I don't think the article summarizes the research paper well. The researchers gave the AI models simple-but-large (which they confusingly called "complex") puzzles. Like Towers of Hanoi but with 25 discs.

The solution to these puzzles is nothing but patterns. You can write code that will solve the Tower puzzle for any size n and the whole program is less than a screen.

The problem the researchers see is that on these long, pattern-based solutions, the models follow a bad path and then just give up long before they hit their limit on tokens. The researchers don't have an answer for why this is, but they suspect that the reasoning doesn't scale.

When are people going to realize, in its current state , an LLM is not intelligent. It doesn’t reason. It does not have intuition. It’s a word predictor.

OK, and? A car doesn't run like a horse either, yet they are still very useful.

I'm fine with the distinction between human reasoning and LLM "reasoning".

It's all "one instruction at a time" regardless of high processor speeds and words like "intelligent" being bandied about. "Reason" discussions should fall into the same query bucket as "sentience".

It's not just the memorization of patterns that matters, it's the recall of appropriate patterns on demand. Call it what you will, even if AI is just a better librarian for search work, that's value - that's the new Google.

XD so, like a regular school/university student that just wants to get passing grades?

I see a lot of misunderstandings in the comments 🫤

This is a pretty important finding for researchers, and it's not obvious by any means. This finding is not showing a problem with LLMs' abilities in general. The issue they discovered is specifically for so-called "reasoning models" that iterate on their answer before replying. It might indicate that the training process is not sufficient for true reasoning.

Most reasoning models are not incentivized to think correctly, and are only rewarded based on their final answer. This research might indicate that's a flaw that needs to be corrected before models can actually reason.

So, what your saying here is that the A in AI actually stands for artificial, and it's not really intelligent and reasoning.

Huh.

NOOOOOOOOO

SHIIIIIIIIIITT

SHEEERRRLOOOOOOCK

What's hilarious/sad is the response to this article over on reddit's "singularity" sub, in which all the top comments are people who've obviously never got all the way through a research paper in their lives all trashing Apple and claiming their researchers don't understand AI or "reasoning". It's a weird cult.

Fucking obviously. Until Data's positronic brains becomes reality, AI is not actual intelligence.

AI is not A I. I should make that a tshirt.

I think it's important to note (i'm not an llm I know that phrase triggers you to assume I am) that they haven't proven this as an inherent architectural issue, which I think would be the next step to the assertion.

do we know that they don't and are incapable of reasoning, or do we just know that for x problems they jump to memorized solutions, is it possible to create an arrangement of weights that can genuinely reason, even if the current models don't? That's the big question that needs answered. It's still possible that we just haven't properly incentivized reason over memorization during training.

if someone can objectively answer "no" to that, the bubble collapses.

No shit. This isn't new.

No way!

Statistical Language models don't reason?

But OpenAI, robots taking over!

Most humans don't reason. They just parrot shit too. The design is very human.