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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"

#181

Post by xcv123 »

SkiFire13 wrote: post #121
robbrown451 wrote: post #99

I agree with Hinton, although a lot hinges on your definition of "understand." I think to best wrap your head around this stuff, you should look to the commonalities of LLM's, image, generators, and even things like Alpha Zero and how it learned to play Go. Alpha Zero is kind of the extreme in terms of not imitating anything that humans have done. It learns to play the game simply by playing itself -- and what they f…

> One other thing to take into consideration, is that to play the game of Go you can't just think of the next move. You have to think far forward in the game -- even though technically all it's doing is picking the next move, it is doing so using a model that has obviously looked forward more than just one move. It doesn't necessarily have to look ahead. Since Go is a deterministic game there is always a best move (o…

> Since Go is a deterministic game there is always a best move

The rules of the game are deterministic, but you may be going a step too far with that claim.

Is the game deterministic when your opponent is non-deterministic?

Is there an optimal move for any board state given that various opponents have varying strategies? What may be the best move against one opponent may not be the best move against another opponent.

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

#182

Post by kragen »

sometimes people writing stack overflow answers stop to think, or test a piece of code they are going to suggest as a solution, or even go back and edit words they've already written

this is also true of books, sometimes, and even blog posts

consequently, a model that models the processes of stopping to think or testing code, or considers whether a given word might be revised after more thought, will do better at predicting the next word of stack overflow answers or books or blog posts than one that only models humans' reactive free association processes

therefore andrew gelman is mistaken about the limitations of that loss function

that logic of course doesn't imply anything in particular about transformers or any other ann architecture, but we can, i hope, observe that currently deployed software is already doing such modeling to some extent

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

#183

Post by astrange »

xcv123 wrote: post #165
astrange wrote: post #164

Earlier quoted context omitted.

If you close your eyes, how long can you navigate in the environment without hitting something? Not long, because you didn't model it. If you're taking out the recycling, do you take the time to identify (model) each piece of it first? No, because that's not necessary.

Wait, you actually think we are talking about modelling as a conscious deliberate process in active working memory? Well there's your fundamental mistake. That is not what we are discussing, not even remotely. The vast model in your brain is learned and generated unconsciously without your direct awareness.

No, I didn't say anything about doing it consciously. Motion is largely unconscious, like how you can throw things at a target without thinking about it.

But if you're just using it to mean "factual memory", calling it modeling seems like false precision.

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

#184

Post by theGnuMe »

robbrown451 wrote: post #99

I agree with Hinton, although a lot hinges on your definition of "understand." I think to best wrap your head around this stuff, you should look to the commonalities of LLM's, image, generators, and even things like Alpha Zero and how it learned to play Go. Alpha Zero is kind of the extreme in terms of not imitating anything that humans have done. It learns to play the game simply by playing itself -- and what they f…

Alphazero runs monte carlo tree search so it has a next move "planning" simulator. This computes the probability that specific moves up to some distance lead to a win.

LLMs do not have a "planning" module or simulator. There is no way the LLM can plan.

Could build a planning system into an LLM? Possibly and probably, but that is still open research. LeCunn is trying to figure out how to train them effectively. But even an LLM with a planning system does not make it AGI.

Some will argue that iteratively feeding the output embedding back into the input will retain the context but even in those cases it rapidly diverges or as we say "hallucinates"... still happens even with large input context windows. So there is still no planning here and no world model or understanding.

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

#185

Post by xcv123 »

astrange wrote: post #183
xcv123 wrote: post #165

Earlier quoted context omitted.

Wait, you actually think we are talking about modelling as a conscious deliberate process in active working memory? Well there's your fundamental mistake. That is not what we are discussing, not even remotely. The vast model in your brain is learned and generated unconsciously without your direct awareness.

No, I didn't say anything about doing it consciously. Motion is largely unconscious, like how you can throw things at a target without thinking about it. But if you're just using it to mean "factual memory", calling it modeling seems like false precision.

Oh well in that case the answer is straightforward.

If you close your eyes and get lost after a few seconds, that's because that aspect of your model was not a 100% perfect exact replica of external reality that extended infinitely far in all spatial directions at all resolutions. For example, your internal spatial model is limited to some degree of accuracy and does not include the entire surface of Mars, but that doesn't mean that your model does not exist at all. Models are not perfect by definition. I thought this would be obvious.

Why would you think any model has to be a perfect exact 1:1 representation of the entire universe?

The model of reality in your head is a simplification that serves a purpose. Arbitrarily closing your fully functioning eyes is not something your model generating hardware was evolutionarily optimized for. Natural selection weeds out that kind of behaviour.

