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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That entire paragraph is much better at supporting the precise opposite argument. Computers can beat Kasparov at chess, but they're clearly not thinking when making a move - even if we use the most open biological definitions for thinking.
By that metric, you can argue Kasparov isn't thinking during chess, either. A lot of human chess "thinking" is recalling memorized openings, evaluating positions many moves deep, and other tasks that map to what a chess engine does. Of course Kasparov is thinking, but then you have to conclude that the AI is thinking too. Thinking isn't a magic process, nor is it tightly coupled to human-like brain processes as we like to think.
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Apple is significantly behind and arrived late to the whole AI hype, so of course it's in their absolute best interest to keep showing how LLMs aren't special or amazingly revolutionary.
They're not wrong, but the motivation is also pretty clear.
Apple always arrives late to any new tech, doesn't mean they haven't been working on it behind the scenes for just as long though...
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Yeah I've always said the the flaw in Turing's Imitation Game concept is that if an AI was indistinguishable from a human it wouldn't prove it's intelligent. Because humans are dumb as shit. Dumb enough to force one of the smartest people in the world take a ton of drugs which eventually killed him simply because he was gay.
I think that person had to choose between the drugs or hard core prison of the 1950s England where being a bit odd was enough to guarantee an incredibly difficult time as they say in England, I would've chosen the drugs as well hoping they would fix me, too bad without testosterone you're going to be suicidal and depressed, I'd rather choose to keep my hair than to be horny all the time
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LOOK MAA I AM ON FRONT PAGE
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.
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That was a roundabout way of admitting you have no idea what logic is or how LLMs work. Logic works with propositions regardless of their literal meaning, LLMs operate with textual tokens irrespective of their formal logical relations. The chatbot doesn't actually do the logical operations behind the scenes, it only produces the text output that looks like the operations were done (because it was trained on a lot of existing text that reflects logical operations in its content).
This is why I said I wasn't sure how AI works behind the scenes. But I do know that logic isn't difficult. Just to not fuck around between us. I have a CS background. Only saying this because I think you may have it as well and we can save some time.
It makes sense to me that logic is something AI can parse easily. Logic in my mind is very easy if it can tokenize some text. Wouldn't the difficulty be if the AI has the right context.
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TBH idk how people can convince themselves otherwise.
They don’t convince themselves. They’re convinced by the multi billion dollar corporations pouring unholy amounts of money into not only the development of AI, but its marketing. Marketing designed to not only convince them that AI is something it’s not, but also that that anyone who says otherwise (like you) are just luddites who are going to be “left behind”.
LLMs are also very good at convincing their users that they know what they are saying.
It's what they're really selected for. Looking accurate sells more than being accurate.
I wouldn't be surprised if many of the people selling LLMs as AI have drunk their own kool-aid (of course most just care about the line going up, but still).
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Right now the hype from most is finding issues with chatgpt
publicity
especially : promotional publicity of an extravagant or contrived kind
You're abusing the meaning of "hype" in order to make the two sides appear the same, because you do understand that "hype" really describes the pro-AI discourse much better.
It did find the fallacies based on what it was asked to do.
It didn't. Put the text of your comment back into GPT and tell it to argue why the fallacies are misidentified.
You act like this is fire and forget.
But you did fire and forget it. I don't even think you read the output yourself.
First I wanted to be honest with the output and not modify it.
Or maybe you were just lazy?
Personally I'm starting to find these copy-pasted AI responses to be insulting. It has the "let me Google that for you" sort of smugness around it. I can put in the text in ChatGPT myself and get the same shitty output, you know. If you can't be bothered to improve it, then there's absolutely no point in pasting it.
Given what this output gave me, I can easily keep working this to get better and better arguments.
That doesn't sound terribly efficient. Polishing a turd, as they say. These great successes of AI are never actually visible or demonstrated, they're always put off - the tech isn't quite there yet, but it's just around the corner, just you wait, just one more round of asking the AI to elaborate, just one more round of polishing the turd, just a bit more faith on the unbelievers' part...
I just feel like you can’t honestly tell me that within 10 seconds having that summary is not beneficial.
Oh sure I can tell you that, assuming that your argumentative goals are remotely honest and you're not just posting stupid AI-generated criticism to waste my time. You didn't even notice one banal way in which AI misinterpreted my comment (I didn't say SMBC is bad), and you'd probably just accept that misreading in your own supposed rewrite of the text. Misleading summaries that you have to spend additional time and effort double checking for these subtle or not so subtle failures are NOT beneficial.
Ok let's give a test here. Let's start with understand logic. Give me a paragraph and let's see if it can find any logical fallacies. You can provide the paragraph. Only constraint is that the context has to exist within the paragraph.
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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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While both Markov models and LLMs forget information outside their window, that’s where the similarity ends. A Markov model relies on fixed transition probabilities and treats the past as a chain of discrete states. An LLM evaluates every token in relation to every other using learned, high-dimensional attention patterns that shift dynamically based on meaning, position, and structure.
Changing one word in the input can shift the model’s output dramatically by altering how attention layers interpret relationships across the entire sequence. It’s a fundamentally richer computation that captures syntax, semantics, and even task intent, which a Markov chain cannot model regardless of how much context it sees.
an llm also works on fixed transition probabilities. All the training is done during the generation of the weights, which are the compressed state transition table. After that, it's just a regular old markov chain. I don't know why you seem so fixated on getting different output if you provide different input (as I said, each token generated is a separate independent invocation of the llm with a different input). That is true of most computer programs.
It's just an implementation detail. The markov chains we are used to has a very short context, due to combinatorial explosion when generating the state transition table. With llms, we can use a much much longer context. Put that context in, it runs through the completely immutable model, and out comes a probability distribution. Any calculations done during the calculation of this probability distribution is then discarded, the chosen token added to the context, and the program is run again with zero prior knowledge of any reasoning about the token it just generated. It's a seperate execution with absolutely nothing shared between them, so there can't be any "adapting" going on
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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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LLMs (at least in their current form) are proper neural networks.
Well, technically, yes. You're right. But they're a specific, narrow type of neural network, while I was thinking of the broader class and more traditional applications, like data analysis. I should have been more specific.
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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
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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LOOK MAA I AM ON FRONT PAGE
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
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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
