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I've been hearing about deep learning revolutionizing "everything" for the past 8 years. So, can someone name me any significant impact it made on figuring out

I've been hearing about deep learning revolutionizing "everything" for the past 8 years. So, can someone name me any significant impact it made on figuring out what to do about the recent epidemic? If not, I think it's worth reflecting on what value we get out of the technology that sucked up so much of our intellectual, financial and computational resources.

forgotmyhnacc 7y ago
I've been hearing about modern JavaScript development revolutionizing "everything" for the past 8 years. So, can someone name me any significant impact it made on figuring out what to do about the recent epidemic? If not, I think it's worth reflecting on what value we get out of the technology that sucked up so much of our intellectual, financial and computational resources.
- gambler 7y ago
  This is a nonsensical response on two levels. First, you're missing the obvious fact that the response to this pandemic is largely dictated by data processing. People are modeling the spread of the virus, trying to make predictions about the optimal policy, trying to figure out infection rates without full testing and evaluating promising drugs. All of those things relate to modeling and data analysis. Deep learning was touted to revolutionize exactly those two things. Second, web technologies are making an obvious impact by allowing (some, many) people to stay in touch, stay informed, keep working and buy stuff without exposing themselves to crowded places. This constitutes visible and significant positive impact.
malux85 7y ago
If you're listening to people who claim any one technology solves "everything" --- and believing them --- then you are the fool. There's no silver bullet. Deep Learning has made huge strides in noise reduction, image and video processing (self driving cars), lidar processing, medical imaging, etc, but it's not made any progress in knitting, tooth brushing, dog walking, or many other tasks ill suited for deep learning - so clearly not everything.
- yters 7y ago
  That's been the general vibe the popular tech press has been spouting for awhile. That NNs are somehow a major step towards AGI, which'll 'capture the lightcone of the future' to quote Sam Altman. At some point, we'll realize that AGI is the modern alchemy. Ironically, alchemy's penultimate goal was to construct a homoculus, which is essentially the goal of AGI. https://en.wikipedia.org/wiki/Homunculus#Alchemy https://en.wikipedia.org/wiki/Homunculus#Alchemy "The allegorical text suggests to the reader that the ultimate goal of alchemy is not chrysopoeia, but it is instead the artificial generation of humans."
  - malux85 7y ago
    > That's been the general vibe the popular tech press has been spouting for awhile This is such a vague generalisation, I cant do anything with it, except maybe how about "nullius in verba", on the word of noone, or "think for yourself"
  - criddell 7y ago
    Penultimate means the second last thing in a set. In the alphabet, 'y' is the penultimate letter.
    - yters 7y ago
      The last thing is 'profit'!
      - criddell 7y ago
        Ha! Makes sense then. :)
- alanbernstein 7y ago
  Your general point is valid, but I can't help pointing out that knitting may not belong in that second list as much as the others. Computational textile work is a niche, but fascinating, area of research. Knitting and crocheting are highly technical crafts, and there are opportunities to use modern computational approaches, including deep learning, in them. Deep Knitting: https://arxiv.org/pdf/1902.02752.pdf https://arxiv.org/pdf/1902.02752.pdf I'm not going to read this paper in detail, it may not be a great example of a successful deep learning application - but at least it's not an absurd idea. Maybe not surprising if you're aware of the place of the Jacquard loom in the history of computer science.
sacado2 7y ago
It's not revolutionizing everything in the field of AI. It's "only" good at prediction in domains where data is poorly structured, such as images, audio, video, text and some games (which is a lot to be fair, hence the publicity it got in the recent years). It's not that great on structured data or on small datasets, and is not really adapted to anything not related to raw, "black-boxed" prediction. I'm not sure what you'd expect that tool to do regarding the current epidemic.
- jonesn11 7y ago
  Not good on "small datasets", well in theory, but in practice transfer learning given a similar training distribution can take you quite far.

forgotmyhnacc 7y ago

I've been hearing about modern JavaScript development revolutionizing "everything" for the past 8 years. So, can someone name me any significant impact it made on figuring out what to do about the recent epidemic? If not, I think it's worth reflecting on what value we get out of the technology that sucked up so much of our intellectual, financial and computational resources.

gambler 7y ago
This is a nonsensical response on two levels. First, you're missing the obvious fact that the response to this pandemic is largely dictated by data processing. People are modeling the spread of the virus, trying to make predictions about the optimal policy, trying to figure out infection rates without full testing and evaluating promising drugs. All of those things relate to modeling and data analysis. Deep learning was touted to revolutionize exactly those two things. Second, web technologies are making an obvious impact by allowing (some, many) people to stay in touch, stay informed, keep working and buy stuff without exposing themselves to crowded places. This constitutes visible and significant positive impact.

malux85 7y ago

If you're listening to people who claim any one technology solves "everything" --- and believing them --- then you are the fool. There's no silver bullet. Deep Learning has made huge strides in noise reduction, image and video processing (self driving cars), lidar processing, medical imaging, etc, but it's not made any progress in knitting, tooth brushing, dog walking, or many other tasks ill suited for deep learning - so clearly not everything.

yters 7y ago
That's been the general vibe the popular tech press has been spouting for awhile. That NNs are somehow a major step towards AGI, which'll 'capture the lightcone of the future' to quote Sam Altman. At some point, we'll realize that AGI is the modern alchemy. Ironically, alchemy's penultimate goal was to construct a homoculus, which is essentially the goal of AGI. https://en.wikipedia.org/wiki/Homunculus#Alchemy https://en.wikipedia.org/wiki/Homunculus#Alchemy "The allegorical text suggests to the reader that the ultimate goal of alchemy is not chrysopoeia, but it is instead the artificial generation of humans."
- malux85 7y ago
  > That's been the general vibe the popular tech press has been spouting for awhile This is such a vague generalisation, I cant do anything with it, except maybe how about "nullius in verba", on the word of noone, or "think for yourself"
- criddell 7y ago
  Penultimate means the second last thing in a set. In the alphabet, 'y' is the penultimate letter.
  - yters 7y ago
    The last thing is 'profit'!
    - criddell 7y ago
      Ha! Makes sense then. :)
alanbernstein 7y ago
Your general point is valid, but I can't help pointing out that knitting may not belong in that second list as much as the others. Computational textile work is a niche, but fascinating, area of research. Knitting and crocheting are highly technical crafts, and there are opportunities to use modern computational approaches, including deep learning, in them. Deep Knitting: https://arxiv.org/pdf/1902.02752.pdf https://arxiv.org/pdf/1902.02752.pdf I'm not going to read this paper in detail, it may not be a great example of a successful deep learning application - but at least it's not an absurd idea. Maybe not surprising if you're aware of the place of the Jacquard loom in the history of computer science.

sacado2 7y ago

It's not revolutionizing everything in the field of AI. It's "only" good at prediction in domains where data is poorly structured, such as images, audio, video, text and some games (which is a lot to be fair, hence the publicity it got in the recent years). It's not that great on structured data or on small datasets, and is not really adapted to anything not related to raw, "black-boxed" prediction. I'm not sure what you'd expect that tool to do regarding the current epidemic.

jonesn11 7y ago
Not good on "small datasets", well in theory, but in practice transfer learning given a similar training distribution can take you quite far.