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Language Models Represent Space and Time

arxiv.org

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171–180 of 192 posts

Re: Language Models Represent Space and Time

#171

Post by YeGoblynQueenne »

circuit10 wrote: post #170
YeGoblynQueenne wrote: post #160

Earlier quoted context omitted.

>> GPT-4 took months to train on a supercomputer and it generated a neural network of hundreds of gigabytes. What exactly was that supercomputer doing for several months and what exactly would the neural network represent if not a world model? I believe GPT-4 was trained on a server farm, not a single computer. In any case what it was doing all that time was going over and over the text in its gigantic training corpu…

> development of a world model, rather than, or in addition to, a model of the training corpus But the training corpus describes the world, so a model of the training corpus is a world model

A model of a training corpus is a model of a training corpus. Unless a system can read the corpus and understand what it says, it won't get any world model by modelling the corpus.

Re: Language Models Represent Space and Time

#172

Post by YeGoblynQueenne »

xcv123 wrote: post #162
YeGoblynQueenne wrote: post #160

Earlier quoted context omitted.

>> GPT-4 took months to train on a supercomputer and it generated a neural network of hundreds of gigabytes. What exactly was that supercomputer doing for several months and what exactly would the neural network represent if not a world model? I believe GPT-4 was trained on a server farm, not a single computer. In any case what it was doing all that time was going over and over the text in its gigantic training corpu…

> I believe GPT-4 was trained on a server farm, not a single computer. Yes a server farm of Nvidia A100 with supercomputing performance. > It is easy to see how this stuff works. .. you can train a Hidden Markov Model.. No. GPT is not a Markov model.

A HMM is one way to train a language model that optimises the conditional objective I note above. A transformer is another. Working with a HMM will give you an insight into how language modelling works, and it's something you can do easily and cheaply, unlike training a giant transformer architecture.

Re: Language Models Represent Space and Time

#173

Post by [deleted] »

xcv123 wrote: post #163
YeGoblynQueenne wrote: post #161

Earlier quoted context omitted.

The question then becomes where do the "semantic" relations in the network come from. Unless there is some way to extract the meaning of words with respect to the real-world concepts they refer to, from text, any relation between words that your model, models is not going to be semantic (i.e. it won't represent meaning). Like I say in another comment, this kind of explanation is "words all the way down", but it reall…

> but go ahead and try to understand what the real-world entities I renamed were. Obviously not. Insufficient data. Try that substitution exercise again with terabytes of data (Common Crawl dataset) then feed it into a neural network and see if it can find any patterns.

[deleted]

Re: Language Models Represent Space and Time

#174

Post by YeGoblynQueenne »

xcv123 wrote: post #163
YeGoblynQueenne wrote: post #161

Earlier quoted context omitted.

The question then becomes where do the "semantic" relations in the network come from. Unless there is some way to extract the meaning of words with respect to the real-world concepts they refer to, from text, any relation between words that your model, models is not going to be semantic (i.e. it won't represent meaning). Like I say in another comment, this kind of explanation is "words all the way down", but it reall…

> but go ahead and try to understand what the real-world entities I renamed were. Obviously not. Insufficient data. Try that substitution exercise again with terabytes of data (Common Crawl dataset) then feed it into a neural network and see if it can find any patterns.

This is not a matter of data quantity, but data quality. Text data doesn't have the information needed to learn a world model from it and so it doesn't matter how big a model you train, it will never learn the right representation (i.e. the right patterns).

Re: Language Models Represent Space and Time

#175

Post by nyrikki »

ImHereToVote wrote: post #152
nyrikki wrote: post #137

Earlier quoted context omitted.

Well as all you have as a response is an ad hominem you aren't the intended audience anyway. I would love to learn why I am wrong.

You are clearly a smart guy, you are also clearly on the far side of the spectrum. Since it took you a while to notice that I was yanking your chain. That's ok, I'm somewhat of a full spectrum spaz myself. The problem with smart people is that they have a very hard time to notice when their brain has sidestepped rational thought, and started to go into an emotional latent space. The reason for this is that your emoti…

The halting problem is part of asymptotic analysis on this case.

Just like with elementary limits you never reach it but you approach it.

The halting problem on what we typically call computers decidable but not in practical time lines.

The fact that a TM is not physically realizable doesn't change that claim.

Logical conjunctions are an example of something that is difficult in PAC learning and we know it is at least super polynomial but we don't if there are tractable forms like Schaefer's dicotomy allowing for linear time solutions for HORNSat as an example.

The tooling that works for asymptotic analysis on deterministic Turing machines does not transfer to biological nurons, because they simply aren't deterministic Turing machines.

Neurobiologists are fully aware of the limitations of modeling cortical neurons as deterministic systems.

While in pop science that difference may not be popular it is the general consensus of experts.

Your claims that cortical neurons are the same as a deterministic Turing machine is not the best accepted theory today.

In fact recent research says that qbits are a closer model to cortical neurons.

You can't blindly carry over the properties of deterministic Turing machines to qbits.

As non-deterministic Turing machine s are typically assigned to the special case of the type of NTM that defines the complexity class NP, I won't complicate the conversation with trying to explain the implications.

