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It sounds like we (in this thread) are all on the same page about the future of AI-writ-large. Regarding good faith, the quotation pattern you used didn't ment

It sounds like we (in this thread) are all on the same page about the future of AI-writ-large. Regarding good faith, the quotation pattern you used didn't mention deep learning - you referred to either "the arms race" or "classifiers on large datasets" as fizzling out, and my reply resolved them to "AI" as a spanning term, since that was the context of the original post you quoted. For what it's worth, I would have written the same response about deep learning specifically for the same reasons you point out later in the thread that AI will remain useful. The specific profile of opportunities opened by DL is finding plenty of valuable homes in corporate processes where other kinds of automation fill in the gaps between it and AI. At this point, I'm not sure whether you disagree with that, or just think that some hype could afford to fade (no argument!). Since you are a grad researcher, I'll throw in some of my context. I did my PhD focused on probabilistic graphical models, back when it was easy to ignore NNs (and when we expected to hit some of the same perceptual milestones). As a grad student, a big part of your job is to filter fads and find ideas that will stay true. Because of that, I was slower than I could have been to recognize the feedback loops in what is "true" about applied AI. Deep learning's initial fit for some architectures and problems has attracted attention that made it much cheaper and easy to experiment, and therefore useful for more and more - even gobbling up adjacent techniques and giving them new names (over any manner of academic protest). That feedback loop isn't unbounded, but I guess I'm just sharing the perspective that hype, while annoying, isn't something that even a grad student can afford to disdain.

YeGoblynQueenne 8y ago
>> At this point, I'm not sure whether you disagree with that, or just think that some hype could afford to fade (no argument!). I think I agree. I believe the hype is primarily driven by industry looking for applications rather than researchers looking for, er, well, understanding, hopefully. >> Because of that, I was slower than I could have been to recognize the feedback loops in what is "true" about applied AI. Deep learning's initial fit for some architectures and problems has attracted attention that made it much cheaper and easy to experiment, and therefore useful for more and more - even gobbling up adjacent techniques and giving them new names (over any manner of academic protest). That feedback loop isn't unbounded, but I guess I'm just sharing the perspective that hype, while annoying, isn't something that even a grad student can afford to disdain. You're right of course. Deep learning has earned its due respect I think and although I expect the field to look for something new eventually, I'm guessing that CNNs and LSTMs in particular will remain as established techniques, probably incorporated into other work. I mean, until some new technique comes up that can match CNNs' accuracy but with much improved sample efficiency and generalisation, CNNs are going to remain the go-to method for image classification. Like, I don't disdain deep learning, I did some work with LSTMs for my Master's and I'm thinking of using CNNs for some vision stuff after my PhD (my subject woulnd't really fit). It's just, there's so many people publishing on deep learning right now that I don't see the point of joining in myself.
- evrydayhustling 8y ago
  Looks like you are doing some cool stuff based on learning of structures! And indeed, the folks who are now celebrated for DNNs were doing the less popular thing for 10-20y prior :) Best of luck on your research.
  - YeGoblynQueenne 8y ago
    Ah, I've been telling myself that, yes. Cheers :)

YeGoblynQueenne 8y ago

>> At this point, I'm not sure whether you disagree with that, or just think that some hype could afford to fade (no argument!). I think I agree. I believe the hype is primarily driven by industry looking for applications rather than researchers looking for, er, well, understanding, hopefully. >> Because of that, I was slower than I could have been to recognize the feedback loops in what is "true" about applied AI. Deep learning's initial fit for some architectures and problems has attracted attention that made it much cheaper and easy to experiment, and therefore useful for more and more - even gobbling up adjacent techniques and giving them new names (over any manner of academic protest). That feedback loop isn't unbounded, but I guess I'm just sharing the perspective that hype, while annoying, isn't something that even a grad student can afford to disdain. You're right of course. Deep learning has earned its due respect I think and although I expect the field to look for something new eventually, I'm guessing that CNNs and LSTMs in particular will remain as established techniques, probably incorporated into other work. I mean, until some new technique comes up that can match CNNs' accuracy but with much improved sample efficiency and generalisation, CNNs are going to remain the go-to method for image classification. Like, I don't disdain deep learning, I did some work with LSTMs for my Master's and I'm thinking of using CNNs for some vision stuff after my PhD (my subject woulnd't really fit). It's just, there's so many people publishing on deep learning right now that I don't see the point of joining in myself.

evrydayhustling 8y ago
Looks like you are doing some cool stuff based on learning of structures! And indeed, the folks who are now celebrated for DNNs were doing the less popular thing for 10-20y prior :) Best of luck on your research.
- YeGoblynQueenne 8y ago
  Ah, I've been telling myself that, yes. Cheers :)