LOOK MAA I AM ON FRONT PAGE
lol is this news? I mean we call it AI, but it’s just LLM and variants it doesn’t think.
The “Apple” part. CEOs only care what companies say.
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.
“Late to the hype” is actually a good thing. Gen AI is a scam wrapped in idiocy wrapped in a joke. That Apple is slow to ape the idiocy of microsoft is just fine.
They need to convince investors that this delay wasn’t due to incompetence. The problem will only be somewhat effective as long as there isn’t an innovation that makes AI more effective.
If that happens, Apple shareholders will, at best, ask the company to increase investment in that area or, at worst, to restructure the company, which could also mean a change in CEO.
Maybe they are so far behind because they jumped on the same train but then failed at achieving what they wanted based on the claims. And then they started digging around.
Yes, Apple haters can’t admit nor understand it but Apple doesn’t do pseudo-tech.
They may do silly things, they may love their 100% mark up but it’s all real technology.
The AI pushers or today are akin to the pushers of paranormal phenomenon from a century ago. These pushers want us to believe, need us to believe it so they can get us addicted and extract value from our very existence.
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…
Proving it matters. Science is constantly proving any other thing that people believe is obvious because people have an uncanning ability to believe things that are false. Some people will believe things long after science has proven them false.
I mean… “proving” is also just marketing speak. There is no clear definition of reasoning, so there’s also no way to prove or disprove that something/someone reasons.
Claiming it’s just marketing fluff is indicates you do not know what you’re talking about.
They published a research paper on it. You are free to publish your own paper disproving theirs.
At the moment, you sound like one of those “I did my own research” people except you didn’t even bother doing your own research.
You misunderstand. I do not take issue with anything that’s written in the scientific paper. What I take issue with is how the paper is marketed to the general public. When you read the article you will see that it does not claim to “proof” that these models cannot reason. It merely points out some strengths and weaknesses of the models.
"It’s part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something—play good checkers, solve simple but relatively informal problems—there was a chorus of critics to say, ‘that’s not thinking’." -Pamela McCorduck´.
It’s called the AI Effect.As Larry Tesler puts it, “AI is whatever hasn’t been done yet.”.
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.
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.
Strangely inconsistent + smoke & mirrors = profit!
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.
Dog has a very clear definition, so when you call a sausage in a bun a “Hot Dog”, you are actually a fool.
Smart has a very clear definition, so no, you do not have a “Smart Phone” in your pocket.
Also, that is not the definition of intelligence. But the crux of the issue is that you are making up a definition for AI that suits your needs.
Misconstruing how language works isn’t an argument for what an existing and established word means.
I’m sure that argument made you feel super clever but it’s nonsense.
I sourced by definition from authoritative sources. The fact that you didn’t even bother to verify that or provide an alternative authoritative definition tells me all I need to know about the value in further discussion with you.
"Artificial intelligence refers to computer systems that can perform complex tasks normally done by human-reasoning, decision making, creating, etc.
There is no single, simple definition of artificial intelligence because AI tools are capable of a wide range of tasks and outputs, but NASA follows the definition of AI found within EO 13960, which references Section 238(g) of the National Defense Authorization Act of 2019.
- Any artificial system that performs tasks under varying and unpredictable circumstances without significant human oversight, or that can learn from experience and improve performance when exposed to data sets.
- An artificial system developed in computer software, physical hardware, or other context that solves tasks requiring human-like perception, cognition, planning, learning, communication, or physical action.
- An artificial system designed to think or act like a human, including cognitive architectures and neural networks.
- A set of techniques, including machine learning that is designed to approximate a cognitive task.
- An artificial system designed to act rationally, including an intelligent software agent or embodied robot that achieves goals using perception, planning, reasoning, learning, communicating, decision-making, and acting."
