5 ms·

I love when machine learning is used to create genuinely new things* instead of trying to imitate human processes. Both "alien" and "human-like" directions are

I love when machine learning is used to create genuinely new things* instead of trying to imitate human processes. Both "alien" and "human-like" directions are related, but the human-like results aren't as mind-blowing because, well, we do it ourselves already. This reminds me of those controlnet images that represent different things on different levels, results that feel like magic. * Yes, the Aphex face (HN favorite) is mentioned in the paper along with a bunch of other interesting references. I'd argue that there is a difference here. It's the way in which audio and spectrogram are both representational while being encoded by the same information. The difference might be a bit subjective.

mrkramer 2y ago
>I love when machine learning is used to create genuinely new things* instead of trying to imitate human processes. Machine learning is based upon human neural system so it was directly inspired by humans and all use cases so far try to mimic human behaviour e.g. vision, speech, data and information synthesis, art generation etc.
- deleted 2y ago
  [deleted]
- colechristensen 2y ago
  The extent to which LLMs are comparable to how collections of neurons work is really quite limited.
  - mrkramer 2y ago
    All I meant is loosely based on animal/human neural networks.
  - mrkramer 2y ago
    I was referring to this: https://en.wikipedia.org/wiki/Neural_network_(machine_learning) https://en.wikipedia.org/wiki/Neural_network_(machine_learni...
- gessha 2y ago
  > Machine learning is based upon human neural system Very, very loosely if at all. There was a moment in computer vision history where researchers began to focus more on practical algorithmic improvements and mathematical optimization techniques rather than direct biological realism. This was around LeCun’s time in the 1990s.
wizzwizz4 2y ago
> It's the way in which audio and spectrogram are both representational while being encoded by the same information. They're not, really. If you've spent much time reading spectrograms, you can look at the images and see which part is the sound. Occasionally the picture and sound are "chosen" to line up (notably the birdsong, disguised as distant blurry flower petals – notably not the large flowers, which don't really sound like birdsong at all), but usually the sound appears as clear, visually-distinct banding (e.g. the kittens meowing), and the image shows up as very audible distortion (e.g. the tigers). Choosing sounds and images that line up properly might not be something a human has tried before, but I can already see how I'd do it, if I were better at drawing. It's like a multimedia ambigram: https://en.wikipedia.org/wiki/Ambigram https://en.wikipedia.org/wiki/Ambigram. The diffusion model is missing a lot of tricks: I could get similar quality by making a collage of clip art. Edit: just saw the third "painting of a farm" one. That one is actually quite good, the way it uses the solid space in the barns. The first "painting of a farm" has the right idea (if I may anthropomorphise the system), working the birdsong in as clouds and trees, but its execution is severely lacking.

mrkramer 2y ago

>I love when machine learning is used to create genuinely new things* instead of trying to imitate human processes. Machine learning is based upon human neural system so it was directly inspired by humans and all use cases so far try to mimic human behaviour e.g. vision, speech, data and information synthesis, art generation etc.

deleted 2y ago
[deleted]
colechristensen 2y ago
The extent to which LLMs are comparable to how collections of neurons work is really quite limited.
- mrkramer 2y ago
  All I meant is loosely based on animal/human neural networks.
- mrkramer 2y ago
  I was referring to this: https://en.wikipedia.org/wiki/Neural_network_(machine_learning) https://en.wikipedia.org/wiki/Neural_network_(machine_learni...
gessha 2y ago
> Machine learning is based upon human neural system Very, very loosely if at all. There was a moment in computer vision history where researchers began to focus more on practical algorithmic improvements and mathematical optimization techniques rather than direct biological realism. This was around LeCun’s time in the 1990s.

wizzwizz4 2y ago

> It's the way in which audio and spectrogram are both representational while being encoded by the same information. They're not, really. If you've spent much time reading spectrograms, you can look at the images and see which part is the sound. Occasionally the picture and sound are "chosen" to line up (notably the birdsong, disguised as distant blurry flower petals – notably not the large flowers, which don't really sound like birdsong at all), but usually the sound appears as clear, visually-distinct banding (e.g. the kittens meowing), and the image shows up as very audible distortion (e.g. the tigers). Choosing sounds and images that line up properly might not be something a human has tried before, but I can already see how I'd do it, if I were better at drawing. It's like a multimedia ambigram: https://en.wikipedia.org/wiki/Ambigram https://en.wikipedia.org/wiki/Ambigram. The diffusion model is missing a lot of tricks: I could get similar quality by making a collage of clip art. Edit: just saw the third "painting of a farm" one. That one is actually quite good, the way it uses the solid space in the barns. The first "painting of a farm" has the right idea (if I may anthropomorphise the system), working the birdsong in as clouds and trees, but its execution is severely lacking.