4 ms·

>Edit: Also generating next state based on previous state is literally what the model does and is the definition of a Markov chain, Markov chains is a statistic

>Edit: Also generating next state based on previous state is literally what the model does and is the definition of a Markov chain, Markov chains is a statistical concept and not just a word chain. There's research (as in actual scientific papers) that shows that in LLMs, while the markov chain is the low level representation of what's going on, at a higher macro level there are other structures at play here. Emergent structures. This is of course similar to the emergence of a macro intelligence from the composition of simple summation and threshold machines (neurons) that the human brain is made out of. I can provide those papers if you so wish. >Or just large dataset. Even in a giant dataset it's easy to identify output that is impossible to exist in the training data. Simply do a google search for it. You will find can produce novel output for things that simply don't exist in the training data.

ghostpepper 4y ago
> I can provide those papers if you so wish. I'd like to see them but I don't have a background in AI or theoretical computer science. Can you post a few of them?
- HervalFreire 4y ago
  - GPT style language models try to build a model of the world: https://arxiv.org/abs/2210.13382 https://arxiv.org/abs/2210.13382 - GPT style language models end up internally implementing a mini "neural network training algorithm" (gradient descent fine-tuning for given examples): https://arxiv.org/abs/2212.10559 https://arxiv.org/abs/2212.10559
Jensson 4y ago
> at a higher macro level there are other structures at play here. Emergent structures. Yes, this is a neural net model, that is what such models do and have done for decades already. I'm not sure why this is relevant. Do you argue that stable diffusion is intelligent since it has emergent structures? Or an image recognition system is intelligent since it has emergent structures? Those are the same things. > Even in a giant dataset it's easy to identify output that is impossible to exist in the training data. Markov chains veer in different directions, they don't reproduce the data.
- HervalFreire 4y ago
  >Yes, this is a neural net model, that is what such models do and have done for decades already. I'm not sure why this is relevant. Do you argue that stable diffusion is intelligent since it has emergent structures? Or an image recognition system is intelligent since it has emergent structures? Those are the same things. No I am saying there are models for intelligence within the neural net that is explicitly different from a stochastic parrot for english vocabulary. For example in one instance they identified a structure in an LLM that logically models the rules and strategy for an actual board game. Obviously I'm not referring to papers on plain old "neural networks" that shit is old news. I'm referring to new research on LLMs. Again I can provide you with papers provided you want evidence that will flip your stubborn viewpoint on this. It just depends on if your bias is flexible enough to accept such a sudden deconstruction of your own stance.
  - Jensson 4y ago
    The fact that it adapts it state to fit the data isn't interesting in itself. An image recognition system forms a lot of macro structures around shapes or different logical parts of the image. Similarly an LLM forms a lot of macro structures around different kinds of text structures or words, including chess game series or song compositions or programming language tutorials. It is exactly the same kind of structures, just that some thinks those structures are sign of intelligence when they are applied to texts. Can such macro structures model intelligence in theory? Yes. But as we see in practice they aren't very logical. For example in this article we see that its markov chain didn't have enough programming language descriptions, so it veered into printing brace scoped code when it said the language had whitespace based scoping. Similarly in popular puzzles, just change the words around and it will start printing nonsense since it cares about what words you use and not what those words mean. Edit: Point is that existence of such structures doesn't make a model smart. You'd need to prove that these structures are smarter than before.
    - HervalFreire 4y ago
      >Can such macro structures model intelligence in theory? Yes. So you agree it's possible. >Yes. But as we see in practice they aren't very logical. For example in this article we see that its markov chain didn't have enough programming language descriptions, Well as humans we have many separate models for all different components an aspects of the world around us. Clearly LLMs form many models that in practice are not accurate. But that does not mean all the models are defective. The fact that it can write blog posts indicates that many of these models are remarkably accurate and that it understands the concept of a "blog post". There is literal evidence of chatGPT answering questions as if it has an accurate underlying model of "understanding" as well as actual identified structures within the nerual net itself. There is also evidence for your point of chatGPT clearly forming broken and inaccurate models by answering questions with wrong answers that don't make sense. What gets me is that even when there is Clear and abundant evidence for both cases some people have to make the claim that chatGPT doesn't understand anything. The accurate answer is that LLMs understand some things and it doesn't understand other things.
Xeamek 4y ago
Link the papers.
- HervalFreire 4y ago
  - GPT style language models try to build a model of the world: https://arxiv.org/abs/2210.13382 https://arxiv.org/abs/2210.13382 - GPT style language models end up internally implementing a mini "neural network training algorithm" (gradient descent fine-tuning for given examples): https://arxiv.org/abs/2212.10559 https://arxiv.org/abs/2212.10559

ghostpepper 4y ago

> I can provide those papers if you so wish. I'd like to see them but I don't have a background in AI or theoretical computer science. Can you post a few of them?

