[wodderam]

It feels like an exercise in anthropomorphization to me.

Sapir-Whorf hypothesis is generally not considered to be reality. It makes intuitive sense but is wrong.

There are hours of podcasts with Chomsky talking about LLMs. The gist of which is that LLMS are extracting surface level statistical structure of language that will be good for routine coding and not much else. It is easy to infer that Chomsky would believe this idea to be utter nonsense.

I believe even the idea of getting a 1000 people together and we agree to label a rock "rock", a tree "tree", a bird "bird" is not even how human language works. Something that is completely counter intuitive.

Reading the paper, no one believes a hidden markov model is creating some kind of new thought process in the hidden state.

I certainly though could have no idea what I am talking about with all this and have pieced together parts that make no sense while this is a breakthrough path to AGI.

[digbybk]

> There are hours of podcasts with Chomsky talking about LLMs

I'm not an expert, but it seems like Chomsky's views have pretty much been falsified at this point. He's been saying for a long time that neural networks are a dead end. But there hasn't been anything close to a working implementation of his theory of language, and meanwhile the learning approach has proven itself to be effective beyond any reasonable doubt. I've been interested in Chomsky for a long time but when I hear him say "there's nothing interesting to learn from artificial neural networks" it just sounds like a man that doesn't want to admit he's been wrong all this time. There is _nothing_ for a linguist to learn from an actually working artificial language model? How can that possibly be? There were two approaches - rule-based vs learning - and who came out on top is pretty damn obvious at this point.

[jokethrowaway]

What can you learn from something parroting data we already have?

Similarly, we are now finding that training on synthetic data is not helpful.

What would have happened if we invested 1/100 of what we spent on LLM on the rule based approach?

[int_19h]

There is an old joke that AI researchers came up with several decades ago: "quality of results is inversely proportional to the number of linguists involved".

This has been tried repeatedly many times before, and so far there has been no indication of a breakthrough.

The fundamental problem is that we don't know the actual rules. We have some theories, but no coherent "unified theory of language" that actually works. Chomsky in particular is notorious for some very strongly held views that have been lacking supporting evidence for a while.

With LLMs, we're solving this problem by bruteforcing it, making the LLMs learn those universal structures by throwing a lot of data at a sufficiently large neural net.

[digbybk]

> What can you learn from something parroting data we already have?

You can learn that a neural network with a simple learning algorithm can become proficient at language. This is counter to what people believed for many years. Those who worked on neural networks during that time were ridiculed. Now we have a working language software object based on learning, while the formal rules required to generate language are nowhere to be seen. This isn’t just a question of what will lead to AGI, it’s a question of understanding how the human brain likely works, which has always been the goal of people pioneering these approaches.

[coldtea]

>Sapir-Whorf hypothesis is generally not considered to be reality. It makes intuitive sense but is wrong

Strong S-W (full determinism) might not be, but there's hardly a clear cut consensus on the general case.

And the whole "scientific field" is more like psychology, with people exchanging and shooting down ideas, and less like Math and Physics, so any consensus is equally likely to be a trend rather than reflecting some hard measurable understanding.

I'd say that the idea S-W is not to a degree reality is naive.

[PittleyDunkin]

> Sapir-Whorf hypothesis is generally not considered to be reality.

This is true only in the strictest terms of the hypothesis, i.e. linguistic determinism. Language still encodes a lot of culture (& hence norms and values) in its grammar & diction—this isn't very controversial.

Granted, I don't think this is that related to the topic at hand. There's bias all over the decisions in how to train and what to train on; choice of language is just one facet of that.

[pjerem]

Well maybe not 1000 people but to our knowledge, the human brain is actually made of physically independent zones that barely communicate together except with the zone that take all the outputs together and tries to do something coherent with all the garbage.

Idk if this could work with LLMs, especially because all the brain zones are somehow specialized into something while two LLMs are just identical machines. But we also know that the specialization isn’t that hardcoded : we know that people losing half their brain (after a stroke) can still relearn things that were managed in the "dead" part.

I don’t know, please correct my errors, I was just thinking aloud to say that multiple independent agents working together may be how "intelligence" already works in the biological world so why not for AIs ?

[IshKebab]

> the human brain is actually made of physically independent zones that barely communicate together except with the zone that take all the outputs together and tries to do something coherent with all the garbage.

That sounds like bullshit. Do you have a source?