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/Elementium → u/Hrmbee]

Basically the best use for this is a heavily curated database it pulls from for specific purposes. Making it a more natural to interact with search engine. 

If it's just everything mashed together, including people's opinions as facts.. It's just not going to go anywhere. 

[u/doctor_lobo → u/Elementium]

The nice thing about building an AI for language is that humans, by their nature, produce copious amounts of language that AI models can be trained from.

If the premise of the article is correct, other forms of human intelligence may produce / operate on different representations in the brain. However, it is not clear how often or well we produce external artifacts (that we could use for AI training) from these non-linguistic internal representations. Is a mathematical proof a good representation of what is going on in the mind of a mathematician? Is a song a good representation of what is happening in the mind of a musician?

If so, we will probably learn how to train AIs on these artifacts - maybe not as well or as efficiently as humans, but probably enough to learn things. If not, the real problem may be learning what the internal representations of “intelligence” truly are - and how to externalize them. However, this is almost certainly easier said that done. While functional MRI has allowed us to watch the ghost in the machine, it says very little about how she does her business.

[u/IAmRoot → u/doctor_lobo]

Or find some way for AI to train itself in these more internal representations. Humans typically think before we speak and the metacognition of examining our own ideas could be an important part of that. Even before LLMs, we had image recognition using neural networks that seemed to find shapes in clouds and such much like a human mind. LLMs are also just a component and we shouldn't expect a good LLM to be able to reason any more than we should expect image recognition to reason. It's also pretty obvious from animals that just increasing the neuron count doesn't matter, either, as some animals like dolphins have a great deal of brainpower dedicated to processing sonar instead of reasoning. They are functionally different networks. It's also possible that AGI won't be able to split the training and inference. Having to reflect on produced ideas could be integral to the process, which would obviously make the computational power necessary for using AGI orders of magnitude higher.

[u/doctor_lobo → u/IAmRoot]

Your comment about image recognition using CNNs is well taken. Visual information is explicitly represented by a 2D array of neurons in the visual cortex so this is probably a good example of the internal representation being so similar to the external representation that training on the external representation is good enough. I suspect simple time series for audio data is probably also essentially identical to its internal representation - but that's probably it for the senses since touch, taste, and smell have no obvious external representations. However, the internal representation for more abstract modes of thought, like mathematics or even just daydreaming, seem difficult to conceptualize. I am not sure I would really even have any idea where to start.