HervalFreire 4y ago
- GPT style language models try to build a model of the world: https://arxiv.org/abs/2210.13382 https://arxiv.org/abs/2210.13382 - GPT style language models end up internally implementing a mini "neural network training algorithm" (gradient descent fine-tuning for given examples): https://arxiv.org/abs/2212.10559 https://arxiv.org/abs/2212.10559

Jensson 4y ago

> at a higher macro level there are other structures at play here. Emergent structures. Yes, this is a neural net model, that is what such models do and have done for decades already. I'm not sure why this is relevant. Do you argue that stable diffusion is intelligent since it has emergent structures? Or an image recognition system is intelligent since it has emergent structures? Those are the same things. > Even in a giant dataset it's easy to identify output that is impossible to exist in the training data. Markov chains veer in different directions, they don't reproduce the data.

HervalFreire 4y ago
>Yes, this is a neural net model, that is what such models do and have done for decades already. I'm not sure why this is relevant. Do you argue that stable diffusion is intelligent since it has emergent structures? Or an image recognition system is intelligent since it has emergent structures? Those are the same things. No I am saying there are models for intelligence within the neural net that is explicitly different from a stochastic parrot for english vocabulary. For example in one instance they identified a structure in an LLM that logically models the rules and strategy for an actual board game. Obviously I'm not referring to papers on plain old "neural networks" that shit is old news. I'm referring to new research on LLMs. Again I can provide you with papers provided you want evidence that will flip your stubborn viewpoint on this. It just depends on if your bias is flexible enough to accept such a sudden deconstruction of your own stance.
- Jensson 4y ago
  The fact that it adapts it state to fit the data isn't interesting in itself. An image recognition system forms a lot of macro structures around shapes or different logical parts of the image. Similarly an LLM forms a lot of macro structures around different kinds of text structures or words, including chess game series or song compositions or programming language tutorials. It is exactly the same kind of structures, just that some thinks those structures are sign of intelligence when they are applied to texts. Can such macro structures model intelligence in theory? Yes. But as we see in practice they aren't very logical. For example in this article we see that its markov chain didn't have enough programming language descriptions, so it veered into printing brace scoped code when it said the language had whitespace based scoping. Similarly in popular puzzles, just change the words around and it will start printing nonsense since it cares about what words you use and not what those words mean. Edit: Point is that existence of such structures doesn't make a model smart. You'd need to prove that these structures are smarter than before.
  - HervalFreire 4y ago
    >Can such macro structures model intelligence in theory? Yes. So you agree it's possible. >Yes. But as we see in practice they aren't very logical. For example in this article we see that its markov chain didn't have enough programming language descriptions, Well as humans we have many separate models for all different components an aspects of the world around us. Clearly LLMs form many models that in practice are not accurate. But that does not mean all the models are defective. The fact that it can write blog posts indicates that many of these models are remarkably accurate and that it understands the concept of a "blog post". There is literal evidence of chatGPT answering questions as if it has an accurate underlying model of "understanding" as well as actual identified structures within the nerual net itself. There is also evidence for your point of chatGPT clearly forming broken and inaccurate models by answering questions with wrong answers that don't make sense. What gets me is that even when there is Clear and abundant evidence for both cases some people have to make the claim that chatGPT doesn't understand anything. The accurate answer is that LLMs understand some things and it doesn't understand other things.

Xeamek 4y ago

Link the papers.

HervalFreire 4y ago
- GPT style language models try to build a model of the world: https://arxiv.org/abs/2210.13382 https://arxiv.org/abs/2210.13382 - GPT style language models end up internally implementing a mini "neural network training algorithm" (gradient descent fine-tuning for given examples): https://arxiv.org/abs/2212.10559 https://arxiv.org/abs/2212.10559