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The Decade of Deep Learning
- Teodolfo 4y ago"The AlexNet paper is generally recognized as the paper that sparked the field of Deep Learning" Uh not really. That would have been "A fast learning algorithm for deep belief nets" in 2006. Also weird how this list completely ignores speech recognition. Deep learning's success in speech recognition predates AlexNet and motivated Google to create TPUs [1], and, more generally, invest in deep learning. [1] https://www.wired.com/video/watch/the-story-behind-google-s-super-chip-wired-bizcon https://www.wired.com/video/watch/the-story-behind-google-s-...
- hardha87 4y agoYou are mistaken. Ladleys papers detailing the explorative method and his figurative routines adopted by “A fast learning algorithm for deep belief nets” was the firecracker that started deep learning. Also anyone who references wired.com should be shown the door
- mdp2021 4y ago> anyone who references [Source] should be shown the door Well - (exportable input) -, we are past the age of editorial boards (but for The Economist, probably) and in the age of independent journalists lending efforts inconsistently to different publishers.
- IanCal 4y agoHonest question, which paper is this? The 2012 one? The 2006 Hinton paper was the trigger surely - that's what I used and was filled with things that worked very quickly on GPUs.
- deleted 4y ago[deleted]
- pmoriarty 4y agoShouldn't this argument be easy to settle with a simple citation search? Step 1 - Rank papers by number of citations Step 2 - Which of the most cited papers is the earliest?
- kgwgk 4y agoOne paper will be the most cited. One paper will be the earliest. Unless they are the same there is not _one_ answer, what you get is an efficient set (the earliest paper with at least X citations / the most cited paper as of D). And of course there is the problem of making the list of relevant papers in the first place.
- tartaglia 4y agoAs of a couple of years ago the main sentiment was: deep learning is neat but your problem could be solved by statistical learning techniques so check those out first. Does this still hold up?
- gallabytes 4y agolmao no. imagine trying to do text to image with anything other than deep learning. nothing else comes close.
- KeplerBoy 4y agoWe have artists for that. I heard they can compete with sota methods.
- sebzim4500 4y agoCompete on quality, certainly not on price or speed.
- gallabytes 4y agoOP was about classical statistical techniques. I'm pretty sure human artists are not logistic regression?
- hardha87 4y agoOfcourse it does. Kings paper “probabilistically statistics” and his method of folding squares is the motivation behind this original advice
- sdeep27 4y agoLink?
- BenoitP 4y agoSmall data is better handled by greedy methods. XGBoost is still the go to in tabular Kaggle challenges.
- namaria 4y agoAll of this "throwing GPUs at problems" approach has brought us an online casino, domination over transparent games (chess, go) and trivia spitting chatbots (Watson, ChatGPT) and not much more. It's brisk business for hardware makers, operators and engineers in general. Society keeps getting promised freedom, amazing new tech. And people keep using datacenters-as-a-service to drain speculative VC funds and government subsidies (depending on which side of the Atlantic they are located). The core promise of silver bullet tech that can defeat real world limitations is such an obvious ploy. Well guess what they are just hiding the entropy exhaust somehow. And we keep falling for it.
- karmasimida 4y agoChatGPT is the silver bullet, you can't convince me otherwise. If the end of deep learning is to achieve artificial intelligence, we had almost achieved it at this stage.
- kabes 4y agoDefine silver bullet. Because it doesn't really bring us closer to stuff like self driving cars.
- mdp2021 4y agoThere is an ironic possibility that the poster confused the expression "silver bullet" for something different from "fake solution placing hopes on shiny idols". (Probably - sorry, just analytically - by negating the expression "it is no silver bullet". Process that does not work, since it is the actual silver bullet that does nothing special.) Edit: although, reading better, the later poster reprised the expression, probably ironically, from the original poster. Sorry, I missed that in the original.
- losvedir 4y agoI don't quite understand your comment, but I think you have your definition of "silver bullet" backwards. It means a solution that works incredibly, almost magically, well.
- bblpeter 4y ago[flagged]
- ronnykylin 4y agoThree pillars of deep learning: training dataset, computing power, and machine learning models