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> It used to be that, to teach a machine how to [X], the designer of the system had to understand how [X] worked. It does feel like we're rapidly losing this r

by bhaney 2y ago

> It used to be that, to teach a machine how to [X], the designer of the system had to understand how [X] worked. It does feel like we're rapidly losing this relationship in general. I think it's going to be a good thing overall for productivity and the advancement of mankind, but it definitely takes a lot of the humanity out of our collective accomplishments. I feel warm and fuzzy when a person goes on a quest to deeply understand a subject and then shares the fruits of their efforts with everyone else, but I don't feel like that when someone points at a subject and says "hey computer, become good at that" with similar end results.

userbinator 2y ago
I think it's going to be a good thing overall for productivity and the advancement of mankind, but it definitely takes a lot of the humanity out of our collective accomplishments. I think AI will only cause us to become stuck in another local maximum, since not understanding how something works can only lead to imitation at best, and not inspiration.
- vidarh 2y ago
  I'm not convinced, because I think there will be a drive to distill down models and constrain them, and try to train models with access to "premade blocks" of functionality we know should help. E.g. we know human voices can be produced well with formant synthesis because we know how the human vocal tract is shaped. So you can "give" a model a formant synth, and try to train smaller models outputting to it. I think there's going to be a whole lot of research possibilities in placing constraints and training smaller models, and even training ensembles of models constrained in how they're interacting and their relative sizes to try to "force" extraction of functionality. E.g. we have reasonable estimates at the lowest bitrate raw audio that produces passable voice. Now consider training two models A, B, where A => B => audio, and the "channel" between A and B is constrained to a small fraction of the bitrate that'd let A do all the work, and where the size of B is set at a level you've first struggled to get passable TTS output from. Try to squeeze the bitrate and/or the size of B down and see if you can get something to emerge where analysing what happens in B is doable.
- nuc1e0n 2y ago
  I've said this before on HN, but neural networks are not a tabula rasa. Their structure is created by people. With better domain understanding we can make better structures for AI models. It's not an either-or situation.
- T_MacThrowFace 2y ago
  isn't the ultimate point of AI though, that instead of the traditional situation where the machines are the tools of humans, we become the (optional) tools for the AI? The AGI will do the understanding for us, and we'll profit by getting what we want from the black box, like a dog receiving industrially manufactured treats it cannot comprehend from its owner.
  - deleted 2y ago
    [deleted]

by bhaney 2y ago

userbinator 2y ago

I think it's going to be a good thing overall for productivity and the advancement of mankind, but it definitely takes a lot of the humanity out of our collective accomplishments. I think AI will only cause us to become stuck in another local maximum, since not understanding how something works can only lead to imitation at best, and not inspiration.

vidarh 2y ago
I'm not convinced, because I think there will be a drive to distill down models and constrain them, and try to train models with access to "premade blocks" of functionality we know should help. E.g. we know human voices can be produced well with formant synthesis because we know how the human vocal tract is shaped. So you can "give" a model a formant synth, and try to train smaller models outputting to it. I think there's going to be a whole lot of research possibilities in placing constraints and training smaller models, and even training ensembles of models constrained in how they're interacting and their relative sizes to try to "force" extraction of functionality. E.g. we have reasonable estimates at the lowest bitrate raw audio that produces passable voice. Now consider training two models A, B, where A => B => audio, and the "channel" between A and B is constrained to a small fraction of the bitrate that'd let A do all the work, and where the size of B is set at a level you've first struggled to get passable TTS output from. Try to squeeze the bitrate and/or the size of B down and see if you can get something to emerge where analysing what happens in B is doable.
nuc1e0n 2y ago
I've said this before on HN, but neural networks are not a tabula rasa. Their structure is created by people. With better domain understanding we can make better structures for AI models. It's not an either-or situation.
T_MacThrowFace 2y ago
isn't the ultimate point of AI though, that instead of the traditional situation where the machines are the tools of humans, we become the (optional) tools for the AI? The AGI will do the understanding for us, and we'll profit by getting what we want from the black box, like a dog receiving industrially manufactured treats it cannot comprehend from its owner.
- deleted 2y ago
  [deleted]