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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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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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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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
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.
