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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"

#251

Post by user_named »

jeffparsons wrote: post #220
user_named wrote: post #214

Earlier quoted context omitted.

Okay, so then tell me how does it decide whether it is true or false that Biden is the POTUS? It's response is not based on facts about the world as it exists, but on the text data it has been trained on. As such, it is not able to determine true or false even if the response in the above example would be correct.

Serious question, in pursuit of understanding where you're coming from: in what way do you think that your own reckoning is fundamentally different to or more "real" than what you're describing above? I know I don't experience the world as it is, but rather through a whole bunch of different signals I get that give me some hints about what the real world might be. For example, text.

LLMs only knows it's text embeddings. It does not know the real world. Clear?

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

#252

Post by theGnuMe »

nighthawk454 wrote: post #203
theGnuMe wrote: post #190

Earlier quoted context omitted.

>information-theoretic approach to the process Can you elaborate on this? I've studied some information theory and I don't see it.

I think the analogy is something like: if you have a simple distribution over all words, then that's just word frequency. Obviously not a good predictor. The 'information' necessary to predict the correct next word contextually is just not there if you're predicting words in a vacuum. In order to be practically useful and predict the right words _in context_, the model must be conditioning off of more of the sentence…

That's not information theoretic, that's just conditional probability.

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

#253

Post by dbsmith83 »

abm53 wrote: post #98
civilized wrote: post #52

Earlier quoted context omitted.

It is very easy to separate humans from LLMs. Humans created math without being given all the answers beforehand. LLMs can't do that yet. When an LLM can create math to solve a problem, we will be much closer to AGI.

You can ask ChatGPT to solve maths problems which are not in its training data, and it will answer an astonishing amount of them correctly. The fact that we have trained it on examples of human-produced maths texts (rather than through interacting with the world over several millennia) seems like more of an implementation detail and not piece of evidence about whether it has “understood” or not.

They also get problems wrong, in the most dumb way possible. I've tested it out many times where the LLM got most of the more 'difficult' part of the problem right, but then forgot to do something simple in the final answer--and not like a simple error a human would make. It's incredibly boneheaded, like forgetting to apply the coefficient it solved for and just returning the initial problem value. Sometimes for coding snippets, it says one thing, and then produces code which does not even incorporate the thing it was talking about. It is clear that there is no actual conceptual understanding going on. I predict the next big breakthroughs in physics will not be made by LLMs--even if they have the advantage of being able to read every single paper ever published, because they cannot think.

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

#254

Post by maltelau »

MVissers wrote: post #226
stubybubs wrote: post #125

Earlier quoted context omitted.

I gave it the three lightbulbs in a closet riddle. https://puzzles.nigelcoldwell.co.uk/seven.htm The key complication is "once you've opened the door, you may no longer touch a switch." It gets this. There are many examples of it written out on the web. When I give it a variation and say "you can open the door to look at the bulbs and use the switches all you want" and it is absolutely unable to understand this. To a…

GPT-4 on platform.openai.com says this on the first try: Switch on the first switch and leave it on for a few minutes. Then, switch it off and switch on the second switch. Leave the third switch off. Now, walk into the room. The bulb that is on corresponds to the second switch. The bulb that is off and still warm corresponds to the first switch because it had time to heat up. The bulb that is off and cool corresponds…

I think your comment misunderstands the comment you're responding to.

The point is that while LLMs can solve the puzzle when the constraints are unchanged -- as you said, there are loads of examples of people asking and answering variations of this puzzle on the internet -- but when you change the constraints slightly ("you can open the door to look at the bulbs and use the switches all you want") it is unable to break out of the mold and keeps giving complicated answers, while a human would understand that under the new constraints, you could simply flip each switch and observe the changes in turn.

A similar example that language models used to get stuck on is this: "Which is heavier, a pound of feathers or two pounds of bricks?"

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

#255

Post by xcv123 »

user_named wrote: post #249
xcv123 wrote: post #223

Earlier quoted context omitted.

> It's response is not based on facts about the world as it exists, but on the text data it has been trained on How did you find out that Biden was elected if not through language by reading or listening to news? Do you have extra sensory perception? Psychic powers? Do you magically perceive "facts" without any sensory input or communication? Ridiculous. By the same argument your knowledge is also not based on "facts…

You didn't answer my question ergo you concede that LLMs don't know true or false.

