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I'd like to offer a more optimistic counter view. I think it's likely that deep learning has stumbled upon something deep and profound. And now we're at the po

by colah3 9y ago

I'd like to offer a more optimistic counter view. I think it's likely that deep learning has stumbled upon something deep and profound. And now we're at the point of struggling to make sense of it. Of course things are messy: we're knee deep in the business of trying to start to sort things out. In the optimistic view, the ideas we're grappling with -- ideas like feature visualization, attribution, etc -- might be the seeds of deep abstractions like calculus or information theory. (Of course, these early versions are messy! For example, early calculus was deeply criticized by figures like Berkeley, and took more than a century to put on firm footing via the introduction of limits.) Powerful, novel abstractions may not look like what you expect at first. I do think it's very reasonable of you to be skeptical, of course. Most attempts to craft new ways of thinking about hard problems don't pan out. But I think it's worth pushing really hard on them, because if they do they're very valuable. I feel like we have initial promising results -- give us a decade to see where they go! :) But we could also be totally barking up the wrong tree. :) > What's more- this only works with vision, where activations can sort of map to images. It's no use for, say, text, sound, or other types of data (despite what the article says). That's a reasonable concern. We didn't give any demonstrations of our methods outside vision in the article. I can say that we have done very early-stage prototypes that suggestion similar interfaces work in other domains. Of course, instead of images you get symbols in what ever domain you're working with -- such as audio or text.

cosmic_ape 9y ago
Could you point to some reasons why you'd think that there is anything deep and profound about deep learning? As far as the evidence I know goes, its very shallow as a scientific concept. All there is, is the idea of using convolutions for image feature extraction, which predates "deep learning" by decades. And there is little evidence that the nets do anything more than memorization, which is hardly profound too.
YeGoblynQueenne 9y ago
Thank you for taking the time to write a substantial reply! You might be right about deep learning having stumbled upon something deep and profound. Or it may just be the case of "big machine performs well at task that is hard for humans". Like you say, we will have to wait and see. It's just that we won't be seeing much, unless the field focuses real effort on the task of coming up with some kind of "calculus of (deep) learning". As things go right now, it might take more than ten years to see the progress you're hoping for. On a personal note, I should say that I do quite like your idea, in principle. But that's because you're proposing a grammar of design spaces; I think we should use grammars everywhere :P On the demonstration of your technique in other domains than vision- well, that would be really interesting to see. I watched a presentation by a gentleman called Willem Zuidema recently, whose work is on computational linguistics. His team had worked to interpret their deep learning models by visualising their hidden unit activations; he said that it was extremely painful and didn't scale well (he was talking at CoCoSym 2018, a workshop that featured much work on the prospects of combining deep learning with symbolic techniques, mainly for interpretation). If your method can work well in other domains it will definitely be useful to many people. It's still not the kind of theoretical result I'm hoping for, but it would be nice to see a principled way to extract symbolic representations from a continuous space - if that's what you're talking about. Anyway, I'll keep an eye out :)

by colah3 9y ago

cosmic_ape 9y ago

Could you point to some reasons why you'd think that there is anything deep and profound about deep learning? As far as the evidence I know goes, its very shallow as a scientific concept. All there is, is the idea of using convolutions for image feature extraction, which predates "deep learning" by decades. And there is little evidence that the nets do anything more than memorization, which is hardly profound too.

YeGoblynQueenne 9y ago

Thank you for taking the time to write a substantial reply! You might be right about deep learning having stumbled upon something deep and profound. Or it may just be the case of "big machine performs well at task that is hard for humans". Like you say, we will have to wait and see. It's just that we won't be seeing much, unless the field focuses real effort on the task of coming up with some kind of "calculus of (deep) learning". As things go right now, it might take more than ten years to see the progress you're hoping for. On a personal note, I should say that I do quite like your idea, in principle. But that's because you're proposing a grammar of design spaces; I think we should use grammars everywhere :P On the demonstration of your technique in other domains than vision- well, that would be really interesting to see. I watched a presentation by a gentleman called Willem Zuidema recently, whose work is on computational linguistics. His team had worked to interpret their deep learning models by visualising their hidden unit activations; he said that it was extremely painful and didn't scale well (he was talking at CoCoSym 2018, a workshop that featured much work on the prospects of combining deep learning with symbolic techniques, mainly for interpretation). If your method can work well in other domains it will definitely be useful to many people. It's still not the kind of theoretical result I'm hoping for, but it would be nice to see a principled way to extract symbolic representations from a continuous space - if that's what you're talking about. Anyway, I'll keep an eye out :)