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We can solve that question in an intuitive way: if human input is not what is driving the output then it would be sufficient to present it with a fraction of th

by jacquesm 11mo ago

We can solve that question in an intuitive way: if human input is not what is driving the output then it would be sufficient to present it with a fraction of the current inputs, say everything up to 1970 and have it generate all of the input data from 1970 onwards as output. If that does not work then the moment you introduce AI you cap their capabilities unless humans continue to create original works to feed the AI. The conclusion - to me, at least - is that these pieces of software regurgitate their inputs, they are effectively whitewashing plagiarism, or, alternatively, their ability to generate new content is capped by some arbitrary limit relative to the inputs.

andrepd 11mo ago
Excellent observation.
bfffbgfdcb 11mo ago
[flagged]
- jacquesm 11mo ago
  I think my track record belies your very low value and frankly cowardly comment. If you have something to say at least do it under your real username instead of a throwaway.
andsoitis 11mo ago
I like your test. Should we also apply to specific humans? We all stand on the shoulders of giants and learn by looking at others’ solutions.
- jacquesm 11mo ago
  That's true. But if we take your implied rebuttal then current level AI would be able to learn from current AI as well as it would learn from humans, just like humans learn from other humans. But so far that does not seem to be the case, in fact, AI companies do everything they can to avoid eating their own tail. They'd love eating their own tail if it was worth it. To me that's proof positive they know their output is mangled inputs, they need that originality otherwise they will sooner or later drown in nonsense and noise. It's essentially a very complex game of Chinese whispers.
  - andsoitis 11mo ago
    I share that perspective.
  - handoflixue 11mo ago
    Equally, of course, all six year olds need to be trained by other six year olds; we must stop this crutch of using adult teachers
    - subscribed 11mo ago
      Beautiful, thank you.
- deleted 11mo ago
  [deleted]
measurablefunc 11mo ago
This is known as the data processing inequality. Non-invertible functions can not create more information than what is available in their inputs: https://blog.blackhc.net/2023/08/sdpi_fsvi/ https://blog.blackhc.net/2023/08/sdpi_fsvi/. Whatever arithmetic operations are involved in laundering the inputs by stripping original sources & references can not lead to novelty that wasn't already available in some combination of the inputs. Neural networks can at best uncover latent correlations that were already available in the inputs. Expecting anything more is basically just wishful thinking.
- xyzzy123 11mo ago
  Using this reasoning, would you argue that a new proof of a theorem adds no new information that was not present in the axioms, rules of inference and so on? If so, I'm not sure it's a useful framing. For novel writing, sure, I would not expect much truly interesting progress from LLMs without human input because fundamentally they are unable to have human experiences, and novels are a shadow or projection of that. But in math – and a lot of programming – the "world" is chiefly symbolic. The whole game is searching the space for new and useful arrangements. You don’t need to create new information in an information-theoretic sense for that. Even for the non-symbolic side (say diagnosing a network issue) of computing, AIs can interact with things almost as directly as we can by running commands so they are not fundamentally disadvantaged in terms of "closing the loop" with reality or conducting experiments.
  - measurablefunc 11mo ago
    Sound deductive rules of logic can not create novelty that exceeds the inherent limits of their foundational axiomatic assumptions. You can not expect novel results from neural networks that exceed the inherent information capacity of their training corpus & the inherent biases of the neural network (encoded by its architecture). So if the training corpus is semantically unsound & inconsistent then there is no reason to expect that it will produce logically sound & semantically coherent outputs (i.e. garbage inputs → garbage outputs).
    - 11mo ago
ninetyninenine 11mo ago
[dead]

andrepd 11mo ago

Excellent observation.

bfffbgfdcb 11mo ago

[flagged]

jacquesm 11mo ago
I think my track record belies your very low value and frankly cowardly comment. If you have something to say at least do it under your real username instead of a throwaway.

andsoitis 11mo ago

I like your test. Should we also apply to specific humans? We all stand on the shoulders of giants and learn by looking at others’ solutions.

jacquesm 11mo ago
That's true. But if we take your implied rebuttal then current level AI would be able to learn from current AI as well as it would learn from humans, just like humans learn from other humans. But so far that does not seem to be the case, in fact, AI companies do everything they can to avoid eating their own tail. They'd love eating their own tail if it was worth it. To me that's proof positive they know their output is mangled inputs, they need that originality otherwise they will sooner or later drown in nonsense and noise. It's essentially a very complex game of Chinese whispers.
- andsoitis 11mo ago
  I share that perspective.
- handoflixue 11mo ago
  Equally, of course, all six year olds need to be trained by other six year olds; we must stop this crutch of using adult teachers
  - subscribed 11mo ago
    Beautiful, thank you.
deleted 11mo ago
[deleted]

measurablefunc 11mo ago

This is known as the data processing inequality. Non-invertible functions can not create more information than what is available in their inputs: https://blog.blackhc.net/2023/08/sdpi_fsvi/ https://blog.blackhc.net/2023/08/sdpi_fsvi/. Whatever arithmetic operations are involved in laundering the inputs by stripping original sources & references can not lead to novelty that wasn't already available in some combination of the inputs. Neural networks can at best uncover latent correlations that were already available in the inputs. Expecting anything more is basically just wishful thinking.

xyzzy123 11mo ago
Using this reasoning, would you argue that a new proof of a theorem adds no new information that was not present in the axioms, rules of inference and so on? If so, I'm not sure it's a useful framing. For novel writing, sure, I would not expect much truly interesting progress from LLMs without human input because fundamentally they are unable to have human experiences, and novels are a shadow or projection of that. But in math – and a lot of programming – the "world" is chiefly symbolic. The whole game is searching the space for new and useful arrangements. You don’t need to create new information in an information-theoretic sense for that. Even for the non-symbolic side (say diagnosing a network issue) of computing, AIs can interact with things almost as directly as we can by running commands so they are not fundamentally disadvantaged in terms of "closing the loop" with reality or conducting experiments.
- measurablefunc 11mo ago
  Sound deductive rules of logic can not create novelty that exceeds the inherent limits of their foundational axiomatic assumptions. You can not expect novel results from neural networks that exceed the inherent information capacity of their training corpus & the inherent biases of the neural network (encoded by its architecture). So if the training corpus is semantically unsound & inconsistent then there is no reason to expect that it will produce logically sound & semantically coherent outputs (i.e. garbage inputs → garbage outputs).
  - 11mo ago

ninetyninenine 11mo ago

[dead]