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Rodney Brooks on GPT-4

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Re: Rodney Brooks on GPT-4

#61

Post by XorNot »

causality0 wrote: post #49
fwlr wrote: 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…

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.

Why would that be surprising though? The natural world climbs up to understanding from a severe disadvantage. Chemicals don't understand the world - the natural world had to be randomly build every bit of its systems from scratch, before it could produce a succinct descriptive system for it.

LLMs on the other hand start out with a succinct descriptive system, and translate that to the world of chemicals and photons via some very complicated naturally evolved systems.

Re: Rodney Brooks on GPT-4

#62

Post by thatguyknows »

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. We've pretty much tapped out that depth with GPT4 and are going to need better designs.

This could only take half a decade or it could be half a century. Plenty of enterprises stagnate for decades.

Re: Rodney Brooks on GPT-4

#63

Post by jstx1 »

Gigachad wrote: post #25
quickthrower2 wrote: post #8

It is a disruption. It now has more wood behind the arrow. You can’t ignore it just because it lies today for some inputs.

Feels like there have been a lot of previous technologies where the last 10% was far more complex than the first 90%. Self driving cars were pretty much solved a decade ago, and yet we still aren't there yet. VR was pretty much working and ready to change the world a decade ago, and we still aren't there yet. So it's hard to tell if this is an iphone moment where it just rockets off in to space and changes the world.…

The difference is that GPT-4 is already useful even in its current imperfect form. It’s not a 90% thing that needs to get to 100% for us to use it.

Re: Rodney Brooks on GPT-4

#64

Post by p-e-w »

> It gives an answer with complete confidence, and I sort of believe it. And half the time, it’s completely wrong.

That's bullshit, unless you are asking questions specifically designed to make GPT-4 hallucinate. For most real-world, everyday topics, the accuracy is close to 100%. GPT-4 would be utterly useless otherwise.

Re: Rodney Brooks on GPT-4

#65

Post by seanhunter »

causality0 wrote: post #49
fwlr wrote: 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…

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 so sapience is only selected for to the extent that it also moves forward those other goals.

By contrast, LLM training is just focussed on the task of making the model better. The model doesn't have to figure out how to feed itself, ward off predators, not accidentally die in the myriad ways things die, reproduce itself etc. It's way more specific. It doesn't seem unreasonable to think that the complexity would be lower given it's not trying to achieve nearly as much.

Re: Rodney Brooks on GPT-4

#66

Post by agotterer »

sheeshkebab wrote: post #5
softwaredoug wrote: post #3

Annoyed at all these N=1 articles from prominent thinkers about this stuff. Especially from scientists - can these sorts of folks please more carefully quantify, how often it’s “wrong” and then from that decide whether or not to “calm down”. Right now I suspect we hear from the outliers on both ends of the spectrum here. People who either see AGI happening tomorrow and the more dismissive crowd. But aside from what w…

Anytime I ask these things something (bard, gpt etc), 33% of the answer is genius, 33% misleading garbage, 33% filler stuff that’s neither here or there The problem is distinguishing between these parts requires me to be be an expert in the area I’m inquiring about - and then why the heck do I need to ask some idiot bot for answers to questions that I already know an answer to? I don’t know who finds these things use…

I personally find value because it saves me time. I’ll ask chat-gpt to write something slightly more complex than boilerplate code for me based on some requirements. Because I’m an “expert” I can read/run the code and message back improvements and tweaks until to arrives at something satisfactory. It certainly doesn’t always produce correct code from the start and I often encounter syntax errors or code which doesn’t work. However, I’ve found it pretty good at remedying those issues when I describe what’s wrong.

It produces something which is a good enough starting place. Sure, I could have written the code myself because I already know how. But I’ve found it saves me time and require minimal effort.

Re: Rodney Brooks on GPT-4

#67

Post by famouswaffles »

astrange wrote: post #52
famouswaffles wrote: post #21

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…

Do you know what a "world model" is? It's a thing people were assumed to have in 1970s psychology, but was never well-defined enough to tell if it exists or not, so I don't think it's obvious anything else has one. https://twitter.com/Meaningness/status/1639120720088408065 I think "common sense" or "long term memory" might be more productive things to say.

The brain does a lot of hierarchical prediction with sense data. When new information comes in, it references "something" and adjusts the sense data likewise.

That's why when you shift your eyes quickly, you see blurred images pass by. In reality, you should be seeing complete black because the brain doesn't actually process visual information that shifts so quickly.

But your brain "knows" it should see...well something. And so it fits that blurred passthrough as compensation. Completely made up data. But not ungrounded data, data that seems like it should fit according to that "something". That "something" being referenced is a world model.

That's a sub-conscious example but you can think of a lot of times people consciously reference "something" to better take actions or make decisions.

For Othello-GPT, training seemed to induce a learned board state that the model referenced at every Inference to make moves.

It seems that fully accurate or not, deep predictions might require "something" to reference. Else perhaps you might need to derive everything from first principles at every turn. Not very efficient.

Re: Rodney Brooks on GPT-4

#68

Post by p-e-w »

thatguyknows wrote: post #62

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?

Re: Rodney Brooks on GPT-4

#69

Post by morkalork »

JoshTko wrote: post #38
fwlr wrote: 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.

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.

Re: Rodney Brooks on GPT-4

#70

Post by jmyeet »

GPT-4 is pretty amazing but I, too, feel this is being overhyped. For me, a sobering example is how OpenAI does math (eg [1]). Specifically, the model clearly doesn't really understand multiplication and "learns" it from training data. This tends to get the first few and last few digits right for a simple multiplication with 6-7 digit numbers.

Now you can solve that with plugins (eg training the model to recognize math problems and have access to a calculator) so it's a solvable problem but you realize there's an extremely long tail of such problems. It goes to show that GPT-4 isn't "magic" and we still have a long way to go.

[1]: https://www.reddit.com/r/OpenAI/comments/12donja/gpt4_and_ma...

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