience describes.