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.
Technology
245
Beiträge
130
Kommentatoren
0
Aufrufe
-
You've hit the nail on the head.
Personally, I wish that there's more progress in our understanding of human intelligence.
Their argument is that we don't understand human intelligence so we should call computers intelligent.
That's not hitting any nail on the head.
-
Agreed. We don't seem to have a very cohesive idea of what human consciousness is or how it works.
... And so we should call machines "intelligent"? That's not how science works.
-
I'm going to write a program to play tic-tac-toe. If y'all don't think it's "AI", then you're just haters. Nothing will ever be good enough for y'all. You want scientific evidence of intelligence?!?! I can't even define intelligence so take that! \s
Seriously tho. This person is arguing that a checkers program is "AI". It kinda demonstrates the loooong history of this grift.
It is. And has always been. "Artificial Intelligence" doesn't mean a feeling thinking robot person (that would fall under AGI or artificial conciousness), it's a vast field of research in computer science with many, many things under it.
-
Performance collapses because luck runs out. Bigger destruction of the planet won't fix that.
Brother you better hope it does because even if emissions dropped to 0 tonight the planet wouldnt stop warming and it wouldn't stop what's coming for us.
-
I can envision a system where an LLM becomes one part of a reasoning AI, acting as a kind of fuzzy "dataset" that a proper neural network incorporates and reasons with, and the LLM could be kept real-time updated (sort of) with MCP servers that incorporate anything new it learns.
But I don't think we're anywhere near there yet.
The only reason we're not there yet is memory limitations.
Eventually some company will come out with AI hardware that lets you link up a petabyte of ultra fast memory to chips that contain a million parallel matrix math processors. Then we'll have an entirely new problem: AI that trains itself incorrectly too quickly.
Just you watch: The next big breakthrough in AI tech will come around 2032-2035 (when the hardware is available) and everyone will be bitching that "chain reasoning" (or whatever the term turns out to be) isn't as smart as everyone thinks it is.
-
did i do it here? also that's where i live, if i can't talk about womens struggle then i appologize
I don't think that person cares about women or anything else. They just said that they don't even want to hear about it.
-
This post did not contain any content.
this is so Apple, claiming to invent or discover something "first" 3 years later than the rest of the market
-
...... So you're saying there's a chance?
10^36 flops to be exact
-
Who is "you"?
Just because some dummies supposedly think that NPCs are "AI", that doesn't make it so. I don't consider checkers to be a litmus test for "intelligence".
"You" applies to anyone that doesnt understand what AI means. It's a portmanteau word for a lot of things.
Npcs ARE AI. AI doesnt mean "human level intelligence" and never did. Read the wiki if you need help understanding.
-
Unlike Markov models, modern LLMs use transformers that attend to full contexts, enabling them to simulate structured, multi-step reasoning (albeit imperfectly). While they don’t initiate reasoning like humans, they can generate and refine internal chains of thought when prompted, and emerging frameworks (like ReAct or Toolformer) allow them to update working memory via external tools. Reasoning is limited, but not physically impossible, it’s evolving beyond simple pattern-matching toward more dynamic and compositional processing.
I'm not convinced that humans don't reason in a similar fashion. When I'm asked to produce pointless bullshit at work my brain puts in a similar level of reasoning to an LLM.
Think about "normal" programming: An experienced developer (that's self-trained on dozens of enterprise code bases) doesn't have to think much at all about 90% of what they're coding. It's all bog standard bullshit so they end up copying and pasting from previous work, Stack Overflow, etc because it's nothing special.
The remaining 10% is "the hard stuff". They have to read documentation, search the Internet, and then—after all that effort to avoid having to think—they sigh and start actually start thinking in order to program the thing they need.
LLMs go through similar motions behind the scenes! Probably because they were created by software developers but they still fail at that last 90%: The stuff that requires actual thinking.
Eventually someone is going to figure out how to auto-generate LoRAs based on test cases combined with trial and error that then get used by the AI model to improve itself and that is when people are going to be like, "Oh shit! Maybe AGI really is imminent!" But again, they'll be wrong.
AGI won't happen until AI models get good at retraining themselves with something better than basic reinforcement learning. In order for that to happen you need the working memory of the model to be nearly as big as the hardware that was used to train it. That, and loads and loads of spare matrix math processors ready to go for handing that retraining.
-
This post did not contain any content.
Just like me
-
Humans apply judgment, because they have emotion. LLMs do not possess emotion. Mimicking emotion without ever actually having the capability of experiencing it is sociopathy. An LLM would at best apply patterns like a sociopath.
That just means they'd be great CEOs!
According to Wall Street.
-
why is it assumed that this isn’t what human reasoning consists of?
Because science doesn't work work like that. Nobody should assume wild hypotheses without any evidence whatsoever.
Isn’t all our reasoning ultimately a form of pattern memorization? I sure feel like it is.
You should get a job in "AI". smh.
