hey I cant recognize patterns so theyre smarter than me at least
Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all.
Submitted 9 months ago by Allah@lemm.ee to technology@lemmy.world
Comments
burgerpocalyse@lemmy.world 9 months ago
technocrit@lemmy.dbzer0.com 9 months ago
Peak pseudo-science. The burden of proof is on the grifters to prove reason. There’s absolutely no reason to disprove something that has no evidence anyway. Apple has no idea what “reason” means. It’s pseudo-science against pseudo-science in a fierce battle.
x0x7@lemmy.world 9 months ago
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 is buzzing.
I_Has_A_Hat@lemmy.world 9 months ago
By many definitions, reasoning IS a form of pattern recognition so the lines are definitely blurred.
SoftestSapphic@lemmy.world 9 months ago
Wow it’s almost like the computer scientists were saying this from the start but were shouted over by marketing teams.
aidan@lemmy.world 9 months ago
And engineers who stood to make a lot of money
BlushedPotatoPlayers@sopuli.xyz 9 months ago
For me it kinda went the other way, I’m almost convinced that human intelligence is the same pattern repeating, just more general (yet)
raspberriesareyummy@lemmy.world 9 months ago
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
technocrit@lemmy.dbzer0.com 9 months ago
It’s hard to to be heard when you’re buried under all that sweet VC/grant money.
zbk@lemmy.ca 9 months 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.
NostraDavid@programming.dev 9 months ago
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”.
technocrit@lemmy.dbzer0.com 9 months ago
Cars are horses. How do you feel about statement?
fishy@lemmy.today 9 months ago
The guy selling the car doesn’t tell you it runs like a horse, the guy selling you AI is telling you it has reasoning skills. AI absolutely has utility, the guys making it are saying it’s utility is nearly limitless because Tesla has demonstrated there’s no actual penalty for lying to investors.
Brutticus@midwest.social 9 months ago
Then use a different word. “AI” and “reasoning” makes people think of Skynet, which is what the weird tech bros want the lay person to think of. LLMs do not “think”, but that’s not to say I might not be persuaded of their utility. But thats not the way they are being marketed.
billwashere@lemmy.world 9 months ago
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.
StereoCode@lemmy.world 9 months ago
You’d think the M in LLM would give it away.
x0x7@lemmy.world 9 months ago
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 it 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.
Slaxis@discuss.tchncs.de 9 months ago
You had a compelling description of how ML models work and just had to swerve into politics, huh?
NotASharkInAManSuit@lemmy.world 9 months ago
People think they want AI, but they don’t even know what AI is on a conceptual level.
Buddahriffic@lemmy.world 9 months ago
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).
technocrit@lemmy.dbzer0.com 9 months ago
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 sentience.”
jj4211@lemmy.world 9 months ago
And that’s pretty damn useful, but obnoxious to have expectations wildly set incorrectly.
SaturdayMorning@lemmy.ca 9 months ago
I agree with you. In its current state, LLM is not sentient, and thus not “Intelligence”.
MouldyCat@feddit.uk 9 months ago
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.
MangoCats@feddit.it 9 months ago
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.
cactopuses@lemm.ee 9 months ago
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.
MangoCats@feddit.it 9 months ago
Hallucinations and the cost of running the models.
So, inaccurate information in books is nothing new. Agreed that the rate of hallucinations needs to decline, a lot, but there has always been a need for a veracity filter - just because it comes from “a book” or “the TV” has never been an indication of absolute truth, even though many people stop there and assume it is. In other words: blind trust is not a new problem.
The cost of running the models is an interesting one - how does it compare with publication on paper to ship globally to store in environmentally controlled libraries which require individuals to physically travel to/from the libraries to access the information? What’s the price of the resulting increased ignorance of the general population due to the high cost of information access?
What good is a bunch of knowledge stuck behind a search engine when people don’t know how to access it, or access it efficiently?
Granted, search engines already take up 95% (IMO) of the way from paper libraries to what AI is almost succeeding in being today, but ease of access of information has tremendous value - and developing ways to easily access the information available on the internet is a very valuable endeavor.
Personally, I feel more emphasis should be put on establishing the veracity of the information before we go making all the garbage easier to find.
I also worry that “easy access” to automated interpretation services is going to lead to a bunch of information encoded in languages that most people don’t know because they’re dependent on machines to do the translation for them. As an example: shiny new computer language comes out but software developer is too lazy to learn it, developer uses AI to write code in the new language instead…
Mniot@programming.dev 9 months 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.
melsaskca@lemmy.ca 9 months ago
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”.
MangoCats@feddit.it 9 months ago
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.
Harbinger01173430@lemmy.world 9 months ago
XD so, like a regular school/university student that just wants to get passing grades?
Xatolos@reddthat.com 9 months ago
[deleted]coolmojo@lemmy.world 9 months ago
The AI stands for Actually Indians /s
minoscopede@lemmy.world 9 months ago
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 more likely that the training is not right, specifically for so-called “reasoning models” that iterate on their answer before replying.
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. If so, that opens the door for experimentation on more rigorous training processes that could lead to more capable models that actually do “reason”.
Allah@lemm.ee 9 months ago
Cognitive scientist Douglas Hofstadter (1979) showed reasoning emerges from pattern recognition and analogy-making - abilities that modern AI demonstrably possesses. The question isn’t if AI can reason, but how its reasoning differs from ours.
technocrit@lemmy.dbzer0.com 9 months ago
There’s probably alot of misunderstanding because these grifters intentionally use misleading language: AI, reason, etc.
Knock_Knock_Lemmy_In@lemmy.world 9 months ago
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.
MangoCats@feddit.it 9 months ago
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.
