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Many in the AI field think the bigger-is-better approach is running out of road

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Re: Many in the AI field think the bigger-is-better approach is running out of road

#331

Post by regularfry »

Retric wrote: post #322
regularfry wrote: post #319

Earlier quoted context omitted.

> That’s a statistical association not a concept. Try asking it questions that mix concepts like “Can you eat Apple share price?” which aren’t in its corpus. ChatGPT: > No, you cannot physically eat an Apple share or any other stock share. A share of a company's stock represents ownership in that company and is typically bought and sold on stock exchanges. Share prices fluctuate based on various factors such as suppl…

Obviously it gets such a simple case correct, the grammar makes the subject clear. I was illustrating the approach using your wording for clarity, Chess was the actual example. The Othello paper is hardly a counter example. Researchers created an Othello specific model that almost learned the grammar of Othello not how to play well. Yes, there was largely correct internal game state built up from past moves. No it di…

> The Othello paper is hardly a counter example. Researchers created an Othello specific model that almost learned the grammar of Othello not how to play well. Yes, there was largely correct internal game state built up from past moves. No it didn’t actually learn the rules so it would make strictly legal moves nor did it learn to make good moves

It is, though. Nobody said anything about playing well, or learning the rules. The very fact that it had a valid internal representation of the game state means it's extrapolated beyond token-level. Which is the point.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#332

Post by edgyquant »

quesera wrote: post #97
edgyquant wrote: post #54

Earlier quoted context omitted.

That hasn’t worked since about three months after companies found out people do it. It’s all astroturfing now days anyway and if it applies to products (which it for sure does) you can be sure that government actors caught on as well.

I'm not sure if it would help or restore the kinds of results you were seeing previously, but instead of adding "reddit", you can add "site:reddit.com" to get only results from that site. (Originally a Google feature, but works on DuckDuckGo also. Not sure about others.) Unless you mean that Reddit is astroturfed with the SEO garbage you're trying to avoid, in which case this will definitely not help. Is search on Re…

Yeah I mean it’s astroturfed and likely in ways beyond just corporate

Re: Many in the AI field think the bigger-is-better approach is running out of road

#333

Post by welshwelsh »

tempodox wrote: post #252
thomasahle wrote: post #185

Earlier quoted context omitted.

> An LLM can only ever reproduce what it has seen before. Anyone who's played around with these models know that at least some generalization is taking place.

So generalization counts as creativity in your book?

Yes, generalization is creativity. I don't think there's a difference between the two concepts.

Humans don't come up with ideas out of nowhere. Open a novel and you will find that even though the overall work is unique, it is composed of tropes from other literature and experiences from the author's life which have been generalized into another context.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#334

Post by sfpotter »

michaelteter wrote: post #327
sfpotter wrote: post #239

Earlier quoted context omitted.

This is a great set of observations. The only thing missing is an analysis of how much power it takes to accomplish each of these tasks. If ChatGPT-4 is at about 1:1 in terms of “effectiveness”, all that remains is to divide by the amount of power required to reach the answer using ChatGPT-4 vs by conventional means. If requires significantly more energy, then it’s a waste, and because of climate change we should rea…

This is a good point. There's a hidden cost (energy consumption) which we will eventually pay for. However, even in the 1:1 case it means I am training myself to become a "prompt engineer" rather than to be an actual thinker and problem solver. As long as there will always be another system for me to depend on, maybe that's ok. But as with people who never learned to read maps and navigate without GPS tend to be very…

I totally agree, although I think it's even worse than that.

In my opinion, a precondition for creativity and inventiveness is understanding. If you rely on a surrogate to give you answers, you will never reach the level of proficiency required to come up with something new. If we train a generation of thinkers to rely on an external brain to get anything done, they will only understand things superficially, and our ability to innovate at the society level will suffer.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#335

Post by kaliqt »

TeMPOraL wrote: post #42
Animats wrote: post #11

We need a way to make tight little specialist models that don't hallucinate and reliably report when they don't know. Trying to cram all of the web into a LLM is a dead end.

How much general "thinking"[0] would you want those "tight little specialist models" to retain? I think that cramming "all of the web" is actually crucial for this capability[1], so at least with LLM-style models, you likely can't avoid it. The text in the training data set doesn't encode just the object-level knowledge, but indirectly also higher-level, cross-domain and general concepts; cutting down on the size and…

I think you two are both right in the most obvious of ways... it needs to trained on a ton of data so it can comprehend better, and when it needs to fact-check or search for anything concrete, it should hit up domain experts and databases.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#336

Post by Retric »

regularfry wrote: post #331
Retric wrote: post #322

Earlier quoted context omitted.

