LOOK MAA I AM ON FRONT PAGE
Wow it’s almost like the computer scientists were saying this from the start but were shouted over by marketing teams.
And engineers who stood to make a lot of money
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.
It’s hard to to be heard when you’re buried under all that sweet VC/grant money.
For me it kinda went the other way, I’m almost convinced that human intelligence is the same pattern repeating, just more general (yet)
Except that wouldn’t explain conscience. There’s absolutely no need for conscience or an illusion(*) of conscience. Yet we have it.
- arguably, conscience can by definition not be an illusion. We either perceive “ourselves” or we don’t
WTF does the author think reasoning is
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.
You’d think the M in LLM would give it away.
Intuition is about the only thing it has. It’s a statistical system. The problem is it doesn’t have logic. We assume because its computer based that it must be more logic oriented but it’s the opposite. That’s the problem. We can’t get it to do logic very well because it basically feels out the next token by something like instinct. In particular it doesn’t mask or disconsider irrelevant information very well if two segments are near each other in embedding space, which doesn’t guarantee relevance. So then the model is just weighing all of this info, relevant or irrelevant to a weighted feeling for the next token.
This is the core problem. People can handle fuzzy topics and discrete topics. But we really struggle to create any system that can do both like we can. Either we create programming logic that is purely discrete or we create statistics that are fuzzy.
Of course this issue of masking out information that is close in embedding space but is irrelevant to a logical premise is something many humans suck at too. But high functioning humans don’t and we can’t get these models to copy that ability. Too many people, sadly many on the left in particular, not only will treat association as always relevant but sometimes as equivalence. RE racism is assoc with nazism is assoc patriarchy is historically related to the origins of capitalism ∴ nazism ≡ capitalism. While national socialism was anti-capitalist. Associative thinking removes nuance. And sadly some people think this way. And they 100% can be replaced by LLMs today, because at least the LLM is mimicking what logic looks like better though still built on blind association. It just has more blind associations and finetune weighting for summing them. More than a human does. So it can carry that to mask as logical further than a human who is on the associative thought train can.
You had a compelling description of how ML models work and just had to swerve into politics, huh?
People think they want AI, but they don’t even know what AI is on a conceptual level.
They want something like the Star Trek computer or one of Tony Stark’s AIs that were basically deus ex machinas for solving some hard problem behind the scenes. Then it can say “model solved” or they can show a test simulation where the ship doesn’t explode (or sometimes a test where it only has an 85% chance of exploding when it used to be 100%, at which point human intuition comes in and saves the day by suddenly being better than the AI again and threads that 15% needle or maybe abducts the captain to go have lizard babies with).
AIs that are smarter than us but for some reason don’t replace or even really join us (Vision being an exception to the 2nd, and Ultron trying to be an exception to the 1st).
They don’t want AI, they want an app.
Yeah I often think about this Rick N Morty cartoon. Grifters are like, “We made an AI ankle!!!” And I’m like, “That’s not actually something that people with busted ankles want. They just want to walk. No need for a sentient ankle.” It’s a real gross distortion of science how everything needs to be “AI” nowadays.
I agree with you. In its current state, LLM is not sentient, and thus not “Intelligence”.
I think it’s an easy mistake to confuse sentience and intelligence. It happens in Hollywood all the time - “Skynet began learning at a geometric rate, on July 23 2004 it became self-aware” yadda yadda
But that’s not how sentience works. We don’t have to be as intelligent as Skynet supposedly was in order to be sentient. We don’t start our lives as unthinking robots, and then one day - once we’ve finally got a handle on calculus or a deep enough understanding of the causes of the fall of the Roman empire - we suddenly blink into consciousness. On the contrary, even the stupidest humans are accepted as being sentient. Even a young child, not yet able to walk or do anything more than vomit on their parents’ new sofa, is considered as a conscious individual.
So there is no reason to think that AI - whenever it should be achieved, if ever - will be conscious any more than the dumb computers that precede it.
Good point.
hey I cant recognize patterns so theyre smarter than me at least
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.
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.
