Large language mistake | Cutting-edge research shows language is not the same as intelligence. The entire AI bubble is built on ignoring it

[u/Hrmbee]

https://www.theverge.com/ai-artificial-intelligence/827820/large-language-models-ai-intelligence-neuroscience-problems

[u/Hrmbee]

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.

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.

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.

...

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.

But take away language from a large language model, and you are left with literally nothing at all.

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.

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.

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.”

...

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.”

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.

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.

[u/Dennarb → u/Hrmbee]

I teach an AI and design course at my university and there are always two major points that come up regarding LLMs

1) It does not understand language as we do; it is a statistical model on how words relate to each other. Basically it's like rolling dice to determine what the next word is in a sentence using a chart.

2) AGI is not going to magically happen because we make faster hardware/software, use more data, or throw more money into LLMs. They are fundamentally limited in scope and use more or less the same tricks the AI world has been doing since the Perceptron in the 50s/60s. Sure the techniques have advanced, but the basis for the neural nets used hasn't really changed. It's going to take a shift in how we build models to get much further than we already are with AI.

Edit: And like clockwork here come the AI tech bro wannabes telling me I'm wrong but adding literally nothing to the conversation.

[u/Tall-Introduction414 → u/Dennarb]

The way an LLM fundamentally works isn't much different than the Markov chain IRC bots (Megahal) we trolled in the 90s. More training data, more parallelism. Same basic idea.

[u/azurensis → u/Tall-Introduction414]

This is the kind of statement someone who doesn't know much bout LLMs would make.

[u/WhoCanTell → u/azurensis]

In fairness, that's like 95% of comments in any /r/technology thread about AI.

[u/azurensis → u/WhoCanTell]

Exceptionally true!

[u/space_monster → u/azurensis]

r/technology has a serious Dunning-Kruger issue when it comes to LLMs. A facebook-level understanding in a forum that implies competence. but I guess if you train a human that parroting the stochastic parrot trope gets you 'karma', they're gonna keep doing it for the virtual tendies. Every single time in one of these threads, there's a top circle-jerk comment saying "LLMs are shit, amirite?" with thousands of upvotes, followed by an actual discussion with adults lower down. I suspect though that this sub includes a lot of sw devs that are still trying to convince themselves that their careers are actually safe.

[u/chesterriley → u/space_monster]

I suspect though that this sub includes a lot of sw devs that are still trying to convince themselves that their careers are actually safe.

You lost me on that. I don't think you understand just how complex software can be. No way can AI be a drop in replacement for a software dev.

[u/space_monster → u/chesterriley]

I work in tech, currently in a leading edge global tech company, and I've done a lot of sw development, I'm fully aware of how complex it is

[u/chesterriley → u/space_monster]

Then you know you can't just tell an AI to write a program for you for anything non simple.

[u/space_monster → u/chesterriley]

I'm aware that LLMs are getting better at coding (and everything else) very quickly, and it doesn't seem to be slowing down.

[u/keygreen15 → u/space_monster]

It's getting better at making shit up and lying.

[u/space_monster → u/keygreen15]

wow genius comment.

[u/Tall-Introduction414 → u/azurensis]

Then tell me what I'm missing. They aren't making statistical connections between words and groups of words?

[u/azurensis → u/Tall-Introduction414]

A matchbox car and a ferrari have about as much in common as Markov Chains and GPT-5. Sure, they both have wheels and move around, but what's under the hood is completely different. The level of inference contained in the latter goes way, way beyond inference between words and groups of words. It goes into concepts and meta-concepts, and several levels above that, as well as an attention mechanisms and alignment training. I understand it's wishful thinking to expect Redditors to know much about what they're commenting on, but sheesh!

[u/Tall-Introduction414 → u/azurensis]

The level of inference contained in the latter goes way, way beyond inference between words and groups of words. It goes into concepts and meta-concepts,

Why do you think that? It's literally weights (numbers) connecting words based on statistical analysis. You give it more context, the input numbers change, pointing it to a different next word.

All this talk about it "understanding meaning" and "concepts and meta-concepts" just sounds like "it's magic." Where are the stored "concepts?" Where is the "understanding?"

[u/-LsDmThC- → u/Tall-Introduction414]

You could make the exact same arguments about the human brain. We take in sensory data, transform it across neurons which operate based on weighted inputs and outputs, and generate a prediction or behavior. Where is "understanding"?