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Rodney Brooks on GPT-4

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Re: Rodney Brooks on GPT-4

#111

Post by usaar333 »

seanhunter wrote: post #87
jmyeet wrote: post #70

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 ma…

Most of the time when people find a maths problem that they can trick the model into getting wrong, it's also possible to get the model to give the correct answer with better prompting. A trick that's worth knowing is just to ask the model to give each step in the solution and explain as it goes. This gives the model "time to think" and leads to better results.

Pretty sure you can't get GPT-4 to do 8 digit multiplication with any prompt.

For what it's worth, I'm not even sure if chain of thought provides much value to GPT-4. The RLHF it went through seems to have encouraged more logical thinking already.

Re: Rodney Brooks on GPT-4

#112

Post by cageface »

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.

Maybe in casual conversation but that's not how I experience my though process about anything non trivial at all. I usually spend a lot of time thinking about the concept in non verbal terms and that process involves recalling images and sensory information in fairly abstract terms and then through what feels like several iterations it starts to coalesce into something I can encode in language.

I think we can all agree that these LLMs are surprisingly good at generating text that is often coherent but I don't see how you can discard all those extra inputs and claim you have the same process.

Re: Rodney Brooks on GPT-4

#113

Post by famouswaffles »

steveBK123 wrote: post #84
thatguyknows wrote: post #72

Earlier quoted context omitted.

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.

Exactly, and OpenAI has been around nearly 8 years, consumed huge amount of data with tons of compute. They are just showing us the product now. It is possible they've reached some 80/20 point and he is pretty honest about how much more extendable the current approach really is. Would explain going to congress and asking for regulation (of their not-quite-there-yet competitors who they want a regulatory moat against)…

https://news.ycombinator.com/item?id=36017977

Re: Rodney Brooks on GPT-4

#114

Post by wombatpm »

grugagag wrote: post #9
RC_ITR wrote: post #7

Earlier quoted context omitted.

Remember when the Internet was new and no-one believed anything on it? Then, learning what to believe became a marketable skill for many people? Then society fundamentally changed because not everyone learned that skill? This is just that again. Gen Z will joke about their millennial/Gen X bosses believing anything the AI tells them and it will probably lead to some sort of mainstream conspiracy that Jackie O herself…

> Remember when the Internet was new and no-one believed anything on it? Is this true?

On the internet no one knew you were a dog according to the Net Yorker cartoon

Re: Rodney Brooks on GPT-4

#115

Post by anybodyz »

fwlr wrote: post #74
consilient wrote: post #43

Earlier quoted context omitted.

> 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 (o…

What LLM's are doing when they imagine playing chess is what we do when we stand up after sitting on the floor, or what we do when we see a few million individual samples of color and light intensity and realize there's an apple and a knife in front of us.

I think what is almost impossible for most people to understand is that AI's do not need to be structured like the human brain and use the crutches we use to solve problems the way we do because evolution did not provide us with a way of instantly understanding complex physics or instantly absorbing the structure of a computer program by seeing it's code in one shot.

Re: Rodney Brooks on GPT-4

#116

Post by jiggawatts »

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.

Elephant and whale brains are both bigger than human brains, but they're less intelligent than us. Much of the volume of their brains goes towards controlling a larger body. Conversely, some birds have much smaller brains than any primate but can learn to solve simple puzzles and problems.

Re: Rodney Brooks on GPT-4

#117

Post by xvilka »

p-e-w wrote: post #82
zone411 wrote: post #56

Rodney Brooks' deep learning predictions have not aged well. For example, in his 2018 blog post ( http://rodneybrooks.com/forai-steps-toward-super-intelligenc... ), he rates various approaches from 1-3 (with 3 being the best). Neural nets scored: Composition: 1 Grounding: 3 Spatial: 1 Sentience: 1 Ambiguity: 2 At that time, the potential of neural nets was already very clear. He also predicted that by 2020 we'll have…

I'll let you in on a secret: There aren't actually any "AI experts". There are machine learning experts, that is, people whose expertise lies in designing and analyzing systems that perform (semi-)automatic inference on data. But nobody can be an expert on "artificial intelligence", because we don't know what that word really means. We don't even know what intelligence really is. We have no idea how the human mind wo…

> We have no idea how the human mind works.

True, but we already know the so called "neural networks" that many computer scientists believe are how brain works aren't even close. They are all based on half-a-century old concept of neuron that was debunked many times over, experimentally by real neuroscientists.

Re: Rodney Brooks on GPT-4

#119

Post by cageface »

yberreby wrote: post #14
sheeshkebab wrote: post #5

Earlier quoted context omitted.

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…

> 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? Because it can be significantly faster to check something for correctness than to produce it? More so when the correctness check can itself be automated to some extent.

My experience with Github copilot is that the time it saves me typing out boilerplate has been more than lost when I have to spend time carefully debugging bugs in the code it produces. And those are the bugs I catch right away.

I expect this will improve but it's certainly not always the case that checking something is cheaper or easier than generating it in the first place.

Re: Rodney Brooks on GPT-4

#120

Post by Hendrikto »

quad_eye_oh wrote: post #73
JoshTko wrote: 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.

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.

Sounds like the free energy principle [0]:

> The free energy principle is based on the Bayesian idea of the brain as an “inference engine.” Under the free energy principle, systems pursue paths of least surprise, or equivalently, minimize the difference between predictions based on their model of the world and their sense and associated perception.

[0]: https://en.m.wikipedia.org/wiki/Free_energy_principle

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