this post was submitted on 08 Jun 2025
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[–] FourWaveforms@lemm.ee 2 points 6 days ago* (last edited 6 days ago)

WTF does the author think reasoning is

[–] Nanook@lemm.ee 229 points 1 week ago (55 children)

lol is this news? I mean we call it AI, but it’s just LLM and variants it doesn’t think.

[–] MNByChoice@midwest.social 77 points 1 week ago (1 children)

The "Apple" part. CEOs only care what companies say.

[–] kadup@lemmy.world 51 points 1 week ago (5 children)

Apple is significantly behind and arrived late to the whole AI hype, so of course it's in their absolute best interest to keep showing how LLMs aren't special or amazingly revolutionary.

They're not wrong, but the motivation is also pretty clear.

[–] homesweethomeMrL@lemmy.world 29 points 1 week ago

“Late to the hype” is actually a good thing. Gen AI is a scam wrapped in idiocy wrapped in a joke. That Apple is slow to ape the idiocy of microsoft is just fine.

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[–] Clent@lemmy.dbzer0.com 19 points 1 week ago (3 children)

Proving it matters. Science is constantly proving any other thing that people believe is obvious because people have an uncanning ability to believe things that are false. Some people will believe things long after science has proven them false.

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[–] SoftestSapphic@lemmy.world 97 points 1 week ago (5 children)

Wow it's almost like the computer scientists were saying this from the start but were shouted over by marketing teams.

[–] zbk@lemmy.ca 22 points 1 week ago

This! Capitalism is going to be the end of us all. OpenAI has gotten away with IP Theft, disinformation regarding AI and maybe even murder of their whistle blower.

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[–] minoscopede@lemmy.world 67 points 1 week ago* (last edited 1 week ago) (22 children)

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.

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[–] mavu@discuss.tchncs.de 58 points 1 week ago

No way!

Statistical Language models don't reason?

But OpenAI, robots taking over!

[–] billwashere@lemmy.world 49 points 1 week ago (13 children)

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.

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[–] Jhex@lemmy.world 49 points 1 week ago (1 children)

this is so Apple, claiming to invent or discover something "first" 3 years later than the rest of the market

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[–] sev@nullterra.org 49 points 1 week ago (35 children)

Just fancy Markov chains with the ability to link bigger and bigger token sets. It can only ever kick off processing as a response and can never initiate any line of reasoning. This, along with the fact that its working set of data can never be updated moment-to-moment, means that it would be a physical impossibility for any LLM to achieve any real "reasoning" processes.

[–] kescusay@lemmy.world 18 points 1 week ago (3 children)

I can envision a system where an LLM becomes one part of a reasoning AI, acting as a kind of fuzzy "dataset" that a proper neural network incorporates and reasons with, and the LLM could be kept real-time updated (sort of) with MCP servers that incorporate anything new it learns.

But I don't think we're anywhere near there yet.

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[–] brsrklf@jlai.lu 47 points 1 week ago (2 children)

You know, despite not really believing LLM "intelligence" works anywhere like real intelligence, I kind of thought maybe being good at recognizing patterns was a way to emulate it to a point...

But that study seems to prove they're still not even good at that. At first I was wondering how hard the puzzles must have been, and then there's a bit about LLM finishing 100 move towers of Hanoï (on which they were trained) and failing 4 move river crossings. Logically, those problems are very similar... Also, failing to apply a step-by-step solution they were given.

[–] auraithx@lemmy.dbzer0.com 39 points 1 week ago

This paper doesn’t prove that LLMs aren’t good at pattern recognition, it demonstrates the limits of what pattern recognition alone can achieve, especially for compositional, symbolic reasoning.

[–] technocrit@lemmy.dbzer0.com 16 points 1 week ago* (last edited 1 week ago)

Computers are awesome at "recognizing patterns" as long as the pattern is a statistical average of some possibly worthless data set. And it really helps if the computer is setup to ahead of time to recognize pre-determined patterns.

[–] Mniot@programming.dev 42 points 1 week ago

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.

[–] reksas@sopuli.xyz 37 points 1 week ago (4 children)

does ANY model reason at all?

[–] 4am@lemm.ee 34 points 1 week ago (3 children)

No, and to make that work using the current structures we use for creating AI models we’d probably need all the collective computing power on earth at once.

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[–] bjoern_tantau@swg-empire.de 36 points 1 week ago* (last edited 1 week ago)
[–] technocrit@lemmy.dbzer0.com 29 points 1 week ago* (last edited 1 week ago) (3 children)

Peak pseudo-science. The burden of evidence is on the grifters who claim "reason". But neither side has any objective definition of what "reason" means. It's pseudo-science against pseudo-science in a fierce battle.

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[–] skisnow@lemmy.ca 26 points 1 week ago (1 children)

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.

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[–] vala@lemmy.world 25 points 1 week ago
[–] SplashJackson@lemmy.ca 24 points 1 week ago (1 children)
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[–] technocrit@lemmy.dbzer0.com 23 points 1 week ago* (last edited 1 week ago) (5 children)

Why would they "prove" something that's completely obvious?

The burden of proof is on the grifters who have overwhelmingly been making false claims and distorting language for decades.

[–] TheRealKuni@midwest.social 33 points 1 week ago (2 children)

Why would they "prove" something that's completely obvious?

I don’t want to be critical, but I think if you step back a bit and look and what you’re saying, you’re asking why we would bother to experiment and prove what we think we know.

That’s a perfectly normal and reasonable scientific pursuit. Yes, in a rational society the burden of proof would be on the grifters, but that’s never how it actually works. It’s always the doctors disproving the cure-all, not the snake oil salesmen failing to prove their own prove their own product.

There is value in this research, even if it fits what you already believe on the subject. I would think you would be thrilled to have your hypothesis confirmed.

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[–] yeahiknow3@lemmings.world 23 points 1 week ago* (last edited 1 week ago) (1 children)

They’re just using the terminology that’s widespread in the field. In a sense, the paper’s purpose is to prove that this terminology is unsuitable.

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[–] tauonite@lemmy.world 16 points 1 week ago

That's called science

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[–] GaMEChld@lemmy.world 20 points 1 week ago (8 children)

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

[–] elbarto777@lemmy.world 26 points 1 week ago (5 children)

LLMs deal with tokens. Essentially, predicting a series of bytes.

Humans do much, much, much, much, much, much, much more than that.

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[–] FreakinSteve@lemmy.world 20 points 1 week ago (4 children)

NOOOOOOOOO

SHIIIIIIIIIITT

SHEEERRRLOOOOOOCK

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[–] RampantParanoia2365@lemmy.world 18 points 1 week ago* (last edited 1 week ago) (2 children)

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.

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[–] flandish@lemmy.world 18 points 1 week ago

stochastic parrots. all of them. just upgraded “soundex” models.

this should be no surprise, of course!

[–] sp3ctr4l@lemmy.dbzer0.com 17 points 1 week ago* (last edited 1 week ago) (2 children)

This has been known for years, this is the default assumption of how these models work.

You would have to prove that some kind of actual reasoning capacity has arisen as... some kind of emergent complexity phenomenon.... not the other way around.

Corpos have just marketed/gaslit us/themselves so hard that they apparently forgot this.

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