Großer Sprachfehler | Aktuelle Forschungsergebnisse zeigen, dass Sprache nicht dasselbe ist wie Intelligenz. Die gesamte KI-Blase basiert darauf, sie zu ignorieren
https://www.theverge.com/ai-artificial-intelligence/827820/large-language-models-ai-intelligence-neuroscience-problems
45 Kommentare
LLMs are fancy auto-complete.
Falling in love with ChatGPT is basically like falling in love with the predictive text feature in your cell phone. Who knew T9 had so much game?
Some highlights from this critique:
>The problem is that according to current neuroscience, human thinking is largely independent of human language — and we have little reason to believe ever more sophisticated modeling of language will create a form of intelligence that meets or surpasses our own. Humans use language to communicate the results of our capacity to reason, form abstractions, and make generalizations, or what we might call our intelligence. We use language to think, but that does not make language the same as thought. Understanding this distinction is the key to separating scientific fact from the speculative science fiction of AI-exuberant CEOs.
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>The AI hype machine relentlessly promotes the idea that we’re on the verge of creating something as intelligent as humans, or even “superintelligence” that will dwarf our own cognitive capacities. If we gather tons of data about the world, and combine this with ever more powerful computing power (read: Nvidia chips) to improve our statistical correlations, then presto, we’ll have AGI. Scaling is all we need.
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>But this theory is seriously scientifically flawed. LLMs are simply tools that emulate the communicative function of language, not the separate and distinct cognitive process of thinking and reasoning, no matter how many data centers we build.
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>Take away our ability to speak, and we can still think, reason, form beliefs, fall in love, and move about the world; our range of what we can experience and think about remains vast.
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>But take away language from a large language model, and you are left with literally nothing at all.
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>An AI enthusiast might argue that human-level intelligence doesn’t need to necessarily function in the same way as human cognition. AI models have surpassed human performance in activities like chess using processes that differ from what we do, so perhaps they could become superintelligent through some unique method based on drawing correlations from training data.
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>Maybe! But there’s no obvious reason to think we can get to general intelligence — not improving narrowly defined tasks —through text-based training. After all, humans possess all sorts of knowledge that is not easily encapsulated in linguistic data — and if you doubt this, think about how you know how to ride a bike.
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>In fact, within the AI research community there is growing awareness that LLMs are, in and of themselves, insufficient models of human intelligence. For example, Yann LeCun, a Turing Award winner for his AI research and a prominent skeptic of LLMs, left his role at Meta last week to found an AI startup developing what are dubbed world models: “systems that understand the physical world, have persistent memory, can reason, and can plan complex action sequences.” And recently, a group of prominent AI scientists and “thought leaders” — including Yoshua Bengio (another Turing Award winner), former Google CEO Eric Schmidt, and noted AI skeptic Gary Marcus — coalesced around a working definition of AGI as “AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult” (emphasis added). Rather than treating intelligence as a “monolithic capacity,” they propose instead we embrace a model of both human and artificial cognition that reflects “a complex architecture composed of many distinct abilities.”
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>We can credit Thomas Kuhn and his book The Structure of Scientific Revolutions for our notion of “scientific paradigms,” the basic frameworks for how we understand our world at any given time. He argued these paradigms “shift” not as the result of iterative experimentation, but rather when new questions and ideas emerge that no longer fit within our existing scientific descriptions of the world. Einstein, for example, conceived of relativity before any empirical evidence confirmed it. Building off this notion, the philosopher Richard Rorty contended that it is when scientists and artists become dissatisfied with existing paradigms (or vocabularies, as he called them) that they create new metaphors that give rise to new descriptions of the world — and if these new ideas are useful, they then become our common understanding of what is true. As such, he argued, “common sense is a collection of dead metaphors.”
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>As currently conceived, an AI system that spans multiple cognitive domains could, supposedly, predict and replicate what a generally intelligent human would do or say in response to a given prompt. These predictions will be made based on electronically aggregating and modeling whatever existing data they have been fed. They could even incorporate new paradigms into their models in a way that appears human-like. But they have no apparent reason to become dissatisfied with the data they’re being fed — and by extension, to make great scientific and creative leaps.
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>Instead, the most obvious outcome is nothing more than a common-sense repository. Yes, an AI system might remix and recycle our knowledge in interesting ways. But that’s all it will be able to do. It will be forever trapped in the vocabulary we’ve encoded in our data and trained it upon — a dead-metaphor machine. And actual humans — thinking and reasoning and using language to communicate our thoughts to one another — will remain at the forefront of transforming our understanding of the world.
These are some interesting perspectives to consider when trying to understand the shifting landscapes that many of us are now operating in. Is the current paradigms of LLM-based AIs able to make those cognitive leaps that are the hallmark of revolutionary human thinking? Or is it ever constrained by their training data and therefore will work best when refining existing modes and models?
