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I disagree with Geoff Hinton regarding "glorified autocomplete"

statmodeling.stat.columbia.edu

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Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#31

Post by drwiggly »

pixl97 wrote: post #23
tayo42 wrote: post #6

This kind of was on my mind recently, I was wondering, when I speak am I just spitting out the next word that makes sense or is there more to it. I think there is for people, I can think before I speak, I can plan out my thought entirely before turning it into words. Each invocation of the chat bot/llm is a new set of probabilities. I can plan what my 2nd token output will be and stick to it. Llm models dont have the…

>I can think before I speak, I can plan out my thought entirely before turning it into words. In theory models can/could do the same thing. Think of the current text output of a model being those thoughts inside your head, you have an internal scratch space you work on ideas, then after you perform 'chain of thought' on them, you output to the world. What you're outputting to the world isn't your directly thoughts, b…

>Really for LLMs you just need to have the model put it's output to an internal buffer, read that buffer and make sure it makes sense, then output that to the end user.

Makes sense to what. The LLM doesn't have a goal, other then to spew text that looks like it should be there.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#32

Post by dboreham »

overactor wrote: post #8

I think this misses the point made by Hinton. The point is that, if a model is to reach a certain level of autocomplete, it needs to understand what it is talking about. Imagine the result of what required deep thought from you is included in the training data, along with billions of other such exchanges. At some point the only way to improve training score is to develop the ability to "understand" things. (That is,…

> it needs to understand what it is talking about

"to understand" is a concept invented by the human brain to help it operate. It doesn't actually mean anything concrete in the physical world. See: the Chinese room argument (paradoxically it proves the converse of what Searle was arguing -- that there is no such thing as understanding, because you can emulate a system with identical outputs from the same set of inputs without caring or labeling what happens inside the room).

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#33

Post by wyago »

cmdli wrote: post #21

The argument “a sufficiently capable autocomplete must contain a level of general intelligence” is correct but also not very useful. It is a lot like saying “a sufficiently fast horse can fly”. It is technically correct that when you take things to the extreme you can accomplish great things, but we may not reach those levels. We may require completely different technology to reach those levels of autocomplete, and w…

Funnily enough, "a sufficiently fast horse can fly" sounds sort of like a decent way to convey the idea of planes to a pre-car world.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#34

Post by RandomLensman »

satuke wrote: post #12
mbrumlow wrote: post #5

> If you want to be really good, you have to understand what’s being said. That’s the only way. This is simply not true. Predicting the next letter or word, or id you abstract it away from things that mean something to you, like the next color of a block in a long chain of colored blocks. You would realize that all we are doing is using statistics to predict what the next item might be. There simply is no need or req…

Isn't that exactly how humans learn to respond to stimuli? Don't we just try to predict the best next response to everything? Yes, It's statistics but the fun part is nobody is writing this statistical function by hand.

Yes, if you are in the no free will school of thought, then that would be what humans do.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#35

Post by tayo42 »

pixl97 wrote: post #23
tayo42 wrote: post #6

This kind of was on my mind recently, I was wondering, when I speak am I just spitting out the next word that makes sense or is there more to it. I think there is for people, I can think before I speak, I can plan out my thought entirely before turning it into words. Each invocation of the chat bot/llm is a new set of probabilities. I can plan what my 2nd token output will be and stick to it. Llm models dont have the…

>I can think before I speak, I can plan out my thought entirely before turning it into words. In theory models can/could do the same thing. Think of the current text output of a model being those thoughts inside your head, you have an internal scratch space you work on ideas, then after you perform 'chain of thought' on them, you output to the world. What you're outputting to the world isn't your directly thoughts, b…

Yeah thinking about it more, it does seem possible.

Like the attention masking done in transformers, I get why, but I also never understood why you wouldn't want attention to happen, at least about concepts and abstract ideas about what is coming up in the phrase before predicting a word

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#36

Post by im_down_w_otp »

Joeri wrote: post #27
albertzeyer wrote: post #10

Earlier quoted context omitted.

> You would realize that all we are doing is using statistics to predict what the next item might be. So what does "understanding" really means then? "Understanding" is not really well defined. Either we (humans) do it, but then LLMs might just do it as well, depending on the definition of "understanding", or we both don't do it. But if no-one is really "understanding" anything, then this definition of "understanding…

Nobody actually understands how humans actually understand something, just like nobody actually understands how LLMs do what they do. Everybody opining about it is doing just that: offering an opinion. Geoff Hinton’s opinion is worth more than someone else’s, but it is still an opinion.

I don’t know about the human part, but we absolutely understand how LLMs do what they do. They’re not magic.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#37

Post by scarmig »

overactor wrote: post #17
hopfenspergerj wrote: post #11

Earlier quoted context omitted.

I agree. We should prompt the model with the statement of the Riemann hypothesis. If the autocomplete is good, the model will output a proof.

No human can solve the Riemann Hypothesis. Why do you expect an AI to do it before you consider it able to understand things?

The key bit is constructing a hard, novel proof. The fact that AI doesn't (yet) do this isn't evidence that it doesn't reason, but if it did so it would be strong evidence that it does reason.

(I also take the pessimistic point of view that most humans don't reason, so YMMV.)

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#38

Post by [deleted] »

mbrumlow wrote: post #5

> If you want to be really good, you have to understand what’s being said. That’s the only way. This is simply not true. Predicting the next letter or word, or id you abstract it away from things that mean something to you, like the next color of a block in a long chain of colored blocks. You would realize that all we are doing is using statistics to predict what the next item might be. There simply is no need or req…

[deleted]

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#39

Post by lordnacho »

civilized wrote: post #29
satuke wrote: post #12

Earlier quoted context omitted.

Isn't that exactly how humans learn to respond to stimuli? Don't we just try to predict the best next response to everything? Yes, It's statistics but the fun part is nobody is writing this statistical function by hand.

LLMs don't have a concept of "best". Only most likely in what they've been trained on. I think LLMs ultimately just take imitation to a creative and sophisticated extreme. And imitation simply doesn't comprise the whole of human intelligence at all, no matter how much it is scaled up. The sophistication of the imitation has some people confused and questioning whether everything can be reduced to imitation. It can't.…

Ok, so now we need an example that separates humans from LLMs?

I struggle to think of one, maybe someone on HN has a good example.

Eg if I'm in middle school and learning quadratic equations, am I imitating solving the problem by plugging in the coefficients? Or am I understanding it?

Most of what I see coming out of chatGPT and copilot could be said to be either. If you're generous, it's understanding. If not, it's imitation.

Re: I disagree with Geoff Hinton regarding "glorified autocomplete"

#40

Post by usrbinbash »

satuke wrote: post #12
mbrumlow wrote: post #5

> If you want to be really good, you have to understand what’s being said. That’s the only way. This is simply not true. Predicting the next letter or word, or id you abstract it away from things that mean something to you, like the next color of a block in a long chain of colored blocks. You would realize that all we are doing is using statistics to predict what the next item might be. There simply is no need or req…

Isn't that exactly how humans learn to respond to stimuli? Don't we just try to predict the best next response to everything? Yes, It's statistics but the fun part is nobody is writing this statistical function by hand.

> Isn't that exactly how humans learn to respond to stimuli?

Maybe it is, maybe it isn't. Maybe we are "just" an incredibly powerful prediction engine. Or maybe we work from a completely different modus operandi, and our ability to predict things is an emergent capability of it.

The thing is, no one actually knows what makes us intelligent, or even how to define intelligence for that matter.

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