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Jeff Dean on Large-Scale Deep Learning at Google
- yeukhon 11y agoNice. Forbbiden. Did we manage to crash the site? highscalability.com supposed to be pretty high-volume site.
- hartator 11y agoIt seems to work fine for me, a link to the actual talk on YouTube: https://www.youtube.com/watch?v=QSaZGT4-6EY https://www.youtube.com/watch?v=QSaZGT4-6EY Jeff Dean - Chuck Norris for us nerds - fact as a bonus: "The rate at which Jeff Dean produces code jumped by a factor of 40 in late 2000 when he upgraded his keyboard to USB2.0."
- milesward 11y agoIf you like this talk, come see him talk about what's even beyond that at GCP Next: https://cloudplatformonline.com/NEXT2016.html https://cloudplatformonline.com/NEXT2016.html Disclaimer: I will be there freaking out because I work at Google on Cloud and Jeff Dean is rad.
- return0 11y agoTangentially, watching the pace of papers coming out in machine learning is insane. It's so fast, people may literally cite powerpoint slides when the paper doesnt exist yet. The culture of openness seems to have fostered this insane pace. Contrasting that with the reclusive culture of life sciences explains why there is slow progress there.
- hackuser 11y agoIf someone with technical expertise wanted to keep up on this field, but it wasn't their profession - i.e., they don't need to know every detail and don't have time to read a lot - what would be a good source?
- p1esk 11y agoFollow Yann Lecun's posts on Facebook.
- jasonwatkinspdx 11y agoI think what you're saying is broadly true, but I'd moderate it a bit by pointing out that reproducing and extending research in software is a heck of a lot simpler and faster than working in topic areas that require physical experiments and processes.
- snowwrestler 11y agoArent's the systems being studied in life sciences much more complex than in machine learning? Consider the problem of protein folding, which has taken up many a processor cycle over the past decade or so. And that's just a tiny sliver of life sciences.
- p1esk 11y agoML and life sciences operate on different abstraction levels. It's like asking which is more complex: quantum mechanics or algebraic topology.
- giardini 11y agoFrom the article "...it seems like an excellent time to gloss Jeff’s talk..." "gloss" a talk? WTF?
- npalli 11y agoTo gloss is to annotate some text (or talk)[1], the word glossary comes from that. That meaning is overshadowed by the more modern association with shininess but the annotation meaning seems appropriate here. [1] https://en.wikipedia.org/wiki/Gloss_(annotation) https://en.wikipedia.org/wiki/Gloss_(annotation)
- goc 11y agoI am very interested in AI that can teach itself(sounds too great). Where can I learn up about such AI(related concepts and the whole 9 yards) to start reading papers in the field? I am just looking for comprehensive sources(preferably textbooks).
- sounds 11y agoI wish I had more than one upvote for this article. Read the article. If you have the time, just watch the video.
- return0 11y agoHe gave a similar talk at stanford a few days later: https://www.youtube.com/watch?v=T7YkPWpwFD4 https://www.youtube.com/watch?v=T7YkPWpwFD4
- YeGoblynQueenne 11y ago>> If you’re not considering how to use deep neural nets to solve your data understanding problems, you almost certainly should be. This line is taken directly from the talk And this is exactly why Google's hype of their tech is getting dangerous for everyone else, who is not Google. Because they advocate, nay, they preach, that everyone should abandon what they're doing and do what Google tells them works. And, oh, look, we just released those nice, free tools you can use to do it like we do! Which is insane. Google is a corporate entity. It has financial interests. The purpose of its existence is to sell you its stuff, it doesn't give a dime if you'll solve your problems or not. This piece of advice is like Bayer, back in the day, selling its Aspirin as the cure of all ills: "If you're not considering how to take Aspirin to solve your health problems, you almost certainly should be".
- dekhn 11y agoAlthough Google is a corp and has financial interests, I think it's in Google's interest to share these ideas in workable form with the world. It can (and I hope it will) contribute a lot to improving a number of things that are wrong with the world. When I was an academic scientist in the mid 2000s, I ended up with more data than I could deal with, and none of the computing systems in academia at the time dealt well with that (they were tuned for HPC/supercomputers). The bigtable, mapreduce, and GFS papers were huge to me, because they provided a nicer framework for data processing. Although Google made those tools for Search and Ads (and profited greatly from them) they also published them, and Doug Cutting and others incorporated them into Hadoop. A similar thing is happening now, but Google got better at releasing their codes as open source, which reduces the time between publication of a good idea, and replication of that work by others outside the corp. (eventually, I went to google to get direct access to its infrastructure; built Exacycle, gave away an enormous amount of free computing time that cost Google rather than profiting it, the leadership loved it even though it cost money, and I even managed to get Googler to apply machine learning to academic problems I cared about). So I don't think Google solely acts in its own short term financial interests. Also, aspirin has turned out to be amazing at solving a wide range of health problems, so I think bayer was probably right (if not for the right reasons) on that one.
- unexistance 11y agoyou need to understand the data before it can be made to good 'use' https://news.ycombinator.com/item?id=11272473 https://news.ycombinator.com/item?id=11272473