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Rodney Brooks on GPT-4

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Re: Rodney Brooks on GPT-4

#261

Post by tornato7 »

RandomLensman wrote: post #254
tornato7 wrote: post #250

Earlier quoted context omitted.

https://i.imgur.com/feEiiZA.png I tried to stack all of these objects myself and couldn't really. I think GPT-4's approach is actually really good. It correctly points out that the gummy worms make a flexible base for the DSLR (otherwise the protruding buttons/viewfinder make it wobbly on the hard book), and the light bulbs are able to nestle into the front of the lens. If they were smaller light bulbs I could probab…

Might also put the light bulbs as a base (especially if in a box). They are pretty sturdy and can hold a book.

The point is that ChatGPT undeniably built a world model good enough to understand the physical and three-dimensional properties of these items pretty well, and it gives me a somewhat workable way to stack them, despite never having seen that in its training data.

Re: Rodney Brooks on GPT-4

#262

Post by DiogenesKynikos »

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…

I don't think it's at all surprising that a system designed to predict the next word ends up understanding the world.

If you want to predict the next word accurately, you first have to know which words exist. To progress, you'll have to learn about the mechanics of grammar and which words are used more frequently or in combination. To become even more accurate, it helps to understand context, so that the sentences you string together will at least be relevant to the subject. If you want to increase your accuracy even further, you'll have to start memorizing all sorts of facts (e.g., "Who was the monarch of England in 1600?"). Being able to synthesize those facts into a coherent argument will increase your accuracy even further.

In the end, predicting the next word accurately requires an understanding of the world.

This isn't all that different from how our own intelligence evolved. You could look at humans from the outside and disparagingly point out that the ultimate purpose of the human brain is to direct muscle motions in a way that maximizes the chances of reproductive success. It just turns out that solving that problem effectively has led to the development of an enormously complicated piece of machinery, capable of synthesizing all sorts of input stimuli into a coherent picture of the world, and ultimately of producing the works of Shakespeare and the music of Beethoven.

Re: Rodney Brooks on GPT-4

#263

Post by namaria »

anon84873628 wrote: post #233
Sai_ wrote: post #181

Earlier quoted context omitted.

This is an interesting question. To paraphrase as per my understanding of your comment, is intelligence an emergent property of being able to interact with each other through language? Say I speak gibberish (to you) which is actually me explaining to you the theory of relativity, would you consider me intelligent?

What's the difference between "understanding" and "having really good probabilistic information about how words combine"? Kids learn to speak by parroting what they hear and observing the outcome. Then they run tests that reinforce the connections between words. That's what the model is . But humans also get to link words with all the other sense experience we have (like how sweet cherries, loud fire trucks, and that…

Spot on. That we have internal language models doesn't mean we only have language models as cognition. People seem to be redefining imposter syndrome as reflected by stand alone language models. Makes me think of Tropic Thunder. "Am I even real". Lol get a grip people.

Re: Rodney Brooks on GPT-4

#264

Post by leobg »

ramraj07 wrote: post #158
krainboltgreene wrote: post #83

Earlier quoted context omitted.

> 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 None of the experts think this.

Is Chomsky an expert? Try reading his infuriatingly tone-deaf NYTimes editorial.

He’s doing the same to ChatGPT that he did to Skinner in the 1960s. Back then, it put him on the map, and he lives in the illusion that he was right. Now ChatGPT is pretty much Skinner’s work come to life. The BFS book Chomsky critiqued was literally “Verbal Behavior” - a book about how intelligence arises from “dumb” reinforcement learning of words. Obviously Chomsky must now claim that ChatGPT only pretends to be intelligent. Else his entire life’s work is proven wrong.

Re: Rodney Brooks on GPT-4

#265

Post by namaria »

yblu wrote: post #149
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…

What I found disheartening was many of those scientists, especially those on the "nothing to worry about" camp, seemed not to entertain the thought that they could be wrong, considering the scale of the matter, i.e. human extinction. If there's a chance AI poses an existential threat to us, even if it is 0.00000001% (I made that up), should they be at least a bit more humble? This is uncharted domain and I find it in…

Humans have a start in time and will have an end. I was born and I will die. I don't know why we're so obsessed about this. We will most definitely cease existing soon in geological/cosmic time scale. Doesn't matter.

Re: Rodney Brooks on GPT-4

#266

Post by [deleted] »

aappleby wrote: post #128
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…

What exactly does "understanding the world" really mean?