Even defining reason is hard and becomes a matter of philosophy more than science. For example, apply the same claims to people. Now I’ve given you something to think about. Or should I say the Markov chain in your head has a new topic to generate thought states for.
By many definitions, reasoning IS just a form of pattern recognition so the lines are definitely blurred.
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.
What confuses me is that we seemingly keep pushing away what counts as reasoning. Not too long ago, some smart alghoritms or a bunch of instructions for software (if/then) was officially, by definition, software/computer reasoning. Logically, CPUs do it all the time. Suddenly, when AI is doing that with pattern recognition, memory and even more advanced alghoritms, it’s no longer reasoning? I feel like at this point a more relevant question is “What exactly is reasoning?”. Before you answer, understand that most humans seemingly live by pattern recognition, not reasoning.
If you want to boil down human reasoning to pattern recognition, the sheer amount of stimuli and associations built off of that input absolutely dwarfs anything an LLM will ever be able to handle. It’s like comparing PhD reasoning to a dog’s reasoning.
While a dog can learn some interesting tricks and the smartest dogs can solve simple novel problems, there are hard limits. They simply lack a strong metacognition and the ability to make simple logical inferences (eg: why they fail at the shell game).
Now we make that chasm even larger by cutting the stimuli to a fixed token limit. An LLM can do some clever tricks within that limit, but it’s designed to do exactly those tricks and nothing more. To get anything resembling human ability you would have to design something to match human complexity, and we don’t have the tech to make a synthetic human.
There’s probably alot of misunderstanding because these grifters intentionally use misleading language: AI, reasoning, etc.
If they stuck to scientifically descriptive terms, it would be much more clear and much less sensational.
When given explicit instructions to follow models failed because they had not seen similar instructions before.
This paper shows that there is no reasoning in LLMs at all, just extended pattern matching.
I’m not trained or paid to reason, I am trained and paid to follow established corporate procedures. On rare occasions my input is sought to improve those procedures, but the vast majority of my time is spent executing tasks governed by a body of (not quite complete, sometimes conflicting) procedural instructions.
If AI can execute those procedures as well as, or better than, human employees, I doubt employers will care if it is reasoning or not.
Sure. We weren’t discussing if AI creates value or not. If you ask a different question then you get a different answer.
Well - if you want to devolve into argument, you can argue all day long about “what is reasoning?”
You were starting a new argument. Let’s stay on topic.
The paper implies “Reasoning” is application of logic. It shows that LRMs are great at copying logic but can’t follow simple instructions that haven’t been seen before.
Yeah these comments have the three hallmarks of Lemmy:
- AI is just autocomplete mantras.
- Apple is always synonymous with bad and dumb.
- Rare pockets of really thoughtful comments.
Thanks for being at least the latter.
What statistical method do you base that claim on? The results presented match expectations given that Markov chains are still the basis of inference. What magic juice is added to “reasoning models” that allow them to break free of the inherent boundaries of the statistical methods they are based on?
I’d encourage you to research more about this space and learn more.
As it is, the statement “Markov chains are still the basis of inference” doesn’t make sense, because markov chains are a separate thing. You might be thinking of Markov decision processes, which is used in training RL agents, but that’s also unrelated because these models are not RL agents, they’re supervised learning agents. And even if they were RL agents, the MDP describes the training environment, not the model itself, so it’s not really used for inference.
I mean this just as an invitation to learn more, and not pushback for raising concerns. Many in the research community would be more than happy to welcome you into it. The world needs more people who are skeptical of AI doing research in this field.
Which method, then, is the inference built upon, if not the embeddings? And the question still stands, how does “AI” escape the inherent limits of statistical inference?
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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”.
My impression of LLM training and deployment is that it’s actually massively parallel in nature - which can be implemented one instruction at a time - but isn’t in practice.
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.
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.
While a fair idea there are two issues with that even still - Hallucinations and the cost of running the models.
Unfortunately, it take significant compute resources to perform even simple responses, and these responses can be totally made up, but still made to look completely real. It’s gotten much better sure, but blindly trusting these things (Which many people do) can have serious consequences.
