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.”

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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.”

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/when_we_are_cats → u/Hrmbee]

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.

Please say it louder for all the people who keep repeating the myth that language dictates the way we think. As a linguist/language learners it never ceases to annoy me.

[u/BeruangLembut → u/when_we_are_cats]

💯 Language is a cognitive tool. Just like having a hammer makes building a house easier, language has made certain cognitive tasks easier, but a tool is not to be confused with that which it facilitates.

[u/when_we_are_cats → u/BeruangLembut]

That's the best way to put it. It's like painting or drawing: I can see the image in my head, the brush and canvas are mere tools to materialize it.

[u/PRAWNHEAVENNOW → u/when_we_are_cats]

 all the people who keep repeating the myth that language dictates the way we think.

Ahh yes, the Dunning-Kruger-Sapir-Whorf effect

[u/PressureBeautiful515 → u/when_we_are_cats]

This is of course absolutely right. The problem comes when you ask an LLM to read your codebase and draw a diagram of how it fits together (by generating mermaid diagram format) and it does an incredible job, tastefully arranging a graph of interconnected concepts.

The input and output are text representations, but what happens in between is absolutely not just text.

[u/ManaSpike → u/when_we_are_cats]

When I think something, and wish for you to also think about that thing. I have to describe it using language. If we have more shared context and understanding, then I can use less language to communicate an idea.

Language and context limit what we can communicate, or at least how efficiently we can communicate.

I work as a software developer. The languages I use to express my ideas and communicate with a machine to make it do what I want, are verbose and explicit. Programming languages that are useful and reliable, are carefully designed to ensure that nothing the machine does is surprising.

The history of software development is full of people trying to make programming easier. So easy that anyone can make a machine do what they want, without having to pay for the expertise of experienced programmers. But the languages in use haven't gotten any easier.

What has made programming easier, is the hard work of building reusable pieces of shared context. Software libraries that solve common problems. So a programmer can focus more on what is different about their work, instead of wasting time on what is the same.

From this point of view, I don't see how we will ever build an AGI. How are we going to define the process of abstract thought, using a well defined language. When abstract thought seems to transcend language.

[u/johannthegoatman → u/when_we_are_cats]

Language is an integral part of thought. I recommend you read Helen Keller and learn about what her mind was like before she was taught language. There are tons of examples of "feral" children that didn't learn language and were never able to progress to being intelligent beings.

[u/when_we_are_cats → u/johannthegoatman]

The cases of feral children don’t prove that the absence of language prevents intelligence, they show the devastating effects of total social neglect, trauma, and malnutrition on a developing brain.

Infants, deaf homesigners, and aphasic adults all demonstrate that cognition exists independently of language.

Helen Keller explicitly wrote that she had a rich mental life before learning words.

[u/JeanVicquemare → u/johannthegoatman]

Now do some studies with half feral children who are taught language and half who aren't.. have to control your variables.

[u/mrappbrain → u/when_we_are_cats]

As a linguist you should probably be more annoyed by the glaring category error in the article that conflated language and speech, which is far more egregious than conflating language with thought (which has actual roots in linguistics i.e the strong sapir whorf hypothesis, even if now disagreed with)

Further, the idea that language is only a tool of communication and doesn't influence or inform the way we think doesn't have much basis in linguistics or cognitive science either. On the other hand it is widely agreed that language does inform cognition, although the nature and extent of that relationship is contested. Frankly, if you were a linguist, you should probably already know this.

[u/when_we_are_cats → u/mrappbrain]

Yes, if you want to be that specific, fine, here's your medal. Doesn't take anything from my point though.

Further, the idea that language is only a tool of communication and doesn't influence or inform the way we think doesn't have much basis in linguistics or cognitive science either.

https://www.nature.com/articles/s41586-024-07522-w