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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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.
wrote on last edited by [email protected]It does need to do that to meaningfully change anything, however.
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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?
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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
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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
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It does need to do that to meaningfully change anything, however.
Other way around. The claimed meaningful change (reasoning) has not occurred.
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LOOK MAA I AM ON FRONT PAGE
hey I cant recognize patterns so theyre smarter than me at least
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Other way around. The claimed meaningful change (reasoning) has not occurred.
Meaningful change is not happening because of this paper, either, I don't know why you're playing semantic games with me though.
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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.
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Meaningful change is not happening because of this paper, either, I don't know why you're playing semantic games with me though.
I don't know why you're playing semantic games
I'm trying to highlight the goal of this paper.
This is a knock them down paper by Apple justifying (to their shareholders) their non investment in LLMs. It is not a build them up paper trying for meaningful change and to create a better AI.
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I don't know why you're playing semantic games
I'm trying to highlight the goal of this paper.
This is a knock them down paper by Apple justifying (to their shareholders) their non investment in LLMs. It is not a build them up paper trying for meaningful change and to create a better AI.
That's not the only way to make meaningful change, getting people to give up on llms would also be meaningful change. This does very little for anyone who isn't apple.
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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.
I don't mean it to extol LLM's but rather to denigrate humans. How many of us are self imprisoned in echo chambers so we can have our feelings validated to avoid the uncomfortable feeling of thinking critically and perhaps changing viewpoints?
Humans have the ability to actually think, unlike LLM's. But it's frightening how far we'll go to make sure we don't.
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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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LOOK MAA I AM ON FRONT PAGE
wrote on last edited by [email protected]WTF does the author think reasoning is
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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
How do you define consciousness?
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How do you define consciousness?
It's the thing that the only person who can know for sure you have it is you yourself. If you have to ask, I might have to assume you could be a biological machine.
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It's the thing that the only person who can know for sure you have it is you yourself. If you have to ask, I might have to assume you could be a biological machine.
Is that useful for completing tasks?