Sorry, I can see why my original post was confusing, but I think you've misunderstood me. I'm not claiming that I know the way humans reason. In fact you and I are on total agreement that it is unscientific to assume hypotheses without evidence. This is exactly what I am saying is the mistake in the statement "AI doesn't actually reason, it just follows patterns". That is unscientific if we don't know whether or "actually reasoning" consists of following patterns, or something else. As far as I know, the jury is out on the fundamental nature of how human reasoning works. It's my personal, subjective feeling that human reasoning works by following patterns. But I'm not saying "AI does actually reason like humans because it follows patterns like we do". Again, I see how what I said could have come off that way. What I mean more precisely is:
It's not clear whether AI's pattern-following techniques are the same as human reasoning, because we aren't clear on how human reasoning works. My intuition tells me that humans doing pattern following seems equally as valid of an initial guess as humans not doing pattern following, so shouldn't we have studies to back up the direction we lean in one way or the other?
I think you and I are in agreement, we're upholding the same principle but in different directions.
-
It is. And has always been. "Artificial Intelligence" doesn't mean a feeling thinking robot person (that would fall under AGI or artificial conciousness), it's a vast field of research in computer science with many, many things under it.
ITT: people who obviously did not study computer science or AI at at least an undergraduate level.
Y'all are too patient. I can't be bothered to spend the time to give people free lessons.
-
This has been known for years, this is the default assumption of how these models work.
You would have to prove that some kind of actual reasoning capacity has arisen as... some kind of emergent complexity phenomenon.... not the other way around.
Corpos have just marketed/gaslit us/themselves so hard that they apparently forgot this.
Define, "reasoning". For decades software developers have been writing code with conditionals. That's "reasoning."
LLMs are "reasoning"... They're just not doing human-like reasoning.
-
Employers who are foaming at the mouth at the thought of replacing their workers with cheap AI:
🫢
Can’t really replace. At best, this tech will make employees more productive at the cost of the rainforests.
-
In fact, simple computer programs do a great job of solving these puzzles...
Yes, this shit is very basic. Not at all "intelligent."
But reasoning about it is intelligent, and the point of this study is to determine the extent to which these models are reasoning or not. Which again, has nothing to do with emotions. And furthermore, my initial question about whether or not pattern following should automatically be disqualified as intelligence, as the person summarizing this study (and notably not the study itself) claims, is the real question here.
-
But it still manages to fuck it up.
I've been experimenting with using Claude's Sonnet model in Copilot in agent mode for my job, and one of the things that's become abundantly clear is that it has certain types of behavior that are heavily represented in the model, so it assumes you want that behavior even if you explicitly tell it you don't.
Say you're working in a yarn workspaces project, and you instruct Copilot to build and test a new dashboard using an instruction file. You'll need to include explicit and repeated reminders all throughout the file to use yarn, not NPM, because even though yarn is very popular today, there are so many older examples of using NPM in its model that it's just going to assume that's what you actually want - thereby fucking up your codebase.
I've also had lots of cases where I tell it I don't want it to edit any code, just to analyze and explain something that's there and how to update it... and then I have to stop it from editing code anyway, because halfway through it forgot that I didn't want edits, just explanations.
To be fair, the world of JavaScript is such a clusterfuck... Can you really blame the LLM for needing constant reminders about the specifics of your project?
When a programming language has five hundred bazillion absolutely terrible ways of accomplishing a given thing—and endless absolutely awful code examples on the Internet to "learn from"—you're just asking for trouble. Not just from trying to get an LLM to produce what you want but also trying to get humans to do it.
This is why LLMs are so fucking good at writing rust and Python: There's only so many ways to do a thing and the larger community pretty much always uses the same solutions.
JavaScript? How can it even keep up? You're using yarn today but in a year you'll probably like, "fuuuuck this code is garbage... I need to convert this all to [new thing]."
-
No, it shows how certain people misunderstand the meaning of the word.
You have called npcs in video games "AI" for a decade, yet you were never implying they were somehow intelligent. The whole argument is strangely inconsistent.
"Artificial" has several meanings.
One is:
not being, showing, or resembling sincere or spontaneous behavior : fake
AI in video games literally means "fake intelligence"
-
This is why I say these articles are so similar to how right wing media covers issues about immigrants.
There's some weird media push to convince the left to hate AI. Think of all the headlines for these issues. There are so many similarities. They're taking jobs. They are a threat to our way of life. The headlines talk about how they will sexual assault your wife, your children, you. Threats to the environment. There's articles like this where they take something known as twist it to make it sound nefarious to keep the story alive and avoid decay of interest.
Then when they pass laws, we're all primed to accept them removing whatever it is that advantageous them and disadvantageous us.
Unlike fear-mongering from the right about immigrants, current iterations of AI development:
- literally consume the environment (they are using electricity and water)
- are taking jobs and siphoning money from the economy towards centralized corporate revenue streams that don't pay a fair share of taxes
- I don't know of headlines claiming they will sexually assault you, but many headlines note that they can be used as part of sophisticated catfishing scams, which they are
All of these things aren't scare tactics. They're often overblown and exaggerated for clicks, but the fundamental nature of the technology and corporate implementation of it indisputable.
Open-source AI can change the world for the better. Corporate-controlled AI in some limited cases will improve the world, but without reasonable regulations they will severely harm it first.