Obviously it gets such a simple case correct, the grammar makes the subject clear. I was illustrating the approach using your wording for clarity, Chess was the actual example. The Othello paper is hardly a counter example. Researchers created an Othello specific model that almost learned the grammar of Othello not how to play well. Yes, there was largely correct internal game state built up from past moves. No it di…

> The Othello paper is hardly a counter example. Researchers created an Othello specific model that almost learned the grammar of Othello not how to play well. Yes, there was largely correct internal game state built up from past moves. No it didn’t actually learn the rules so it would make strictly legal moves nor did it learn to make good moves It is, though. Nobody said anything about playing well, or learning the…

> The very fact that it had a valid internal representation of the game state means it's extrapolated beyond token-level. Which is the point.

The paper said it was making incorrect moves thus it has an invalid representation of the game.

So an LLM when specifically trained on Othello, a game with very simple and completely mechanical rules, failed to abstract what those rules actually where. This means at a purely mechanical level it doesn’t understand the game when that was exclusively what it was trained to do.

It’s a clear illustration that these things are really really bad at abstraction. But that should be obvious because they are simply manipulating arbitrary tokens from their perspective. It doesn’t intuit that the game should have simple rules and therefore it doesn’t find them. People on the other hand have a real bias regarding simple rules.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#337

Post by nomel »

ChatGTP wrote: post #78
nomel wrote: post #49

Earlier quoted context omitted.

> that don't hallucinate “Hallucination” is part of thought. Solving a new problem requires hallucinating new, non existing, possible outcomes and solutions, to find one that will work. It seems that eliminating the ability to interpolate and extrapolate (hallucinations) would make intelligence impossible. It would eliminate creativity, tying together new concepts, creation, etc. Is the goal AI, or a nice database fr…

“Hallucination” is part of thought. Any evidence to support this claim or just commentary ?

Most intro courses to perception/cognition will cover the "gap filling" and "extrapolation" that takes place in a "data sparse" context that is the human experience/speech, but here's a related article [1]. I think "speculative thinking" and "heuristic simulation" are more appropriate/technical terms [2] (and surely involved in AGI). I used "hallucination" a bit liberally, but with text being the only "sense" of an LLM, I think it's somewhat reasonable.

Disclaimer: I know little of this field.

[1] https://www.scientificamerican.com/article/perception-and-me...

[2] https://www.frontiersin.org/articles/10.3389/fpsyg.2021.7289....

Re: Many in the AI field think the bigger-is-better approach is running out of road

#338

Post by nulltype »

School-Cotton wrote: post #293
michaelteter wrote: post #63

Isn't the fundamental problem that LLM's don't actually understand anything (as greater concepts), but rather operate as complex probability machines? My 2 month active experience with ChatGPT-4 gave me the following takeaways: - when it's right, it's amazing; and when you, the operator, can recognize the niche use case where it performs really well, it can be a game-changer (although you could have programmed a tool…

I still don’t understand what it means when people say stuff like “ChatGPT just predicts the most likely next word with the highest probability” or “ChatGPT is just a probability machine”. Concretely, given the N most recent words, what algorithm are you proposing/claiming it uses to assign probabilities to word N+1? Just saying “it chooses the next word with highest probability” doesn’t explain how the probabilities…

[0]

[0] http://jalammar.github.io/illustrated-transformer/

Re: Many in the AI field think the bigger-is-better approach is running out of road

#339

Post by letitgo12345 »

flangola7 wrote: post #191
letitgo12345 wrote: post #71

Earlier quoted context omitted.

It's been literally just 3 months since GPT-4 was released. I think you're gonna see a lot of changes especially once GPT starts being trained on ChatGPT data

OpenAI can just filter out text that they generated. Other models might be problematic.

I'm talking about RLHF on their chatbot data, not ChatGPT data on the web.

Re: Many in the AI field think the bigger-is-better approach is running out of road

#340

Post by usrbinbash »

exitb wrote: post #303
usrbinbash wrote: post #232

Earlier quoted context omitted.

What specifically about these observations contradicts my statement? Wrong statements about Python are simply less probable than wrong statements about Rust, since there is more Python than Rust in the training data. That changes exactly nothing about the fact that the system isn't able to detect when it makes a blunder in Python.

You've claimed that LLMs create most probable output, which does not necessarily align with truth. So a bigger LLM will be better at creating the most probable output, but that would not translate into being more truthful. That could be interpreted as "better LLMs are expected to be better bullshitters". That is not what we've observed though. Quite the opposite - we're seeing that the bigger LLM is and the more doma…

> You've claimed that LLMs create most probable output, which does not necessarily align with truth.

No, that is not my claim. That is part of the explanation for it.

My claim is this: An LLM is incapable of knowing when it produces false information, as it simply doesn't have a concept of "truthfulness". It deals in probabilities, not alignment with objective reality.

And it doesn't matter how big you make them...this fact cannot change, as it is rooted in the basic MO of language models.

So, now that we have covered what my claim actually is...

> That is not what we've observed though. Quite the opposite - we're seeing that the bigger LLM is and the more domain-specific material it digested, the more truthful it becomes.

...I can ask what this observation has to do with it, and the answer is: Nothing at all. LMs with more params may produce untruthful statements less often, but what does this change about their ability to recignize when they do produce them? And the answer is: Nothing. They still can't.

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