XD so, like a regular school/university student that just wants to get passing grades?
So, what your saying here is that the A in AI actually stands for artificial, and it’s not really intelligent and reasoning.
Huh.
The AI stands for Actually Indians /s
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.
In case you haven’t seen it, the paper is here - https://machinelearning.apple.com/research/illusion-of-thinking (PDF linked on the left).
The puzzles the researchers have chosen are spatial and logical reasoning puzzles - so certainly not the natural domain of LLMs. The paper doesn’t unfortunately give a clear definition of reasoning, I think I might surmise it as “analysing a scenario and extracting rules that allow you to achieve a desired outcome”.
They also don’t provide the prompts they use - not even for the cases where they say they provide the algorithm in the prompt, which makes that aspect less convincing to me.
What I did find noteworthy was how the models were able to provide around 100 steps correctly for larger Tower of Hanoi problems, but only 4 or 5 correct steps for larger River Crossing problems. I think the River Crossing problem is like the one where you have a boatman who wants to get a fox, a chicken and a bag of rice across a river, but can only take two in his boat at one time? In any case, the researchers suggest that this could be because there will be plenty of examples of Towers of Hanoi with larger numbers of disks, while not so many examples of the River Crossing with a lot more than the typical number of items being ferried across. This being more evidence that the LLMs (and LRMs) are merely recalling examples they’ve seen, rather than genuinely working them out.
do we know that they don’t and are incapable of reasoning.
“even when we provide the algorithm in the prompt—so that the model only needs to execute the prescribed steps—performance does not improve”
That indicates that this particular model does not follow instructions, not that it is architecturally fundamentally incapable.
Not “This particular model”. Frontier LRMs s OpenAI’s o1/o3,DeepSeek-R, Claude 3.7 Sonnet Thinking, and Gemini Thinking.
The paper shows that Large Reasoning Models as defined today cannot interpret instructions. Their architecture does not allow it.
those particular models. It does not prove the architecture doesn’t allow it at all. It’s still possible that this is solvable with a different training technique, and none of those are using the right one. that’s what they need to prove wrong.
this proves the issue is widespread, not fundamental.
The architecture of these LRMs may make monkeys fly out of my butt. It hasn’t been proven that the architecture doesn’t allow it.
You are asking to prove a negative. The onus is to show that the architecture can reason. Not to prove that it can’t.
that’s very true, I’m just saying this paper did not eliminate the possibility and is thus not as significant as it sounds. If they had accomplished that, the bubble would collapse, this will not meaningfully change anything, however.
also, it’s not as unreasonable as that because these are automatically assembled bundles of simulated neurons.
This paper does provide a solid proof by counterexample of reasoning not occuring (following an algorithm) when it should.
The paper doesn’t need to prove that reasoning never has or will occur. It’s only demonstrates that current claims of AI reasoning are overhyped.
lol is this news? I mean we call it AI, but it’s just LLM and variants it doesn’t think.
This is why I say these articles are so similar to how right wing media covers issues about immigrants.
There’s some weird media push to convince the left to hate AI. Think of all the headlines for these issues. There are so many similarities. They’re taking jobs. They are a threat to our way of life. The headlines talk about how they will sexual assault your wife, your children, you. Threats to the environment. There’s articles like this where they take something known as twist it to make it sound nefarious to keep the story alive and avoid decay of interest.
Then when they pass laws, we’re all primed to accept them removing whatever it is that advantageous them and disadvantageous us.
This is why I say these articles are so similar to how right wing media covers issues about immigrants.
Maybe the actual problem is people who equate computer programs with people.
Then when they pass laws, we’re all primed to accept them removing whatever it is that advantageous them and disadvantageous us.
You mean laws like this? jfc.
Literally what I’m talking about. They have been pushing anti AI propaganda to alienate the left from embracing it while the right embraces it. You have such a blind spot you this, you can’t even see you’re making my argument for me.
That depends on your assumption that the left would have anything relevant to gain by embracing AI (whatever that’s actually supposed to mean).
