If you put 1000 dumb people together, they don't magically become smart?
It's about everyboty having a say in decisions of government that affects them.
The failure of democracy as a system is not when people make dumb decisions (experts and high-IQ people have made some of the most stupid and catastrophic decisions in history), but when people's collective decisions are not being respected.
May be true but who knows.
I wonder if anyone has somehow tested the Sapir-Whorf hypothesis for LLMs somehow by training them on different languages and comparing task performance. I guess it's too difficult to get a large equivalent training set in different languages.
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.
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 ?
That sounds like bullshit. Do you have a source?
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.
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.
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.
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?
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.
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.
It works fairly well in my native language, I’m surprised to learn that things get translated back.
But there's also no guarantee any particular query generalizes (vs is memorized), so it might only be able to answer some queries in some languages.
1000 is probably too high, but groups of people are in fact more intelligent than individuals (though for humans it is likely because recognizing a correct answer is easier than finding it in the first place)
also, the bottlenecks that teamwork helps solve (eg the high cost of gaining expertise and low throughput of reasoning capacity) may not be that relevant in the ai age
Sure, but the result would still be far better than the average of the output of the 20 individuals taken alone.
> also, the bottlenecks that teamwork helps solve (eg the high cost of gaining expertise and low throughput of reasoning capacity) may not be that relevant in the ai age
It's always tempting to anthropomorphize these systems and conclude that what works for us would work for them, but yes we don't really know if it would bring anything to AI.
Dysfunctional groups which do the opposite will be catastrophically stupid.
There have been plenty of dysfunctional groups in history.
Is there a way of selecting people to cover each other's intellectual blind spots?
Do they not become smart*er* though?
And yet, here we are.
A group of 1000 apes is large enough to have offspring and, given time, go through evolution.
Diversity of opinions: Different perspectives bring a range of estimates. Independence: Errors aren't systematically biased as long as individuals estimate without external influence. Error averaging: Overestimation and underestimations balance out when averaged. Law of large numbers: More participants increase accuracy by minimizing random errors. It was demonstrated by Francis Galton in 1906, where a crowd's average guess of a bull's weight was almost spot-on. (estimates must be independent and reasonably informed for this to work.)
People learn by being around others being both successful and unsuccessful.
sarcasm aside, throwing away the existing corpus in favor of creating a new one from scratch seems misguided.
this paper isn't about creating a new language, they are omitting the sampler that chooses a single token in favor of sending the entire end state back in to the model like a superposition of tokens. that's the breadth first search part, they don't collapse the choice down to a single token before continuing so it effectively operates on all of the possible tokens each step until it decides it's done.
it would be interesting to try this with similar models that had slightly different post training if you could devise a good way to choose the best answer or combine the outputs effectively or feed the output of a downstream model back in to the initial model, etc. but I'm not sure if there'd necessarily be any benefit to this over using a single specialized model.
https://ai.meta.com/research/publications/large-concept-mode...
The interesting thing then would be - does it converge to similar embedding space as the input, or can LLMs create a more efficient "language".
Or maybe this is my own ignorant confabulation, so nvm.