I did answer your question indirectly. By the reasoning in your argument, you yourself also don't know true or false. Your argument is logically flawed.

Do LLMs know true or false? It depends on how you define "know". By some definitions, they "know true or false" better than humans, as they can explain the concept and solve logic problems better than most humans can. However, by any definition that requires consciousness, they do not know because they are not conscious.

The average person spends a lot of time completely immersed in "false" entertainment. Actors are all liars, pretending to be someone they are not, doing things that didn't really happen, and yet many people are convinced it is all "true" for at least a few minutes.

People also believe crazy things like Flat Earth theory or that the Apollo moon landings were faked.

So LLMs have a conceptual understanding of true/false, strong logical problem solving to evaluate truth or falsity of logical statements, and factual understanding of what is true and false, better than many humans do. But they are not conscious therefore they are not conscious of what is true or false.

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

#256

Post by IshKebab »

civilized wrote: post #158
IshKebab wrote: post #154

Earlier quoted context omitted.

> Did a wise arbiter of truth set up goalposts that I moved? Collectively, yes. The criticism of AI has always been "well it isn't AI because it can't do [thing just beyond its abilities]. Maybe individually your goalpost hasn't moved, and as soon as it invents some maths you'll say "yep, it's intelligent" (though I strongly doubt it). But collectively the naysayers in general will find another reason why it's not re…

Other than complaining about perceived inconsistencies in others' positions, what do you actually believe? Do you think GPT is AGI?

No. I don't think anyone seriously believes that. AGI requires human level reasoning and it hasn't achieved that, despite what benchmarks show (they tend to focus on "how many did it get right" more than "how many did it fail in stupid ways").

The issue with most criticism of LLMs wrt AGI is that they come up with totally bogus reasons why it isn't and can't ever be real intelligence.

It's just predicting the next word. It's a stochastic parrot. It's only repeating stuff it has been trained on. It doesn't have quantum microtubules. It can't really reason. It has some failure modes that humans don't. It can't do .

Seems to be mostly people feeling threatened. Very tedious.

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

#257

Post by xcv123 »

user_named wrote: post #251
jeffparsons wrote: post #220

Earlier quoted context omitted.

Serious question, in pursuit of understanding where you're coming from: in what way do you think that your own reckoning is fundamentally different to or more "real" than what you're describing above? I know I don't experience the world as it is, but rather through a whole bunch of different signals I get that give me some hints about what the real world might be. For example, text.

LLMs only knows it's text embeddings. It does not know the real world. Clear?

Humans and other creatures only know their sensory data input. Therefore they also don't know the real world.

Your eyes and ears perceive a tiny minuscule fraction of what is out there in the real world.

A blind and deaf person must know even less of the real world than an LLM, which can read more than a human can ever read in their lifetime.

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

#258

Post by astrange »

xcv123 wrote: post #245
astrange wrote: post #240

Earlier quoted context omitted.

> 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, i…

So basically you agree with what I was saying. > What principle can you use to decide how precise it should be? It is not up to me or anyone else to decide. Our subjective definitions and concepts of the model are irrelevant. How the brain works is a result of our genetic structure. We don't have a choice.

You can design a human if you want, that's what artificial intelligence is supposedly all about.

Anyway, read the paper I linked.

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

#259

Post by xcv123 »

janalsncm wrote: post #232
xcv123 wrote: post #201

Earlier quoted context omitted.

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.

I stand corrected. Thanks for that explanation.

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

#260

Post by xcv123 »

astrange wrote: post #258
xcv123 wrote: post #245

Earlier quoted context omitted.

So basically you agree with what I was saying. > What principle can you use to decide how precise it should be? It is not up to me or anyone else to decide. Our subjective definitions and concepts of the model are irrelevant. How the brain works is a result of our genetic structure. We don't have a choice.

You can design a human if you want, that's what artificial intelligence is supposedly all about. Anyway, read the paper I linked.

All of this was in response to your comment earlier:

"There is no such thing as a world model, and you don't have one of them."

There is such a thing as a world model in humans, and we all have them otherwise we could not think about or conceptualize or navigate the world. Then you have discussed how to define or construct a useful model or the limitations of a model but that is not relevant to the original point and I'm already aware of that.

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