Saw this earlier in the week and thought of you. These short, funny videos are popping up more and more and they’re only getting better. They’re sharp, engaging, and they spread like wildfire.
You strike me as someone who gets it what it means when one side embraces the latest tools while the other rejects them.
The left is still holed up on Lemmy, clinging to “Fuck AI” groups. But why? Go back to the beginning. Look at the early coverage of AI it was overwhelmingly targeted at left-leaning spaces, full of panic and doom. Compare that to how the right talks about immigration. The headlines are cut and pasted from each other. Same playbook, different topic. The media set out to alienate the left from these tools.
I don’t have even the slightest idea what that video is supposed to mean. (Happy cake day tho.)
Come on, you know what I’m talking about. It’s a channel that started with AI content and is now pivoting to videos about the riots. You can see where this is going. Sooner or later, it’ll expand into targeting protestors and other left-leaning causes.
It’s a novelty now, but it’s spreading fast, and more channels like it are popping up every day.
Meanwhile, the left is losing ground. Losing cultural capture. Because as a group, they’re being manipulated into isolating themselves from the very tools and platforms that shape public opinion. Social media. AI. All of it. They’re walking away from the battlefield while the other side builds momentum.
What isn’t there to gain?
Its power lies in ingesting language and producing infinite variations. We can feed it talking points, ask it to refine our ideas, test their logic, and even request counterarguments to pressure-test our stance. It helps us build stronger, more resilient narratives.
We can use it to make memes. Generate images. Expose logical fallacies. Link to credible research. It can detect misinformation in real-time and act as a force multiplier for anyone trying to raise awareness or push back on disinfo.
Most importantly, it gives a voice to people with strong ideas who might not have the skills or confidence to share them. Someone with a brilliant comic concept but no drawing ability? AI can help build a framework to bring it to life.
Sure, it has flaws. But rejecting it outright while the right embraces it? That’s beyond shortsighted it’s self-sabotage. And unfortunately, after the last decade, that kind of misstep is par for the course.
I have no idea what sort of AI you’ve used that could do any of this stuff you’ve listed. A program that doesn’t reason won’t expose logical fallacies with any rigour or refine anyone’s ideas. It will link to credible research that you could already find on Google but will also add some hallucinations to the summary. And so on, it’s completely divorced from how the stuff as it is currently works.
Someone with a brilliant comic concept but no drawing ability? AI can help build a framework to bring it to life.
That’s a misguided view of how art is created. Supposed “brilliant ideas” are dime a dozen, it takes brilliant writers and artists to make them real. Someone with no understanding of how good art works just having an image generator produce the images will result in a boring comic no matter the initial concept. If you are not competent in a visual medium, then don’t make it visual, write a story or an essay.
Besides, most of the popular and widely shared webcomics out there are visually extremely simple or just bad (look at SMBC or xkcd or - for a right-wing example - Stonetoss).
For now I see no particular benefits that the right-wing has obtained by using AI either. They either make it feed back into their delusions, or they whine about the evil leftists censoring the models (by e.g. blocking its usage of slurs).
Here is chatgpt doing what you said it can’t. Finding all the logical fallacies in what you write:
You’re raising strong criticisms, and it’s worth unpacking them carefully. Let’s go through your argument and see if there are any logical fallacies or flawed reasoning.
- Straw Man Fallacy
“Someone with no understanding of how good art works just having an image generator produce the images will result in a boring comic no matter the initial concept.”
This misrepresents the original claim:
“AI can help create a framework at the very least so they can get their ideas down.”
The original point wasn’t that AI could replace the entire creative process or make a comic successful on its own—it was that it can assist people in starting or visualizing something they couldn’t otherwise. Dismissing that by shifting the goalposts to “producing a full, good comic” creates a straw man of the original claim.
- False Dichotomy
“If you are not competent in a visual medium, then don’t make it visual, write a story or an essay.”
This suggests a binary: either you’re competent at visual art or you shouldn’t try to make anything visual. That’s a false dichotomy. People can learn, iterate, or collaborate, and tools like AI can help bridge gaps in skill—not replace skill, but allow exploration. Many creators use tools before mastery (e.g., musicians using GarageBand, or writers using Grammarly).