REDACTED@infosec.pub 9 months ago
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.
technocrit@lemmy.dbzer0.com 9 months ago
Sure, these grifters are shady AF about their wacky definition of “reason”… But that’s just a continuation of the entire “AI” grift.
stickly@lemmy.world 9 months ago
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.
MangoCats@feddit.it 9 months ago
I think as we approach the uncanny valley of machine intelligence, it’s no longer a cute cartoon but a menacing creepy not-quite imitation of ourselves.
Tobberone@lemm.ee 9 months ago
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?
minoscopede@lemmy.world 9 months ago
I think you might not be using the vocabulary correctly. 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’d encourage you to research more about this space and learn more. We need more people who are skeptical of AI doing research in this field, and many of us in the research community would be more than happy to welcome you into it.
theherk@lemmy.world 9 months ago
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.
Zacryon@feddit.org 9 months ago
Some AI researchers found it obvious as well, in terms of they’ve suspected it and had some indications. But it’s good to see more data on this to affirm this assessment.
jj4211@lemmy.world 9 months ago
Particularly to counter some more baseless marketing assertions about the nature of the technology.
kreskin@lemmy.world 9 months ago
Lots of who has done some time in search and relevancy early on knew this was always largely breathless overhyped marketing.
FreakinSteve@lemmy.world 9 months ago
NOOOOOOOOO
SHIIIIIIIIIITT
SHEEERRRLOOOOOOCK
technocrit@lemmy.dbzer0.com 9 months ago
The funny thing about this “AI” griftosphere is how grifters will make some outlandish claim and then different grifters will “disprove” it. Plenty of grant/VC money for everybody.
jj4211@lemmy.world 9 months ago
Without being explicit with well researched material, then the marketing presentation gets to stand largely unopposed.
So this is good even if most experts in the field consider it an obvious result.
800XL@lemmy.world 9 months ago
Extept for Siri, right? Lol
Threeme2189@lemmy.world 9 months ago
Apple Intelligence
skisnow@lemmy.ca 9 months ago
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.
technocrit@lemmy.dbzer0.com 9 months ago
communist@lemmy.frozeninferno.xyz 9 months ago
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.
MouldyCat@feddit.uk 9 months ago
In case you haven’t seen it, the paper is here - machinelearning.apple.com/…/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.
Knock_Knock_Lemmy_In@lemmy.world 9 months ago
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”
communist@lemmy.frozeninferno.xyz 9 months ago
That indicates that it does not follow instructions, not that it is architecturally fundamentally incapable.
RampantParanoia2365@lemmy.world 9 months ago
Fucking obviously. Until Data’s positronic brains becomes reality, AI is not actual intelligence.
JDPoZ@lemmy.world 9 months ago
It’s an expensive carbon spewing parrot.
Threeme2189@lemmy.world 9 months ago
It’s a very resource intensive autocomplete
Auli@lemmy.ca 9 months ago
No shit. This isn’t new.
intensely_human@lemm.ee 9 months ago
Fair, but the same is true of me. I don’t actually “reason”; I just have a set of algorithms memorized by which I propose a pattern that seems like it might match the situation, then a different pattern by which I break the situation down into smaller components and then apply patterns to those components. I keep the process up for a while. If I find a “nasty logic error” pattern match at some point in the process, I “know” I’ve found a “flaw in the argument” or “bug in the design”.
But there’s no from-first-principles method by which I developed all these patterns; it’s just things that have survived the test of time when other patterns have failed me.
I don’t think people are underestimating the power of LLMs to think; I just think people are overestimating the power of humans to do anything other than language prediction and sensory pattern prediction.
conicalscientist@lemmy.world 9 months ago
This whole era of AI has certainly pushed the brink to existential crisis territory. I think some are even frightened to entertain the prospect that we may not be all that much better than meat machines who on a basic level do pattern matching drawing from the sum total of individual life experience (aka the dataset).
Higher reasoning is taught to humans. We have the capability. That’s why we spend the first quarter of our lives in education. Sometimes not all of us are able.
I’m sure it would certainly make waves if researchers did studies based on whether dumber humans are any different than AI.
GaMEChld@lemmy.world 9 months ago
Most humans don’t reason. They just parrot shit too. The design is very human.
BlaueHeiligenBlume@feddit.org 9 months ago
Of course, that is obvious to all having basic knowledge of neural networks, no?
ZILtoid1991@lemmy.world 9 months ago
Thank you Captain Obvious! Only those who think LLMs are like “little people in the computer” didn’t knew this already.
mavu@discuss.tchncs.de 9 months ago
No way!
Statistical Language models don’t reason?
But OpenAI, robots taking over!
crystalmerchant@lemmy.world 9 months ago
I mean… Is that not reasoning, I guess? It’s what my brain does-- recognizes patterns and makes split second decisions.
WorldsDumbestMan@lemmy.today 9 months ago
It has so much data, it might as well be reasoning. As it helped me with my problem.
surph_ninja@lemmy.world 9 months ago
You assume humans do the opposite? We literally institutionalize humans who not follow set patterns.
Grizzlyboy@lemmy.zip 9 months ago
What a dumb title. I proved it by asking a series of questions. It’s not AI, stop calling it AI, it’s a dumb af language model. Can you get a ton of help from it, as a tool? Yes! Can it reason? NO! It never could and for the foreseeable future, it will not.
It’s phenomenal at patterns, much much better than us meat peeps. That’s why they’re accurate as hell when it comes to analyzing medical scans.
vala@lemmy.world 9 months ago
No shit
LonstedBrowryBased@lemm.ee 9 months ago
Yah of course they do they’re computers
bjoern_tantau@swg-empire.de 9 months ago
FourWaveforms@lemm.ee 9 months ago
WTF do they think reasoning is