Apple just proved AI "reasoning" models like Claude, DeepSeek-R1, and o3-mini don't actually reason at all. They just memorize patterns really well.
-
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 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.
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.
-
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.
-
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
-
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.
-
LOOK MAA I AM ON FRONT PAGE
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.
-
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"wrote on last edited by [email protected]That indicates that this particular model does not follow instructions, not that it is architecturally fundamentally incapable.
-
LOOK MAA I AM ON FRONT PAGE
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".
-
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".
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.
-
Lots of us who has done some time in search and relevancy early on knew ML was always largely breathless overhyped marketing. It was endless buzzwords and misframing from the start, but it raised our salaries. Anything that exec doesnt understand is profitable and worth doing.
wrote on last edited by [email protected]Machine learning based pattern matching is indeed very useful and profitable when applied correctly. Identify (with confidence levels) features in data that would otherwise take an extremely well trained person. And even then it's just for the cursory search that takes the longest before presenting the highest confidence candidate results to a person for evaluation. Think: scanning medical data for indicators of cancer, reading live data from machines to predict failure, etc.
And what we call "AI" right now is just a much much more user friendly version of pattern matching - the primary feature of LLMs is that they natively interact with plain language prompts.
-
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.
-
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.
-
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?
-
LLMs deal with tokens. Essentially, predicting a series of bytes.
Humans do much, much, much, much, much, much, much more than that.
No. They don't. We just call them proteins.
-
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.
-
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".
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.
-
Lots of us who has done some time in search and relevancy early on knew ML was always largely breathless overhyped marketing. It was endless buzzwords and misframing from the start, but it raised our salaries. Anything that exec doesnt understand is profitable and worth doing.
Ragebait?
I'm in robotics and find plenty of use for ML methods. Think of image classifiers, how do you want to approach that without oversimplified problem settings?
Or even in control or coordination problems, which can sometimes become NP-hard. Even though not optimal, ML methods are quite solid in learning patterns of highly dimensional NP hard problem settings, often outperforming hand-crafted conventional suboptimal solvers in computation effort vs solution quality analysis, especially outperforming (asymptotically) optimal solvers time-wise, even though not with optimal solutions (but "good enough" nevertheless). (Ok to be fair suboptimal solvers do that as well, but since ML methods can outperform these, I see it as an attractive middle-ground.) -
Wow it's almost like the computer scientists were saying this from the start but were shouted over by marketing teams.
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.
-
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.
-
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.
wrote on last edited by [email protected]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.