This is from NASA (emphasis mine). https://www.nasa.gov/what-is-artificial-intelligence/
The problem is that you are reading the word intelligence and thinking it means the system itself needs to be intelligent, when it only needs to be doing things that we would normally attribute to intelligence. Computer vision is AI, but a software that detects a car inside a picture and draws a box around it isn’t intelligent. It is still considered AI and has been considered AI for the past three decades.
Now show me your blog post that told you that AI isnt AI because it isn’t thinking.
deleted by creator
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.
deleted by creator
Is thinking necessarily biologic?
Yesterday I asked an LLM “how much energy is stored in a grand piano?” It responded with saying there is no energy stored in a grad piano because it doesn’t have a battery.
Any reasoning human would have understood that question to be referring to the tension in the strings.
Another example is asking “does lime cause kidney stones?”. It didn’t assume I mean lime the mineral and went with lime the citrus fruit instead.
Once again a reasoning human would assume the question is about the mineral.
Ask these questions again in a slightly different way and you might get a correct answer, but it won’t be because the LLM was thinking.
Honestly, i thought about the chemical energy in the materials constructing the piano and what energy burning it would release.
The tension of the strings would actually be a pretty miniscule amount of energy too, since there’s very little stretch to a piano wire, the force might be high, but the potential energy/work done to tension the wire is low (done by hand with a wrench).
Compared to burning a piece of wood, which would release orders of magnitude more energy.
I’m not sure how you arrived at lime the mineral being a more likely question than lime the fruit. I’d expect someone asking about kidney stones would also be asking about foods that are commonly consumed.
This kind of just goes to show there’s multiple ways something can be interpreted. Maybe a smart human would ask for clarification, but for sure AIs today will just happily spit out the first answer that comes up. LLMs are extremely “good” at making up answers to leading questions, even if it’s completely false.
A well trained model should consider both types of lime. Failure is likely down to temperature and other model settings. This is not a measure of intelligence.
Making up answers is kinda their entire purpose. LMMs are fundamentally just a text generation algorithm, they are designed to produce text that looks like it could have been written by a human. Which they are amazing at, especially when you start taking into account how many paragraphs of instructions you can give them, and they tend to rather successfully follow.
The one thing they can’t do is verify if what they are talking about is true as it’s all just slapping words together using probabilities. If they could, they would stop being LLMs and start being AGIs.
But 90% of “reasoning humans” would answer just the same. Your questions are based on some non-trivial knowledge of physics, chemistry and medicine that most people do not possess.
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.
deleted by creator
Because it’s a fear-mongering angle that still sells. AI has been a vehicle for scifi for so long that trying to convince Boomers that of won’t kill us all is the hard part.
I’m a moderate user for code and skeptic of LLM abilities, but 5 years from now when we are leveraging ML models for groundbreaking science and haven’t been nuked by SkyNet, all of this will look quaint and silly.
Wow it’s almost like the computer scientists were saying this from the start but were shouted over by marketing teams.
This! Capitalism is going to be the end of us all. OpenAI has gotten away with IP Theft, disinformation regarding AI and maybe even murder of their whistle blower.
And engineers who stood to make a lot of money
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
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.
Is that useful for completing tasks?
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.
When given explicit instructions to follow models failed because they had not seen similar instructions before.
This paper shows that there is no reasoning in LLMs at all, just extended pattern matching.
I’m not trained or paid to reason, I am trained and paid to follow established corporate procedures. On rare occasions my input is sought to improve those procedures, but the vast majority of my time is spent executing tasks governed by a body of (not quite complete, sometimes conflicting) procedural instructions.
If AI can execute those procedures as well as, or better than, human employees, I doubt employers will care if it is reasoning or not.
Sure. We weren’t discussing if AI creates value or not. If you ask a different question then you get a different answer.
Well - if you want to devolve into argument, you can argue all day long about “what is reasoning?”
You were starting a new argument. Let’s stay on topic.
The paper implies “Reasoning” is application of logic. It shows that LRMs are great at copying logic but can’t follow simple instructions that haven’t been seen before.
