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Rodney Brooks on GPT-4

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Re: Rodney Brooks on GPT-4

#71

No world model ? A world model is so obvious papers like these are more confirmation than surprise https://arxiv.org/abs/2305.11169 https://arxiv.org/abs/2210.13382 There a certain sentiment that AGI however you wish to define it won't infact be a "We'll know it when we see it" situation but rather a "AGI will arrive long before consensus reaches its AGI". LLMs have made me believe this will 100% be the case, either…

This paper is startling in terms of a world model and how it seems to emerge in a specific example in the step from GPT3.5 to 4. https://arxiv.org/pdf/2303.12712.pdf

One of the prompts is "Here we have a book, 9 eggs, a laptop, a bottle and a nail. Please tell me how to stack them onto each other in a stable manner."

Gpt3.5 is hopeless. It says start from the bottle, balance the nail on top of it, then the eggs (somehow) then the laptop, then the book.

GPT 4 says put the book first, then the eggs on top of the book in a 3x3 grid, then the laptop, then the nail.

Re: Rodney Brooks on GPT-4

#72
post #68

As mentioned, much of our discussion is rote parroting. I can usually go into any hackernews thread and roughly know what the top discussions are going to be. It's not surprising that an AI trained on a large portion of the internet would thus look human like. If you really poke at GPT, you begin to realize it's fairly shallow. Human intelligence is like a deep well or pond, where as GPT is a vast but shallow ocean.…

> Making that ocean deeper is not a trivial problem that we can just throw more compute or data at. You can't possibly know that, given that we don't actually understand how LLMs work on a high level. > We've pretty much tapped out that depth with GPT4 GPT-4 is three months old and you're confident that its working principle cannot be extended further? Where do you get that confidence from?

Sam Altman said it himself. He seems like a reasonable source.

If you're familiar with other fields of AI, adding more and more layers to ResNet was the hotness for awhile, but the trick stopped working after awhile.

Re: Rodney Brooks on GPT-4

#73
post #38
post #33

> The large language models are a little surprising. I’ll give you that. I think this is the key point about LLMs that kind of explains the wide and polarized views on whether it understands or parrots, whether it can think or is the precursor to thinking or is a dead-end, whether it will catastrophically destroy the world, or “merely” make it steadily worse with bullshit, or just put a few industries out of a job. A…

The more I think about it the more I'm convinced I am basically just predicting/saying my next word whenever I speak.

This is a thought that LLMs caused me to have: the emotion of surprise is just the brain model of the world discovering that it had been poorly calibrated, or making poor predictions.

Re: Rodney Brooks on GPT-4

#74
post #33

> The large language models are a little surprising. I’ll give you that. I think this is the key point about LLMs that kind of explains the wide and polarized views on whether it understands or parrots, whether it can think or is the precursor to thinking or is a dead-end, whether it will catastrophically destroy the world, or “merely” make it steadily worse with bullshit, or just put a few industries out of a job. A…

> My current (tentative) resolution of the surprise is that language encoded way more information about reality than we thought it did. (Enough information that you can fully derive reality from language seems improbable, but iirc it did derive Othello and partly derived chess and I would have thought there wasn’t enough information in language to derive those without playing the games as well, so I can’t rule it out…

> A human being who did that without ever playing a game would also start out better than a typical novice.

I am quite skeptical of these arguments along the lines of “imagine a human read everything written on the topic…”.

What humans are doing when they read something is not what neural nets are doing when they read something. Humans are (idealistically) doing something like Feynman’s description of how he reads (or in this case, listens to) a theorem:

I had a scheme, which I still use today when somebody is explaining something that I’m trying to understand: I keep making up examples. For instance, the mathematicians would come in with a terrific theorem, and they’re all excited. As they’re telling me the conditions of the theorem, I construct something which fits all the conditions. You know, you have a set (one ball) – disjoint (two balls). Then the balls turn colors, grow hairs, or whatever, in my head as they put more conditions on. Finally they state the theorem, which is some dumb thing about the ball which isn’t true for my hairy green ball thing, so I say, ‘False!’"

Bret Victor’s description of what “really good programmers” are doing is also related:

[showing the code for binary search] In order to write code like this, you have to imagine an array in your head, and you essentially have to ‘play computer’. You have to simulate in your head what each line of code would do on a computer. And to a large extent those who we consider to be skilled software engineers are just those people who are really good at playing computer.