Re: Language Models Represent Space and Time

#176

Post by xcv123 »

YeGoblynQueenne wrote: post #171
circuit10 wrote: post #170

Earlier quoted context omitted.

> development of a world model, rather than, or in addition to, a model of the training corpus But the training corpus describes the world, so a model of the training corpus is a world model

A model of a training corpus is a model of a training corpus. Unless a system can read the corpus and understand what it says, it won't get any world model by modelling the corpus.

You have a subjective philosophical disagreement and are entitled to your opinion but it's not a technical argument.

It provides "a" world model and it's a useful model for its intended purpose. Not the "one true model" that is your your own personal life experience since birth.

Re: Language Models Represent Space and Time

#177

Post by xcv123 »

YeGoblynQueenne wrote: post #174
xcv123 wrote: post #163

Earlier quoted context omitted.

> but go ahead and try to understand what the real-world entities I renamed were. Obviously not. Insufficient data. Try that substitution exercise again with terabytes of data (Common Crawl dataset) then feed it into a neural network and see if it can find any patterns.

This is not a matter of data quantity, but data quality. Text data doesn't have the information needed to learn a world model from it and so it doesn't matter how big a model you train, it will never learn the right representation (i.e. the right patterns).

> Text data doesn't have the information needed to learn a world model from it

As I mentioned in another reply you have a philosophical disagreement and are missing the point.

It provides "a" model of the world. Not necessarily the "one true model" that is your own personal life experience of the world since birth. There is enough information to build "a" world model in all the text ever written by humans about the world. That is obvious.

So your argument is completely subjective and philosophical, not technical.

GPT now includes imagery in its training set BTW.

Re: Language Models Represent Space and Time

#178

Post by xcv123 »

YeGoblynQueenne wrote: post #172
xcv123 wrote: post #162

Earlier quoted context omitted.

> I believe GPT-4 was trained on a server farm, not a single computer. Yes a server farm of Nvidia A100 with supercomputing performance. > It is easy to see how this stuff works. .. you can train a Hidden Markov Model.. No. GPT is not a Markov model.

A HMM is one way to train a language model that optimises the conditional objective I note above. A transformer is another. Working with a HMM will give you an insight into how language modelling works, and it's something you can do easily and cheaply, unlike training a giant transformer architecture.

Those primitive obsolete models are not the subject of the article. I already have a degree in applied mathematics and computer science and know the basics of machine learning.

My questions to you were rhetorical. I wasn't asking you for academic guidance.

Re: Language Models Represent Space and Time

#179

Post by ImHereToVote »

nyrikki wrote: post #175
ImHereToVote wrote: post #152

Earlier quoted context omitted.

You are clearly a smart guy, you are also clearly on the far side of the spectrum. Since it took you a while to notice that I was yanking your chain. That's ok, I'm somewhat of a full spectrum spaz myself. The problem with smart people is that they have a very hard time to notice when their brain has sidestepped rational thought, and started to go into an emotional latent space. The reason for this is that your emoti…

The halting problem is part of asymptotic analysis on this case. Just like with elementary limits you never reach it but you approach it. The halting problem on what we typically call computers decidable but not in practical time lines. The fact that a TM is not physically realizable doesn't change that claim. Logical conjunctions are an example of something that is difficult in PAC learning and we know it is at leas…

Here are some real challenges in creating intelligent agents, but none of that has anything to do with the halting problem. The reason you latch on to the strawman that I belive neurons are touring machines is because you want to win. I on the other hand, don't care about winning. I want to be right. The best way to be right is to change your mind immediately as you notice the flaws in your thinking.

The halting problem, or some other theoretical and esoteric complexities of computer systems, have nothing to do with the fact that current LLMs are not simply stochastic parrots. This doesn't mean they are conscious. They can't be. Because they aren't even multi-modal yet for starters. But that has nothing to do with halts or qbits. I don't even know where that red herring came from. Lay of the Penrose juice. There is no evidence that mammalian brains are room temperature quantum annealers. Nor is there evidence We need to model complete biological neurons to do learning. What if it's just a question of scale? We don't know that it's not. If it is, we are in big trouble.

Your argument is akin to saying that Gödel's incompleteness theorems, are the reason you can't complete your maths homework. Yes, in the "limit", it's true. But practically, we both know that homework can be solved.

Re: Language Models Represent Space and Time

#180

Post by YeGoblynQueenne »

xcv123 wrote: post #177
YeGoblynQueenne wrote: post #174

Earlier quoted context omitted.

This is not a matter of data quantity, but data quality. Text data doesn't have the information needed to learn a world model from it and so it doesn't matter how big a model you train, it will never learn the right representation (i.e. the right patterns).

> Text data doesn't have the information needed to learn a world model from it As I mentioned in another reply you have a philosophical disagreement and are missing the point. It provides "a" model of the world. Not necessarily the "one true model" that is your own personal life experience of the world since birth. There is enough information to build "a" world model in all the text ever written by humans about the w…

I don't do philosophical arguments. But it's obvious this conversation not productive so there's no reason for me to insist.
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