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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"

#231

Post by robbrown451 »

wbillingsley wrote: post #188
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 m…

"LLMs do not directly model the world; they train on and model what people write about the world"

This is true. But human brains don't directly model the world either, they form an internal model based on what comes in through their senses. Humans have the advantage of being more "multi-modal," but that doesn't mean that they get more information or better information.

Much of my "modeling of the world" comes from the fact that I've read a lot of text. But of course I haven't read even a tiny fraction of what GPT4 has.

That said, LLMs can already train on images, as GPT4-V does. And the image generators as well do this, it's just a matter of time before the two are fully integrated. Later we'll see a lot more training on video and sound, and it all being integrated into a single model.

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

#232

Post by janalsncm »

xcv123 wrote: post #201
user_named wrote: post #195

Earlier quoted context omitted.

At every point in time there are a range of moves with different levels of optimality. That range changes at the next point in time following the opponent's move.

The opponents strategy is an unknown variable not determined by the current board state. Therefore the best move cannot be determined by the current board state, as it cannot be determined in isolation from the opponents strategy.

The optimal strategy can be determined from the current state. This is the principle behind minimax.

In a perfect information zero sum game, we can theoretically draw a complete game tree, each terminal node ending with a win, loss, or draw. With a full understanding of the game tree we can make moves to minimize our opponent’s best move.

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

#233

Post by eviks »

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…

The issue with Alpha Zero analogy extremes is that those are extremely constrained conditions, so can't be generalized to something infinitely more complicated like speech

And

> When training, it is never going to be 100% accurate in predicting text it hasn't trained on, but it can continue to get closer and closer to 100% the more it trains.

For example, it can reach 25% of accuracy and have an math limit of 26%, so "forever getting closer to 100% with time" would still result in a waste of even infinite resources

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

#234

Post by wraptile »

breadwinner wrote: post #106

There is evidence that the human brain is also doing "autocomplete" (prediction). The human brain uses predictive mechanisms when processing language, and these mechanisms play an important role in forming thoughts. When we hear or read a word, our brain quickly generates a set of predictions about what word might come next, based on the context of the sentence and our past experiences with language. These prediction…

It's not only language or some tasks - it's literally everything. Predictive Processing Theory proposes that our whole model is predicting future and only then confirming it through our input signals (eyes, ears etc). I highly recommend The Experience Machine by Andy Clark which explains and arguments this theory very convincingly to the point where I firmly believe it to be true.

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

#236

Post by [deleted] »

wbillingsley wrote: post #188
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 m…

[deleted]

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

#237

Post by therealdrag0 »

abeyer wrote: post #215
nearbuy wrote: post #212

Earlier quoted context omitted.

An LLM absolutely doesn't respond the same way each time if asked the same question three times in a row, with temperature (randomness) set to zero. It responds the same way only if you start a new chat, which is a clean instance with no memory of the previous conversation. For a human, this is like if you went back in time to just before you asked the question, and asked them the same question again, in which case t…

> For a human, this is like if you went back in time to just before you asked the question, and asked them the same question again, in which case the person would give the same answer Is it? Would they? You seem to assert that there's no "temperature" in human behavior... which is a reasonable theory, but not one that's universally accepted nor likely to be provable.

No I think they’re saying the temperature in human behavior comes from the “random” noise of inputs around us and ongoing history. But rewinding history and playing it back with the same temperature dice rolls is the only way to have the same thing a a LLM with no random inputs.

LLMs run in simulated environments where you can control randomness so you need the same for a human to compare the two. You can’t just ask a human a question multiple times as everything around them changes and conclude the human is behaving differently because they answer differently the same question. The question is not the bounds of relevant context; the entire operating environment is!

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

#238

Post by therealdrag0 »

juhanima wrote: post #213
bigyikes wrote: post #211

Earlier quoted context omitted.

What are you talking about? This is trivially shown to be incorrect. I just asked ChatGPT the same thing three times in a row, and it gave me three different answers, with the latter two answers being shorter and rephrased. >I would imagine any sentient object would give a different answer every time. The first time it would give you an honest answer based on what it knows about the topic. The second time it would be…

ChatGPT works by cumulating the prompt. You didn't ask the same question three times. In stead you asked question q, then qq and finally qqq. Those are three different questions, which explains why you got different answers. I'm not sure if ChatGPT also cumulates its previous answers in the context. It might do that as well. In that case the prompts would be q, qaq and qaqaq where 'q' is your question and 'a' the ear…

Everything you said applies to humans doesn’t it?

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

#239

Post by nearbuy »

juhanima wrote: post #218
abeyer wrote: post #215

Earlier quoted context omitted.

> For a human, this is like if you went back in time to just before you asked the question, and asked them the same question again, in which case the person would give the same answer Is it? Would they? You seem to assert that there's no "temperature" in human behavior... which is a reasonable theory, but not one that's universally accepted nor likely to be provable.

And of course "temperature" is just an euphemism for the artificial randomness that is mixed in to make the output appear more magical.

The term "temperature" has been used in machine learning for a long time and came from using it as a parameter during training, analogous to physical temperature in https://en.wikipedia.org/wiki/Boltzmann_distribution.

But the relevant point is that we can reset the state of an LLM to its initial state before you asked it anything. This is a feature. You can choose to persist memory (through training, fine-tuning, databases, or context window), or you can choose to wipe it. If we could do the same for a human (eg, by going back in time), the person would behave the same way as the LLM. They wouldn't get annoyed that you asked the same question three times. They wouldn't know they've been asked before.

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

#240

Post by astrange »

xcv123 wrote: post #185
astrange wrote: post #183

Earlier quoted context omitted.

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 d…

> 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.

You're using "your model" as a metaphorical term here, but if you came up with any precise definition of the term here, it'd turn out to be wrong; people have tried this since the 50s and never gotten it correct. (For instance, is it actually a singular "a model" or is it different disconnected things you're using a single name for?)

See Phil Agre (1997) on exactly this idea: https://pages.gseis.ucla.edu/faculty/agre/critical.html

David Chapman (more general and current): https://metarationality.com/rationalism

and this guy was saying it in the 70s: https://en.wikipedia.org/wiki/Hubert_Dreyfus#Dreyfus'_critic...

> limited to some degree of accuracy

This isn't the only issue:

- You may not have observed something in the room in the right way for the action you need to do later.

- You might have observed it in a way you don't need later, which is a waste of time and energy.

- It might change while you're not looking.

- You might just forget it. (Since people do this, this must be an adaptive behavior - "natural selection" - but it's not a good thing in a model.)

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

What principle can you use to decide how precise it should be? (You can't do this; there isn't one.)

> The model of reality in your head is a simplification that serves a purpose.

Not only does it serve a purpose, your observations largely don't exist until you have a purpose for them.

RL agents tend to get stuck investigating irrelevant things when they try to maintain models; humans are built to actively avoid this with attention and boredom. Robot cameras take in their entire visual field and try to interpret it; humans both consciously and unconsciously actively investigate the environment as needed alongside deciding what to do. (Your vision is mostly fake; your eyes are rapidly moving around to update it only after you unconsciously pay attention to something.)

> Natural selection weeds out that kind of behaviour.

Not that well since something like half of Americans are myopic…

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