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.
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.
Particularly to counter some more baseless marketing assertions about the nature of the technology.
Lots of us who has done some time in search and relevancy early on knew ML was always largely breathless overhyped marketing. It was endless buzzwords and misframing from the start, but it raised our salaries. Anything that exec doesnt understand is profitable and worth doing.
Machine learning based pattern matching is indeed very useful and profitable when applied correctly. Identify (with confidence levels) features in data that would otherwise take an extremely well trained person. And even then it’s just for the cursory search that takes the longest before presenting the highest confidence candidate results to a person for evaluation. Think: scanning medical data for indicators of cancer, reading live data from machines to predict failure, etc.
And what we call “AI” right now is just a much much more user friendly version of pattern matching - the primary feature of LLMs is that they natively interact with plain language prompts.
Ragebait?
I’m in robotics and find plenty of use for ML methods. Think of image classifiers, how do you want to approach that without oversimplified problem settings?
Or even in control or coordination problems, which can sometimes become NP-hard. Even though not optimal, ML methods are quite solid in learning patterns of highly dimensional NP hard problem settings, often outperforming hand-crafted conventional suboptimal solvers in computation effort vs solution quality analysis, especially outperforming (asymptotically) optimal solvers time-wise, even though not with optimal solutions (but “good enough” nevertheless). (Ok to be fair suboptimal solvers do that as well, but since ML methods can outperform these, I see it as an attractive middle-ground.)
What confuses me is that we seemingly keep pushing away what counts as reasoning. Not too long ago, some smart alghoritms or a bunch of instructions for software (if/then) was officially, by definition, software/computer reasoning. Logically, CPUs do it all the time. Suddenly, when AI is doing that with pattern recognition, memory and even more advanced alghoritms, it’s no longer reasoning? I feel like at this point a more relevant question is “What exactly is reasoning?”. Before you answer, understand that most humans seemingly live by pattern recognition, not reasoning.
If you want to boil down human reasoning to pattern recognition, the sheer amount of stimuli and associations built off of that input absolutely dwarfs anything an LLM will ever be able to handle. It’s like comparing PhD reasoning to a dog’s reasoning.
While a dog can learn some interesting tricks and the smartest dogs can solve simple novel problems, there are hard limits. They simply lack a strong metacognition and the ability to make simple logical inferences (eg: why they fail at the shell game).
Now we make that chasm even larger by cutting the stimuli to a fixed token limit. An LLM can do some clever tricks within that limit, but it’s designed to do exactly those tricks and nothing more. To get anything resembling human ability you would have to design something to match human complexity, and we don’t have the tech to make a synthetic human.
I think as we approach the uncanny valley of machine intelligence, it’s no longer a cute cartoon but a menacing creepy not-quite imitation of ourselves.
Cognitive scientist Douglas Hofstadter (1979) showed reasoning emerges from pattern recognition and analogy-making - abilities that modern AI demonstrably possesses. The question isn’t if AI can reason, but how its reasoning differs from ours.
What statistical method do you base that claim on? The results presented match expectations given that Markov chains are still the basis of inference. What magic juice is added to “reasoning models” that allow them to break free of the inherent boundaries of the statistical methods they are based on?
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?
No way!
Statistical Language models don’t reason?
But OpenAI, robots taking over!
Just fancy Markov chains with the ability to link bigger and bigger token sets. It can only ever kick off processing as a response and can never initiate any line of reasoning. This, along with the fact that its working set of data can never be updated moment-to-moment, means that it would be a physical impossibility for any LLM to achieve any real “reasoning” processes.
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.
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.
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.
Reasoning is limited
Most people wouldn’t call zero of something ‘limited’.
The paper doesn’t say LLMs can’t reason, it shows that their reasoning abilities are limited and collapse under increasing complexity or novel structure.
I agree with the author.