If you become blind then your model will change and optimize for other sensory inputs. Think of a blind man with a cane.

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

#186

Post by theGnuMe »

jon_richards wrote: post #112
robbrown451 wrote: post #99

I agree with Hinton, although a lot hinges on your definition of "understand." I think to best wrap your head around this stuff, you should look to the commonalities of LLM's, image, generators, and even things like Alpha Zero and how it learned to play Go. Alpha Zero is kind of the extreme in terms of not imitating anything that humans have done. It learns to play the game simply by playing itself -- and what they f…

> to play the game of Go you can't just think of the next move. You have to think far forward in the game -- even though technically all it's doing is picking the next move, it is doing so using a model that has obviously looked forward more than just one move. While I imagine alpha go does some brute force and some tree exploration, I think the main "intelligent" component of alpha go is the ability to recognize a "…

The "meta" here is just the probability distribution of stone densities. The only way it can process those is by monte Carlo simulation. The DNN (trained by reinforcement learning) evaluates the simulations and outputs the top move(s).

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

#187

Post by theGnuMe »

huytersd wrote: post #119
robbrown451 wrote: post #99

I agree with Hinton, although a lot hinges on your definition of "understand." I think to best wrap your head around this stuff, you should look to the commonalities of LLM's, image, generators, and even things like Alpha Zero and how it learned to play Go. Alpha Zero is kind of the extreme in terms of not imitating anything that humans have done. It learns to play the game simply by playing itself -- and what they f…

Next word generation is one way to put it. The key point here is we have no idea what’s happening in the black box that is the neural network. It could be forming very strong connections between concepts in there with multi tiered abstractions.

It is certainly not abstracting things.

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

#188

Post by wbillingsley »

robbrown451 wrote: post #99

I agree with Hinton, although a lot hinges on your definition of "understand." I think to best wrap your head around this stuff, you should look to the commonalities of LLM's, image, generators, and even things like Alpha Zero and how it learned to play Go. Alpha Zero is kind of the extreme in terms of not imitating anything that humans have done. It learns to play the game simply by playing itself -- and what they f…

LLMs are very good at uncovering the mathematical relationships between words, many layers deep. Calling that understanding is a claim about what understanding is. But because we know how the LLMs we're talking about at the moment are trained, it seems to have more problems:

LLMs do not directly model the world; they train on and model what people write about the world. It is an AI model of a computed gestalt human model of the world, rather than a model of the world directly. If you ask it a question, it tells you what it models someone else (a gestalt of human writing) is most likely say. That in turn is strengthened if user interaction accepts it and corrected only if someone tells it something different.

If we were to define that as what "understanding" is, we would equivalently be saying that a human bullshit artist would have expert understanding if only they produced more believable bullshit. (They also just "try to sound like an expert".)

Likewise, I'm not convinced that we can measure its understanding just by identifying inaccuracies or measuring the difference between its answers and expert answers - There would be no difference between bluffing your way through the interview (relying on your interviewer's limitations in how they interrogate you) and acing the interview.

There seems to be a fundamental difference in levels of indirection. Where we "map the territory", LLMs "map the maps of the territory".

It can be an arbitrarily good approximation, and practically very useful, but it's a strong ontological step to say one thing "is" another just because it can be used like it.

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

#189

Post by greenthrow »

robbrown451 wrote: post #99

I agree with Hinton, although a lot hinges on your definition of "understand." I think to best wrap your head around this stuff, you should look to the commonalities of LLM's, image, generators, and even things like Alpha Zero and how it learned to play Go. Alpha Zero is kind of the extreme in terms of not imitating anything that humans have done. It learns to play the game simply by playing itself -- and what they f…

This is absolute nonsense. The game of Go is a grid and two colors of pieces. "The world" here is literally everything.

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

#190

Post by theGnuMe »

tysam_and wrote: post #174
nighthawk454 wrote: post #159

I don’t see how this article even responds to the quote. Hinton didn’t make any claims that because it’s autocomplete it’s not thinking. If anything he’s saying really truly good autocomplete necessarily takes more understanding/thinking than a derogatory interpretation of ‘autocomplete’ would suggest. Somehow OP seemed to twist that into “because I think on autopilot most of the time, then chatbots must think too”.…

Yeah, it's a very silly article with wrong mathematical reasoning. Hinton is quite obviously talking about a much more information-theoretic approach to the process, but he's phrasing it in people-friendly terms. What's a little more concerning to me is that people are reading and upvoting it. I think, because I have hopes and aspirations about working on some very hard problems and communicating them to the public a…

>information-theoretic approach to the process

Can you elaborate on this? I've studied some information theory and I don't see it.

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