[deleted]

Re: Rodney Brooks on GPT-4

#267

Post by namaria »

transcriptase wrote: post #162
yblu wrote: post #149

Earlier quoted context omitted.

What I found disheartening was many of those scientists, especially those on the "nothing to worry about" camp, seemed not to entertain the thought that they could be wrong, considering the scale of the matter, i.e. human extinction. If there's a chance AI poses an existential threat to us, even if it is 0.00000001% (I made that up), should they be at least a bit more humble? This is uncharted domain and I find it in…

Debatable, since there are plenty of other unavoidable existential threats that are far more likely than the best estimates that AI will wipe us out. E.g. supervolcano eruption, massive solar flare, asteroid impact, some novel virus. At least we can take comfort in the fact that if an AI takes us out, one of the aforementioned will avenge us and destroy the AI too on a long enough time scale.

I find striking that we have a rich cultural tradition of claiming we're artificial beings. Maybe we're building a successor lifeform... I've thought about this as a story premise: humans and robots are two stages of a lifecycle. Humans flourish on a planetary ecosystem, build robots that go on to colonize new systems where they seed humans because (reason I haven't been able to formulate).

Re: Rodney Brooks on GPT-4

#268

Post by namaria »

ShaddyDC wrote: post #221
goatlover wrote: post #215

Earlier quoted context omitted.

So what would be the path for GPT5 or 6 creating an improved model of itself? It's not enough to generate working code. It has to come up with a better architecture or training data.

The idea is that a model might already be smarter than us or at the very least have a very different thought process from us and then do something like improving itself. The problem is that it's impossible for us to predict the exact path because it's thought up by an entity whose thinking we don't really understand or are able to predict.

I think "llm builds better llm" is drawing the border at the wrong place. Technical progress has been accelerating for centuries. It's pretty self evident that the technological civilization is improving upon itself.

Re: Rodney Brooks on GPT-4

#269

Post by jiggawatts »

omnicognate wrote: post #241
jiggawatts wrote: post #152

Earlier quoted context omitted.

I am in no shape, way, or form affiliated with OpenAI or any other AI company. What I and many others have noticed about the "Are LLMs really smart?" debate is that everyone on the "Nay" side is using 3.5 and everyone on the "Yay" side is using 4.0. The naming and the versioning implies that GPT 4 is somehow slightly better than 3.5, like not even a "full +1" better, just "+0.5" better. (This goes to show how trivial…

I've been paying for GPT-4 since it came out and have used it extensively. It's clearly an iteration on the same thing and behaves in qualitatively the same way. The differences are just differences of degree. It's not hard to get a feel for the "edges" of an LLM. You just need to come up with a sequence of related tasks of increasing complexity. A good one is to give it a simple program and ask what it outputs. Then…

Most developers -- let alone humans -- I've met can't run trivial programs in their head successfully, let alone complex ones.

I've thrown crazy complicated problems at GPT 4 and had mixed results, but then again, I get mixed results from people too.

I've had it explain a multi-page SQL query I couldn't understand myself. I asked it to write doc-comments for spaghetti code that I wrote for a programming competition, and it spat out a comment for every function correctly. One particular function was unintelligible numeric operations on single-letter identifiers, and its true purpose could only be understood through seven levels of indirection! It figured it out.

The fact that we're debating the finer points of what it can and can't do is by itself staggering.

Imagine if next week you could buy a $20K Tesla bipedal home robot. I guarantee you then people would start arguing that it "can't really cook" because it couldn't cook them a Michelin star quality meal with nothing but stale ingredients, one pot, and a broken spatula.

Re: Rodney Brooks on GPT-4

#270

Post by RandomLensman »

tornato7 wrote: post #261
RandomLensman wrote: post #254

Earlier quoted context omitted.

Might also put the light bulbs as a base (especially if in a box). They are pretty sturdy and can hold a book.

The point is that ChatGPT undeniably built a world model good enough to understand the physical and three-dimensional properties of these items pretty well, and it gives me a somewhat workable way to stack them, despite never having seen that in its training data.

You cannot conclude that from the output - the training data will likely contain a lot stacking things. Everyday objects also might have some stacking properties that make these questions easy to answer even with semi-random answers.

Plus, some stuff clearly makes no sense or is ignored (like the gummy worms in the center, forgetting about the succulent in some cases).

If you want to test world modeling, give it objects it will have never encountered, describe them and then ask to stack etc. For example, a bunch of 7 dimensional objects that can only be stacked a certain way.

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