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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I think because it's language.
There's a famous quote from Charles Babbage when he presented his difference engine (gear based calculator) and someone asking "if you put in the wrong figures, will the correct ones be output" and Babbage not understanding how someone can so thoroughly misunderstand that the machine is, just a machine.
People are people, the main thing that's changed since the Cuneiform copper customer complaint is our materials science and networking ability. Most things that people interact with every day, most people just assume work like it appears to on the surface.
And nothing other than a person can do math problems or talk back to you. So people assume that means intelligence.
"if you put in the wrong figures, will the correct ones be output"
To be fair, an 1840 “computer” might be able to tell there was something wrong with the figures and ask about it or even correct them herself.
Babbage was being a bit obtuse there; people weren't familiar with computing machines yet. Computer was a job, and computers were expected to be fairly intelligent.
In fact I'd say that if anything this question shows that the questioner understood enough about the new machine to realise it was not the same as they understood a computer to be, and lacked many of their abilities, and was just looking for Babbage to confirm their suspicions.
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Most humans don't reason. They just parrot shit too. The design is very human.
Thata why ceo love them. When your job is 90% spewing bs a machine that does that is impressive
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You either an llm, or don't know how your brain works.
LLMs don't know how how they work
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Yeah, well there are a ton of people literally falling into psychosis, led by LLMs. So it’s unfortunately not that many people that already knew it.
Dude they made chat gpt a little more boit licky and now many people are convinced they are literal messiahs. All it took for them was a chat bot and a few hours of talk.
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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.
It’s an expensive carbon spewing parrot.
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"if you put in the wrong figures, will the correct ones be output"
To be fair, an 1840 “computer” might be able to tell there was something wrong with the figures and ask about it or even correct them herself.
Babbage was being a bit obtuse there; people weren't familiar with computing machines yet. Computer was a job, and computers were expected to be fairly intelligent.
In fact I'd say that if anything this question shows that the questioner understood enough about the new machine to realise it was not the same as they understood a computer to be, and lacked many of their abilities, and was just looking for Babbage to confirm their suspicions.
"Computer" meaning a mechanical/electro-mechanical/electrical machine wasn't used until around after WWII.
Babbag's difference/analytical engines weren't confusing because people called them a computer, they didn't.
"On two occasions I have been asked, 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question."
- Charles Babbage
If you give any computer, human or machine, random numbers, it will not give you "correct answers".
It's possible Babbage lacked the social skills to detect sarcasm. We also have several high profile cases of people just trusting LLMs to file legal briefs and official government 'studies' because the LLM "said it was real".
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LOOK MAA I AM ON FRONT PAGE
wrote on last edited by [email protected]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.
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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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LOOK MAA I AM ON FRONT PAGE
NOOOOOOOOO
SHIIIIIIIIIITT
SHEEERRRLOOOOOOCK
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Most humans don't reason. They just parrot shit too. The design is very human.
I hate this analogy. As a throwaway whimsical quip it'd be fine, but it's specious enough that I keep seeing it used earnestly by people who think that LLMs are in any way sentient or conscious, so it's lowered my tolerance for it as a topic even if you did intend it flippantly.
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NOOOOOOOOO
SHIIIIIIIIIITT
SHEEERRRLOOOOOOCK
Extept for Siri, right? Lol
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Extept for Siri, right? Lol
Apple Intelligence
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It’s an expensive carbon spewing parrot.
It's a very resource intensive autocomplete
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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.
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.
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LOOK MAA I AM ON FRONT PAGE
wrote on last edited by [email protected]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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LOOK MAA I AM ON FRONT PAGE
So, what your saying here is that the A in AI actually stands for artificial, and it's not really intelligent and reasoning.
Huh.
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"Computer" meaning a mechanical/electro-mechanical/electrical machine wasn't used until around after WWII.
Babbag's difference/analytical engines weren't confusing because people called them a computer, they didn't.
"On two occasions I have been asked, 'Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?' I am not able rightly to apprehend the kind of confusion of ideas that could provoke such a question."
- Charles Babbage
If you give any computer, human or machine, random numbers, it will not give you "correct answers".
It's possible Babbage lacked the social skills to detect sarcasm. We also have several high profile cases of people just trusting LLMs to file legal briefs and official government 'studies' because the LLM "said it was real".
What they mean is that before Turing, "computer" was literally a person's job description. You hand a professional a stack of calculations with some typos, part of the job is correcting those out. Newfangled machine comes along with the same name as the job, among the first thing people are gonna ask about is where it fall short.
Like, if I made a machine called "assistant", it'd be natural for people to point out and ask about all the things a person can do that a machine just never could.
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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.
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.
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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.
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.
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Intellegence has a very clear definition.
It's requires the ability to acquire knowledge, understand knowledge and use knowledge.
No one has been able to create an system that can understand knowledge, therefor me none of it is artificial intelligence. Each generation is merely more and more complex knowledge models. Useful in many ways but never intelligent.
Wouldn't the algorithm that creates these models in the first place fit the bill? Given that it takes a bunch of text data, and manages to organize this in such a fashion that the resulting model can combine knowledge from pieces of text, I would argue so.
What is understanding knowledge anyways? Wouldn't humans not fit the bill either, given that for most of our knowledge we do not know why it is the way it is, or even had rules that were - in hindsight - incorrect?
If a model is more capable of solving a problem than an average human being, isn't it, in its own way, some form of intelligent? And, to take things to the utter extreme, wouldn't evolution itself be intelligent, given that it causes intelligent behavior to emerge, for example, viruses adapting to external threats? What about an (iterative) optimization algorithm that finds solutions that no human would be able to find?
Intellegence has a very clear definition.
I would disagree, it is probably one of the most hard to define things out there, which has changed greatly with time, and is core to the study of philosophy. Every time a being or thing fits a definition of intelligent, the definition often altered to exclude, as has been done many times.
