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>The advent of chatGPT had millions of people on the internet trying to downplay the intelligence of chatGPT by continuously trying to re-emphasize the things i

>The advent of chatGPT had millions of people on the internet trying to downplay the intelligence of chatGPT by continuously trying to re-emphasize the things it gets wrong. Again the parroting. AI has been getting things wrong for decades. It's old news. The paradigm shift here is in the things it gets right. Because some of the things it gets right can not be attributed to anything else other than the concept of "understanding".

Jensson 4y ago
> Because some of the things it gets right can not be attributed to anything else other than the concept of "understanding". Or just large dataset. The new thing we got was parsing natural languages, then we did a markov chain based on that so that it outputs semantic correct follow-ups based on what people on the internet would likely do/say in similar situations, and you get this result. It is very easy to see that it works this way if you play around a bit with what it can and can't do, just identify what sort of conversation it used as a template and you can make it print nonsense by inputting values that wont work in that template. 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.
- random_cynic 4y ago
  What you described (with superfluous and ornamental technobabble) works perfectly well with a functional human with "understanding" as well, that's why people can be brainwashed or tricked into saying lot of stupid stuff. None of these proves that there is no understanding.
  - Jensson 4y ago
    > (with superfluous and ornamental technobabble) I know how the model works, there was no technobabble there. People who don't understand how it works might view it as magic, like how they view all technology they don't understand as magic, but that doesn't mean it is magic, we shouldn't listen to such crackpots.
  - deleted 4y ago
    [deleted]
  - mcluck 4y ago
    There was absolutely zero technobabble in that persons comment. Perhaps you need to ask yourself how well you understand the space
- worrycue 4y ago
  Arguing with AI (especially LLM) proponents is beginning to feel like arguing with a religious person ... it's almost easier to just let them believe in their god. Pointing out the failures of their favorite LLM to prove to them that it's not doing what they think it's doing just falls on deaf ears as they go digging for more "proof" that ChatGPT actually understands what it's saying.
  - HervalFreire 4y ago
    >Pointing out the failures of their favorite LLM to prove to them that it's not doing what they think it's doing just falls on deaf ears It's not falling on deaf ears. It's because it's stupidity to think the "failures" are proof. Should I point out all the failures in human intelligence? Humans fuck up all the time. Humans make stupid mistakes, assumptions, biases, errors in reasoning and leaps in logic all the time. According to your logic that's proof that humans don't understand anything.
    - worrycue 4y ago
      > According to your logic that's proof that humans don't understand anything. Well, it's reasonable to conclude that the people who constantly making "stupid mistakes, assumptions, and biases leaps in logic" don't actually know what they are talking about.
      - HervalFreire 4y ago
        Right but can you make the claim that they don't understand anything at all? That they're stochastic parrots? Because that's the direction where your own logic is taking you. I'll concede one thing, LLMs can't use vicious insults and subtle slights to cover up the lack of a good argument. That's a human specialty done by people who are too scared to admit that they're wrong.
        worrycue 4y ago
        > claim that they don't understand anything at all? They certainly don't understand enough to get things right without a few thousand examples already demonstrating the solution to a specific problem.
- HervalFreire 4y ago
  >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.

Jensson 4y ago

> Because some of the things it gets right can not be attributed to anything else other than the concept of "understanding". Or just large dataset. The new thing we got was parsing natural languages, then we did a markov chain based on that so that it outputs semantic correct follow-ups based on what people on the internet would likely do/say in similar situations, and you get this result. It is very easy to see that it works this way if you play around a bit with what it can and can't do, just identify what sort of conversation it used as a template and you can make it print nonsense by inputting values that wont work in that template. 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.

random_cynic 4y ago
What you described (with superfluous and ornamental technobabble) works perfectly well with a functional human with "understanding" as well, that's why people can be brainwashed or tricked into saying lot of stupid stuff. None of these proves that there is no understanding.
- Jensson 4y ago
  > (with superfluous and ornamental technobabble) I know how the model works, there was no technobabble there. People who don't understand how it works might view it as magic, like how they view all technology they don't understand as magic, but that doesn't mean it is magic, we shouldn't listen to such crackpots.
- deleted 4y ago
  [deleted]
- mcluck 4y ago
  There was absolutely zero technobabble in that persons comment. Perhaps you need to ask yourself how well you understand the space
worrycue 4y ago
Arguing with AI (especially LLM) proponents is beginning to feel like arguing with a religious person ... it's almost easier to just let them believe in their god. Pointing out the failures of their favorite LLM to prove to them that it's not doing what they think it's doing just falls on deaf ears as they go digging for more "proof" that ChatGPT actually understands what it's saying.
- HervalFreire 4y ago
  >Pointing out the failures of their favorite LLM to prove to them that it's not doing what they think it's doing just falls on deaf ears It's not falling on deaf ears. It's because it's stupidity to think the "failures" are proof. Should I point out all the failures in human intelligence? Humans fuck up all the time. Humans make stupid mistakes, assumptions, biases, errors in reasoning and leaps in logic all the time. According to your logic that's proof that humans don't understand anything.
  - worrycue 4y ago
    > According to your logic that's proof that humans don't understand anything. Well, it's reasonable to conclude that the people who constantly making "stupid mistakes, assumptions, and biases leaps in logic" don't actually know what they are talking about.
    - HervalFreire 4y ago
      Right but can you make the claim that they don't understand anything at all? That they're stochastic parrots? Because that's the direction where your own logic is taking you. I'll concede one thing, LLMs can't use vicious insults and subtle slights to cover up the lack of a good argument. That's a human specialty done by people who are too scared to admit that they're wrong.
      - worrycue 4y ago
        > claim that they don't understand anything at all? They certainly don't understand enough to get things right without a few thousand examples already demonstrating the solution to a specific problem.
HervalFreire 4y ago
>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.