So far, from this article’s perspective, it’s the latter. There’s nothing fundamentally wrong with that, but like with all tools we need to understand how to use them properly and safely.
LLMs seem to have better language comprehension than most humans online do. Soooo just saying.
It might not be real intelligence but it’s more intelligent.
Massive breakthrough demonstrates once and for all that the sky is blue
There’s a reason why there is a lot of attention shifting towards so called „World Models“
Agreed.If language was the same as intelligence, then women could do math
You didn’t need new research for this, it’s been known for years that llms are not intelligent and cannot „think“.
The whole ai scene just now is predicated on being fooled into thinking you’re smarter than anyone else for getting on the train early.
Q: How many stupid people can talk?
A: Appartently not as many as they would gladly listen to them!
[„The ability to speak does not make you intelligent. Now get out of here.“](https://youtu.be/gLrraUzG24w)
You need to train robots with sensory input feedback so that they understand the meaning of words.
Ai can predict which word comes next and it can give you the definition but it doesn’t actually *understand* what it means because it has never experienced anything.
While the article is right that the mainstream „AI“ models are still LLMs at heart, the frontier models into which all the research is going are not strictly speaking LLMs. You have wacky ideas like „world models,“ etc.
Maybe AGI is possible, maybe it’s not, maybe it’s possible in theory but not in practice with the computing resources and energy we currently have or ever will have. Whichever it is, it doesn’t ride LLMs alone.
> The problem is that according to current neuroscience, human thinking is largely independent of human language
That’s rather misleading, and it conflates several uses of the word „language.“ While it’s true that to think you don’t need a „language“ in the sense of the word that the average layperson means when they say that word (e.g., English or Spanish or some other common spoken or written language), thinking still occurs in the abstract language of ideas, concepts, sensory experience, pictures, etc. Basically, it’s information.
Thinking fundamentally requires some representation of information (in your mind). And when mathematicians and computer scientists talk about „language,“ that’s what they’re talking about. It’s not necessarily a spoken or written language as we know it. In an LLM, the model of language is an ultra-high dimensional space in which vector embeddings represent abstract information opaquely, which encodes information about ideas and concepts and the relationships between them. Thinking still requires that kind of language, the abstract language of information. AI models aren’t just trying to model „language“ as a linguist understands the word, but information.
Also, while we don’t have a good model of consciousness and theory of the mind, we do know that language is very important for intelligence. A spoken or written language isn’t *required* for thought, but language deprivation severely limits the kinds of thoughts you’re able to think, and the depth and complexity of abstract reasoning, the complexity of inner monologue. Babies born deaf or who were otherwise deprived of language exposure often end up cognitively underdeveloped. Without language, we could think in terms of how we feel or what we want, but not the complex abstract reasoning that when sustained and built up across time and built up on itself and on previous works leads to the development of culture, of science and engineering and technology.
The upshot is that if it’s even is possible for AGI of a sort that can „think“ (whatever that means) in a way that leads to generalized and novel reasoning in the areas of the sciences or medicine or technology to exist at all, you would need a good model of language (really a good model of information) to start. It would be a foundational layer.
So the study basically confirms what my granny used to say: “There’s book smarts, and there’s common sense.”
Surely Sam Altman and co aren’t lying to us? They most definitely believe what they’re telling us, surely
Yup. That was the disagreement Jan LeCun had with Meta which led to him leaving the company. Many of the top AI researchers know this and published papers years ago warning LRMs are only one facet of general intelligence. The LLM frenzy is driven by investors, not researchers.
Yup, biased probability machine is not intelligent.
It’s still better than google search so there is a lot of utility there
This should have been obvious when LLMs were incapable of reliably doing basic math, something a computer should be better than anything at doing.
There’s no way airplanes can ever fly because they don’t flap their wings.
Common sense shows this as well.
This whole thing is dumb. By that same logic todays AI is also separate from language, it’s actually parameter weights (same as neurons), these are separate from language for instance there’s separate paramter weights for bat and bat (their semantic meanings).
They also refer to different areas of the brains adapting. I mean those are just different models, in theory there’s nothing stopping the fundamental architecture from being truly multimodal, or having one model feed into another model or even just Mixture of Experts (moe).
Also the who whole learning and reasoning thing, if that were true, we wouldn’t need to go to school. We learn patterns and apply them. We update our statistical model of the world and the relationship between the things in it.
r/noshitsherlock
It is so funny how the dialogue around AI development went from, „we can’t really call it intelligence“ to „let’s just call it intelligence anyways and cash in“
“Cutting edge research” = common sense.
The ability to speak does not make you intelligent.
I mean, anyone with eyes and a functioning brain has been saying this for years already.
It just doesn’t matter. This shit has never been driven by rational thinking. At this point, it’s being driven by something much, much darker. Not even greed, exactly. It’s got to be deeper than greed, because the scheme they’re running only ends in massive loss unless they massively change the rules.
