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Sadly a lot of foundational ML research works for single-label image classification and not much else. ImageNet is a niche problem and way too much ML research

Sadly a lot of foundational ML research works for single-label image classification and not much else. ImageNet is a niche problem and way too much ML research is over-indexed on it. If you can make your problem look like ImageNet, you're going to do OK, but if not you effectively need to re-invent the wheel...

p1esk 4y ago
What you wrote was true until 4-5 years ago. These days almost all foundational ML research is about generative models.
- sdenton4 4y ago
  It's still problematic. The HEAR benchmark is a great eye-opener. They have basically three classes of audio tasks, and find very different models excel in each, with the best overall models being kinda-mindless ensembles of the ones that do well on particular problems. So if you've got something that works well for text... it'll take a couple years and maybe an entire new branch of research (diffusion!) to work well for image generation. I have no idea what generative models for chemistry will look like, but will happily bet that it takes some significant specialized effort.
  - p1esk 4y ago
    I see two things currently happening in ML: 1. The era of ML benchmarks is ending. New models have to be and will be evaluated the same way human experts are evaluated. 2. Foundational models are becoming multi modal. There will be no separation of text and image generation. Sure, different methods will be used for each, but the learned representations of visual and textual objects in models like Stable Diffusion already live in the same conceptual space. I don’t think there will be specialized generative models for chemistry two years from now. There will be GPT-5 (and similar competitors) which will be used to perform all kinds of research, including chemistry.
    - sdenton4 4y ago
      I think this is a bit over confident, though... For example, AlphaFold just fundamentally isn't a language model, but is fundamentally useful. We'll still need these models that Do Stuff in many areas, and that will still involve benchmarks... Even if we're able to ask GPT-N+1 to design the next version of the model for us.

p1esk 4y ago

What you wrote was true until 4-5 years ago. These days almost all foundational ML research is about generative models.

sdenton4 4y ago
It's still problematic. The HEAR benchmark is a great eye-opener. They have basically three classes of audio tasks, and find very different models excel in each, with the best overall models being kinda-mindless ensembles of the ones that do well on particular problems. So if you've got something that works well for text... it'll take a couple years and maybe an entire new branch of research (diffusion!) to work well for image generation. I have no idea what generative models for chemistry will look like, but will happily bet that it takes some significant specialized effort.
- p1esk 4y ago
  I see two things currently happening in ML: 1. The era of ML benchmarks is ending. New models have to be and will be evaluated the same way human experts are evaluated. 2. Foundational models are becoming multi modal. There will be no separation of text and image generation. Sure, different methods will be used for each, but the learned representations of visual and textual objects in models like Stable Diffusion already live in the same conceptual space. I don’t think there will be specialized generative models for chemistry two years from now. There will be GPT-5 (and similar competitors) which will be used to perform all kinds of research, including chemistry.
  - sdenton4 4y ago
    I think this is a bit over confident, though... For example, AlphaFold just fundamentally isn't a language model, but is fundamentally useful. We'll still need these models that Do Stuff in many areas, and that will still involve benchmarks... Even if we're able to ask GPT-N+1 to design the next version of the model for us.