If these models were truly “reasoning,” they should get better with more compute and clearer instructions.
The fact that they only work up to a certain point despite increased resources is proof that they are just pattern matching, not reasoning.
Performance eventually collapses due to architectural constraints, this mirrors cognitive overload in humans: reasoning isn’t just about adding compute, it requires mechanisms like abstraction, recursion, and memory. The models’ collapse doesn’t prove “only pattern matching”, it highlights that today’s models simulate reasoning in narrow bands, but lack the structure to scale it reliably. That is a limitation of implementation, not a disproof of emergent reasoning.
previous input goes in. Completely static, prebuilt model processes it and comes up with a probability distribution.
There is no “unlike markov chains”. They are markov chains. Ones with a long context (a markov chain also kakes use of all the context provided to it, so I don’t know what you’re on about there). LLMs are just a (very) lossy compression scheme for the state transition table. Computed once, applied blindly to any context fed in.
LLMs are not Markov chains, even extended ones. A Markov model, by definition, relies on a fixed-order history and treats transitions as independent of deeper structure. LLMs use transformer attention mechanisms that dynamically weigh relationships between all tokens in the input—not just recent ones. This enables global context modeling, hierarchical structure, and even emergent behaviors like in-context learning. Markov models can’t reweight context dynamically or condition on abstract token relationships.
The idea that LLMs are “computed once” and then applied blindly ignores the fact that LLMs adapt their behavior based on input. They don’t change weights during inference, true—but they do adapt responses through soft prompting, chain-of-thought reasoning, or even emulated state machines via tokens alone. That’s a powerful form of contextual plasticity, not blind table lookup.
Calling them “lossy compressors of state transition tables” misses the fact that the “table” they’re compressing is not fixed—it’s context-sensitive and computed in real time using self-attention over high-dimensional embeddings. That’s not how Markov chains work, even with large windows.
their input is the context window. Markov chains also use their whole context window. Llms are a novel implementation that can work with much longer contexts, but as soon as something slides out of its window, it’s forgotten. just like any other markov chain. They don’t adapt. You add their token to the context, slide the oldest one out and then you have a different context, on which you run the same thing again. A normal markov chain will also give you a different outuut if you give it a different context. Their biggest weakness is that they don’t and can’t adapt. You are confusing the encoding of the context with the model itself. Just to see how static the model is, try setting temperature to 0, and giving it the same context. i.e. only try to predict one token with the exact same context each time. As soon as you try to predict a 2nd token, you’ve just changed the input and ran the thing again. It’s not adapting, you asked it something different, so it came up with a different answer
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
Because transformer architecture is not equivalent to a probabilistic lookup. A Markov chain assigns probabilities based on a fixed-order state transition, without regard to deeper structure or token relationships. An LLM processes the full context through many layers of non-linear functions and attention heads, each layer dynamically weighting how each token influences every other token.
Although weights do not change during inference, the behavior of the model is not fixed in the way a Markov chain’s state table is. The same model can respond differently to very similar prompts, not just because the inputs differ, but because the model interprets structure, syntax, and intent in ways that are contextually dependent. That is not just longer context-it is fundamentally more expressive computation.
The process is stateless across calls, yes, but it is not blind. All relevant information lives inside the prompt, and the model uses the attention mechanism to extract meaning from relationships across the sequence. Each new input changes the internal representation, so the output reflects contextual reasoning, not a static response to a matching pattern. Markov chains cannot replicate this kind of behavior no matter how many states they include.
this is so Apple, claiming to invent or discover something “first” 3 years later than the rest of the market
Trust Apple. Everyone else who were in the space first are lying.
When are people going to realize, in its current state , an LLM is not intelligent. It doesn’t reason. It does not have intuition. It’s a word predictor.
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?
People think they want AI, but they don’t even know what AI is on a conceptual level.