Like they looked at 2008 and said, the unfathomably large bailout was cool and all, but what if we upended the world economic system instead?
Like politicians LLMs can give an eloquent answer that doesn’t demonstrate understanding or reasoning
This article gets it the wrong way around.
LLMs *demonstrate* intelligence, that is really quite inarguable at this point. It’s not necessarily the most coherent or consistent intelligence, but it’s there in some form.
The fact that intelligence is not language should suggest to us the opposite from what the article concludes, that LLMs probably haven’t *only* learned language, they have probably learned intelligence in some other form too. It may not be the most optimal form of intelligence, and it might not even be *that* close to how human intelligence works, but it’s there in some form because it’s able to *approximate* human intelligence beyond simple language (even if it’s flawed.)
>Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it
Thank heavens!
No kidding. God, it took this long to realize it?!? These AI systems are aggregators, not thinkers. They are incredible tools, but they are completely unintelligent.
This criticism perhaps applies to pure LLMs, but I don’t see how it applies to state of the art **multi-modal** Transformers. Multi-modal neural networks use much more than language (text) as inputs/outputs. Pictures, videos, sounds, robot sensors and actions (when embedded in a robot, or RL trained in virtual environment)..
LLMs were just the beginning.
The Lying Machine has a silver tongue? Who’da thunk it?
no shit… we need a research to understand that?
yet, its so sad to see people cant even grasp this simple concept
annoyingly that includes most of my friends and families, i am so tired of trying to explain to everyone around me
Anyone who actually uses AI a lot can tell there is some intelligence in there. Most models even pass IQ tests but the scores are topped at about 130 (for now), so still human level.
Some people really mix up the concept of intelligence and consciousness. The AIs definitely have intelligence, otherwise how do they understand complex concepts and give advice. You can argue that it is just a fantastic linguistic response machine, but humans are more or less like that in our thought process. We often clarify our thoughts by writing and speaking, very similar to LLMs actually.
Consciousness is another level, with automatic agencies of what to do, what you want or hate, how to feel etc. These are not explicitly modelled in AIs (yet) but can be (though very dangerous). The AI models can be incredibly smart, recognizing patterns and giving solutions even better than humans, but currently without its own agency and only as mechanistic tools.
So I think AI is indeed modelling intelligence, but intelligence only means pattern recognition and problem solving. Humans are more than that. But the real risk is, an AI doesn’t have to be conscious to be dangerous. Some misaligned optimisation goals wrongly set by humans is all it takes to cause huge troubles.
It’s very funny to read same arguments every year while seeing LLMs successfully solving “surely impossible for LLM” challenges from previous year.
Wait… Didn’t we already know this? Of course Language ≠ Intelligence, and Intelligence ≠ Consciousness.
Haven’t we known thia from the beginning? LLMs never mimicked thought or intelligence, they just place words in order according to a massive computation of likelihood. If we ever get true AI it won’t look anything like an LLM.
I’ve been saying this for a while. we don’t have true AI. marketing peeps just decided to run with it for money. we have sophisticated chatbots.
ironically, you can post any anti-LLM article to Reddit and get dozens of the same predictable responses (from real people) that all sound like they came from an AI.
Well duh… it’s an info processor, not a brain….
I mean, we need only look at the vast examples of people talking and saying nothing of intelligence to know this.
The need to target average people as consumers is the core problem. These systems have amazing potential as tools to take large sums of data and answer questions that are difficult to impossible for human data collectors to do.
Selling these things as generators of artistic information or small tasks is lack luster and misses their bigger purpose.
Lmao. They basically just called it Jar Jar.
The ability to speak does not make you intelligent.
Human thinking is independent of human language. This isn’t new knowledge. Many people don’t even have an internal monologue. It’s long been understood that thought precedes words.
HOWEVER, that does not lend itself to the conclusion that language modeling can not ALSO be a way towards intelligence. We don’t have to recreate the process of human thought to achieve forms of useful intelligence.
It would be foolish to assume that the human brain’s methods are the only path towards intelligence. So, for example, treating language as a MAP of how humans think (as opposed to being the actual land depicted by that map) can very well show us the path to our destination.
We all understand that reading a map allows us to figure out how to get to a place. This is not some kind of „gotcha, AI is fake“ revelation. The map is different from the land it depicts but it is still a means of navigation.
Also consider; we humans make extensive use of language in *sharing knowledge* and, in a sense, demonstrating our thought processes to others. So… again, it is reasonable to believe it can be used to create a means of thought.
People are seeing a ghost in the machine. There is nothing there except fancy math which they’ve intentionally put into a black box to obscure the facts and remove any oversight.
I don’t understand how people can still call ai fancy auto complete.
Just use it for a while and get back to me. It’s not perfect on everything, but it can generally tell you almost anything you need to know. Anyone claiming otherwise is disingenuous or in a highly specialized field.
When will I be able to sell something to an LLM?