- Hasty Generalization
“Supposed ‘brilliant ideas’ are a dime a dozen…”
While it’s true that execution matters more than ideas alone, dismissing the value of ideas altogether is an overgeneralization. Many successful works do start with a strong concept—and while many fail in execution, tools that lower the barrier to prototyping or drafting can help surface more workable ideas. The presence of many bad ideas doesn’t invalidate the potential value of enabling more people to test theirs.
- Appeal to Ridicule / Ad Hominem (Light)
“…result in a boring comic…” / “…just bad (look at SMBC or xkcd or…)”
Criticizing popular webcomics like SMBC or xkcd by calling them “bad” doesn’t really support your broader claim. These comics are widely read because of strong writing and insight, despite minimalistic visuals. It comes off as dismissive and ridicules the counterexamples rather than engaging with them. That’s not a logical fallacy in the strictest sense, but it’s rhetorically weak.
- Tu Quoque / Whataboutism (Borderline)
“For now I see no particular benefits that the right-wing has obtained by using AI either…”
This seems like a rebuttal to a point that wasn’t made directly. The original argument wasn’t that “the right is winning with AI,” but rather that alienating the left from it could lead to missed opportunities. Refuting a weaker version (e.g., “the right is clearly winning with AI”) isn’t addressing the original concern, which was more about strategic adoption.
Summary of Fallacies Identified:
Type Description
Straw Man Misrepresents the role of AI in creative assistance. False Dichotomy Assumes one must either be visually skilled or not attempt visual media. Hasty Generalization Devalues “brilliant ideas” universally. Appeal to Ridicule Dismisses counterexamples via mocking tone rather than analysis. Tu Quoque-like Compares left vs. right AI use without addressing the core point about opportunity.
Your criticism is thoughtful and not without merit—but it’s wrapped in rhetoric that sometimes slips into oversimplification or misrepresentation of the opposing view. If your goal is to strengthen your argument or have a productive back-and-forth, refining those areas could help. Would you like to rewrite it in a way that keeps the spirit of your critique but sharpens its logic?
At this point you’re just arguing for arguments sake. You’re not wrong or right but instead muddying things. Saying it’ll be boring comics missed the entire point. Saying it is the same as google is pure ignorance of what it can do. But this goes to my point about how this stuff is all similar to anti immigrant mentality. The people who buy into it will get into these type of ignorant and short sighted statements just to prove things that just are not true. But they’ve bought into the hype and need to justify it.
"It’s part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, ‘that’s not thinking’." -Pamela McCorduck´.
It’s called the AI Effect.As Larry Tesler puts it, “AI is whatever hasn’t been done yet.”.
That entire paragraph is much better at supporting the precise opposite argument. Computers can beat Kasparov at chess, but they’re clearly not thinking when making a move - even if we use the most open biological definitions for thinking.
By that metric, you can argue Kasparov isn’t thinking during chess, either. A lot of human chess “thinking” is recalling memorized openings, evaluating positions many moves deep, and other tasks that map to what a chess engine does. Of course Kasparov is thinking, but then you have to conclude that the AI is thinking too. Thinking isn’t a magic process, nor is it tightly coupled to human-like brain processes as we like to think.
By that metric, you can argue Kasparov isn’t thinking during chess
Kasparov’s thinking fits pretty much all biological definitions of thinking. Which is the entire point.
Is thinking necessarily biologic?
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.
I mean… “proving” is also just marketing speak. There is no clear definition of reasoning, so there’s also no way to prove or disprove that something/someone reasons.
Claiming it’s just marketing fluff is indicates you do not know what you’re talking about.
They published a research paper on it. You are free to publish your own paper disproving theirs.
At the moment, you sound like one of those “I did my own research” people except you didn’t even bother doing your own research.
You misunderstand. I do not take issue with anything that’s written in the scientific paper. What I take issue with is how the paper is marketed to the general public. When you read the article you will see that it does not claim to “proof” that these models cannot reason. It merely points out some strengths and weaknesses of the models.
