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At the scale/complexity GPT operates at, how is that different from "understanding"? It seems like you've just rephrased it but with more words.

by _dain_ 4y ago

majewsky 4y ago
Yeah, but then it goes back to GP's original argument. If relations between translated tokens are classified as "understanding", that would mean that translation AIs are already capable of understanding: > in which case you could just use machine translation as your example to show that some computational model is capable of understanding, and leave ChatGPT out of it.
- _dain_ 4y ago
  Show me a pre-GPT machine translation model that can do what I've described.
  - deleted 4y ago
    [deleted]
ivanbakel 4y ago
Because it's a purely statistical explanation that doesn't require understanding. Put differently, it's possible that GPT doesn't "understand" language itself as a concept, and instead tokens in the same language are just highly-correlated when it comes to prediction. When affecting weights between tokens, it wouldn't be surprising that those weights have effects across languages, much in the same way they work within languages - after all, it's all just probabilities to GPT. Google Translate works in a comparable way, and nobody suggests that it is sentient. Frankly, the argument that "GPT operates at a scale that means it must be sentient" is begging the question.
- _dain_ 4y ago
  No, you're the one who's begging the question. Why can't a "purely statistical" process have an understanding? If you a priori assume it can't, then nothing could ever persuade you GPT understood anything, no matter how it performed. And again, this magical word "just". "Just highly correlated", "just probabilities". Putting the word "just" in front of something doesn't mean you've explained it.
  - ivanbakel 4y ago
    >No, you're the one who's begging the question. Why can't a "purely statistical" process have an understanding? This is a related, but fundamentally different thing to the point I replied to in your original comment. You asked: > I don't see how it can [apply training across languages] unless it really has some kind of understanding. I provided a potential explanation that is in line with how we think GPT works, and which doesn't require it to have understanding. You may feel that GPT is complex enough that this process itself models understanding - but I disagree, and I think that's begging the question because it falls back on a fact (GPT is highly complex) that is independent of the above problem (how GPT applied training across languages.) I am not compelled by the translation example to believe beyond doubt that GPT actually models and understands abstract concepts. I don't think the fact that its training works across languages is any proof that it parses that training in an abstract way, or that it forms abstract links between the same ideas in different languages, or indeed that it has any notions of language at all.
    - _dain_ 4y ago
      What would convince you? Give an example of something that would show beyond a shadow of a doubt that it understands.
      - foldr 4y ago
        What would convince you that it didn’t?
  - esailija 4y ago
    > Why can't a "purely statistical" process have an understanding? Once you understand long division, you can do it on infinite numbers without ever having seen the specific numbers. You can get this full understanding just from a handful of examples, no need for terabytes of them. No matter how many examples of long division examples you fed to statistical model like GPT, there will always be infinite amount of numbers you can tell it where it will give the wrong answer*, unless you cheated and actually hard coded the understanding into the model. If it cannot understand long division just from few examples it cannot ever understand it. The very reason it needs ridiculous amounts of data is precisely because it cannot understand. If you think it understands you simply aren't trying very hard to confirm otherwise. * in a way that reveals there is no understanding of long division, obviously a human would also give wrong answer after being awake 100 hours writing numbers on paper
sdifnhiono 4y ago
The fact that something is hard to define does not mean it isn't meaningful. I cannot tell you what consciousness is, but I know that I have it and my sock does not. It is trivial to tie these machines up in knots, blatantly contradicting themselves from one sentence to the next. If that is understanding then it is an understanding utterly foreign to any human, and a form of understanding that looks a whole lot like bullshit.

At the scale/complexity GPT operates at, how is that different from "understanding"? It seems like you've just rephrased it but with more words.

by _dain_ 4y ago

At the scale/complexity GPT operates at, how is that different from "understanding"? It seems like you've just rephrased it but with more words.

majewsky 4y ago

Yeah, but then it goes back to GP's original argument. If relations between translated tokens are classified as "understanding", that would mean that translation AIs are already capable of understanding: > in which case you could just use machine translation as your example to show that some computational model is capable of understanding, and leave ChatGPT out of it.

_dain_ 4y ago
Show me a pre-GPT machine translation model that can do what I've described.
- deleted 4y ago
  [deleted]

ivanbakel 4y ago

Because it's a purely statistical explanation that doesn't require understanding. Put differently, it's possible that GPT doesn't "understand" language itself as a concept, and instead tokens in the same language are just highly-correlated when it comes to prediction. When affecting weights between tokens, it wouldn't be surprising that those weights have effects across languages, much in the same way they work within languages - after all, it's all just probabilities to GPT. Google Translate works in a comparable way, and nobody suggests that it is sentient. Frankly, the argument that "GPT operates at a scale that means it must be sentient" is begging the question.

_dain_ 4y ago
No, you're the one who's begging the question. Why can't a "purely statistical" process have an understanding? If you a priori assume it can't, then nothing could ever persuade you GPT understood anything, no matter how it performed. And again, this magical word "just". "Just highly correlated", "just probabilities". Putting the word "just" in front of something doesn't mean you've explained it.
- ivanbakel 4y ago
  >No, you're the one who's begging the question. Why can't a "purely statistical" process have an understanding? This is a related, but fundamentally different thing to the point I replied to in your original comment. You asked: > I don't see how it can [apply training across languages] unless it really has some kind of understanding. I provided a potential explanation that is in line with how we think GPT works, and which doesn't require it to have understanding. You may feel that GPT is complex enough that this process itself models understanding - but I disagree, and I think that's begging the question because it falls back on a fact (GPT is highly complex) that is independent of the above problem (how GPT applied training across languages.) I am not compelled by the translation example to believe beyond doubt that GPT actually models and understands abstract concepts. I don't think the fact that its training works across languages is any proof that it parses that training in an abstract way, or that it forms abstract links between the same ideas in different languages, or indeed that it has any notions of language at all.
  - _dain_ 4y ago
    What would convince you? Give an example of something that would show beyond a shadow of a doubt that it understands.
    - foldr 4y ago
      What would convince you that it didn’t?
- esailija 4y ago
  > Why can't a "purely statistical" process have an understanding? Once you understand long division, you can do it on infinite numbers without ever having seen the specific numbers. You can get this full understanding just from a handful of examples, no need for terabytes of them. No matter how many examples of long division examples you fed to statistical model like GPT, there will always be infinite amount of numbers you can tell it where it will give the wrong answer*, unless you cheated and actually hard coded the understanding into the model. If it cannot understand long division just from few examples it cannot ever understand it. The very reason it needs ridiculous amounts of data is precisely because it cannot understand. If you think it understands you simply aren't trying very hard to confirm otherwise. * in a way that reveals there is no understanding of long division, obviously a human would also give wrong answer after being awake 100 hours writing numbers on paper

sdifnhiono 4y ago

The fact that something is hard to define does not mean it isn't meaningful. I cannot tell you what consciousness is, but I know that I have it and my sock does not. It is trivial to tie these machines up in knots, blatantly contradicting themselves from one sentence to the next. If that is understanding then it is an understanding utterly foreign to any human, and a form of understanding that looks a whole lot like bullshit.