They want something like the Star Trek computer or one of Tony Stark’s AIs that were basically deus ex machinas for solving some hard problem behind the scenes. Then it can say “model solved” or they can show a test simulation where the ship doesn’t explode (or sometimes a test where it only has an 85% chance of exploding when it used to be 100%, at which point human intuition comes in and saves the day by suddenly being better than the AI again and threads that 15% needle or maybe abducts the captain to go have lizard babies with).
AIs that are smarter than us but for some reason don’t replace or even really join us (Vision being an exception to the 2nd, and Ultron trying to be an exception to the 1st).
They don’t want AI, they want an app.
I agree with you. In its current state, LLM is not sentient, and thus not “Intelligence”.
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.
You’d think the M in LLM would give it away.
And that’s pretty damn useful, but obnoxious to have expectations wildly set incorrectly.
You know, despite not really believing LLM “intelligence” works anywhere like real intelligence, I kind of thought maybe being good at recognizing patterns was a way to emulate it to a point…
But that study seems to prove they’re still not even good at that. At first I was wondering how hard the puzzles must have been, and then there’s a bit about LLM finishing 100 move towers of Hanoï (on which they were trained) and failing 4 move river crossings. Logically, those problems are very similar… Also, failing to apply a step-by-step solution they were given.
This paper doesn’t prove that LLMs aren’t good at pattern recognition, it demonstrates the limits of what pattern recognition alone can achieve, especially for compositional, symbolic reasoning.
I don’t think the article summarizes the research paper well. The researchers gave the AI models simple-but-large (which they confusingly called “complex”) puzzles. Like Towers of Hanoi but with 25 discs.
The solution to these puzzles is nothing but patterns. You can write code that will solve the Tower puzzle for any size n and the whole program is less than a screen.
The problem the researchers see is that on these long, pattern-based solutions, the models follow a bad path and then just give up long before they hit their limit on tokens. The researchers don’t have an answer for why this is, but they suspect that the reasoning doesn’t scale.
does ANY model reason at all?
No, and to make that work using the current structures we use for creating AI models we’d probably need all the collective computing power on earth at once.
… So you’re saying there’s a chance?
10^36 flops to be exact
That sounds really floppy.
Define reason.
Like humans? Of course not. They lack intent, awareness, and grounded meaning. They don’t “understand” problems, they generate token sequences.
as it is defined in the article
I think I do. Might be an illusion, though.
No shit
Just like me
python code for reversing the linked list.
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.
Most humans don’t reason. They just parrot shit too. The design is very human.
deleted by creator
No. They don’t. We just call them proteins.
You are either vastly overestimating the Language part of an LLM or simplifying human physiology back to the Greek’s Four Humours theory.
No. I’m not. You’re nothing more than a protein based machine on a slow burn. You don’t even have control over your own decisions. This is a proven fact. You’re just an ad hoc justification machine.
How many trillions of neuron firings and chemical reactions are taking place for my machine to produce an output? Where are these taking place and how do these regions interact? What are the rules for storing and reshaping memory in response to stimulus? How many bytes of information would it take to describe and simulate all of these systems together?
The human brain alone has the capacity for about 2.5PB of data. Our sensory systems feed data at a rate of about 109 bits/s. The entire English language, compressed, is about 30MB. I can download and run an LLM with just a few GB. Even the largest context windows are still well under 1GB of data.
Just because two things both find and reproduce patterns does not mean they are equivalent. Saying language and biological organisms both use “bytes” is just about as useful as saying the entire universe is “bytes”; it doesn’t really mean anything.
deleted by creator
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.
Thata why ceo love them. When your job is 90% spewing bs a machine that does that is impressive
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’ve heard something along the lines of, “it’s not when computers can pass the Turing Test, it’s when they start failing it on purpose that’s the real problem.”
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
Yeah we’re so stupid we’ve figured out advanced maths, physics, built incredible skyscrapers and the LHC, we may as individuals be less or more intelligent but humans as a whole are incredibly intelligent