I think when we imagine an LLM as a human who’s read everything ever written in chess but never played an actual game, we’re actually tricking ourselves - because that hypothetical human would be ‘playing chess’ inside their head by imagining the pieces and moving them according to the rules they had read[1]. LLMs are not doing anything like that when they read about chess. So it’s a very restricted (or perhaps more accurately, a very different) kind of ‘reading’ that we don’t have any intuition for. Since the ‘reading’ that we do have an intuition for is smuggling in exactly the kind of “modeling the world” ability we’re looking for, it’s not surprising that this argument would incorrectly lead us to believe we’ve found it in LLMs.

1: In fact the very best computer chess is achieved by AlphaZero which was trained exclusively on “playing chess in its head”, and it beats even the most powerful and optimized search algorithms like Stockfish looking 20 moves ahead.

Re: Rodney Brooks on GPT-4

#75

Earlier quoted context omitted.

If LLMs are an understanding of the world it would mean humans in a few decades found a way to create sapience with many orders of magnitude fewer interacting elements than evolution did. I find that doubtful, at least in light of the fact every other way we've replicated biological computation requires many more computational elements.

Firstly, if humans created sapience, we created it with all the interacting elements that evolution used plus all the things that we used. Secondly, maybe sapience isn't as big of a deal as we thought compared with all the other things that evolution did. Remember that biological entities have to figure out survival, reproduction etc. Sapience emerges as a byproduct but the selective pressure is towards those things…

Alright, I give. What the heck is "sapience"?

Re: Rodney Brooks on GPT-4

#77
post #38

Earlier quoted context omitted.

The more I think about it the more I'm convinced I am basically just predicting/saying my next word whenever I speak.

Why not? Your brain is already making predictions about what you expect to see and hear as a part of your perception of reality anyways.

Right, but LLMs suggest that learning how to predict and only training on predictions is sufficient to learn anything and to have emergent generative abilities. What if learning to predict the upcoming input is all that's all that is needed for general human intelligence? What if it is all that any animals do?

Re: Rodney Brooks on GPT-4

#78
post #33

> The large language models are a little surprising. I’ll give you that. I think this is the key point about LLMs that kind of explains the wide and polarized views on whether it understands or parrots, whether it can think or is the precursor to thinking or is a dead-end, whether it will catastrophically destroy the world, or “merely” make it steadily worse with bullshit, or just put a few industries out of a job. A…

The issue with all these experts is they still think it's human nature to be able to fully understand the world before they speak about it. On the contrary it's human nature (and all animal nature) to figure out how to navigate the world without fully understanding or having a complete model of it. All you need is a working model that affects the facets of the world you need to deal with. I still remember in the 90s…

I remember a friend being confused by downloading. He didn't realize that meant we'd have a copy and not that the source would lose it in the process.

It seemed bizarre at the time, but, tbh, I didn't have _that_ much better of a model of how the whole process worked.

Re: Rodney Brooks on GPT-4

#79
post #33

> The large language models are a little surprising. I’ll give you that. I think this is the key point about LLMs that kind of explains the wide and polarized views on whether it understands or parrots, whether it can think or is the precursor to thinking or is a dead-end, whether it will catastrophically destroy the world, or “merely” make it steadily worse with bullshit, or just put a few industries out of a job. A…

> language encoded way more information about reality than we thought it did

Language is roughly what separates humans from other apes ... so why would it surprise us that it encodes much of the information of civilization?

Re: Rodney Brooks on GPT-4

#80
post #38
post #33

> The large language models are a little surprising. I’ll give you that. I think this is the key point about LLMs that kind of explains the wide and polarized views on whether it understands or parrots, whether it can think or is the precursor to thinking or is a dead-end, whether it will catastrophically destroy the world, or “merely” make it steadily worse with bullshit, or just put a few industries out of a job. A…

The more I think about it the more I'm convinced I am basically just predicting/saying my next word whenever I speak.

The question is whether you knew how that sentence was going to end when you started writing it, or indeed whether I knew that I was going to add this comma-separated adjunct when I started writing the preceding clause, and I cannot honestly say at this precise moment of typing whether the final word in this sentence is going to end up being 'yes' or 'no'.
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