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.
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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.
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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.
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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.
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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?
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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.
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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.
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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.
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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.
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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.
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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.
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No. They don't. We just call them proteins.
You are either vastly overestimating the Language part of an LLM or simplifying human physiology back to the Greek's Four Humours theory.
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No. They don't. We just call them proteins.
"They".
What are you?
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That’s absolutely what it is. It’s a pattern on here. Any acknowledgment of humans being animals or less than superior gets hit with pushback.
I didn't say we aren't animals or that we don't follow physics rules.
But what you're saying is the equivalent of "everything that goes up will eventually go down - that's how physics works and you don't see that, you're in denial!!!11!!!1"
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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.
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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.
wrote on last edited by [email protected]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 us 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...
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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?"
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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.
I agree with you. In its current state, LLM is not sentient, and thus not "Intelligence".
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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.
And that's pretty damn useful, but obnoxious to have expectations wildly set incorrectly.
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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.
Is "model" not defined as architecture+weights? Those models certainly don't share the same architecture. I might just be confused about your point though
