Live data from Hacker News

Jeff Dean on Large-Scale Deep Learning at Google

highscalability.com

31–32 of 32 posts

Re: Jeff Dean on Large-Scale Deep Learning at Google

#31
post #28

Earlier quoted context omitted.

>> So I don't think Google solely acts in its own short term financial interests. I think what your experience shows is that on the one hand individuals within Google (or any big corp) can and do align their own personal interest with that of the corp and on the other hand that the corp can benefit the community as long as it is making profit and serving its own purposes. Nothing surprising there. As to releasing its…

Did you really say that making TensorFlow open source is a trick to get people roped into Google technology? That doesn't make any sense to me. Another big point I think you missed is those individuals within Google influence the decisions about what gets open sourced. We have an entire team that facilitates taking Google-written code and opensourcing it.

OK, with the hindsight of a good night's sleep I admit that the bit about giving away TensorFlow does sound a bit tinfoil-hats on.

Let me rephrase that then: I can't possibly hope to know why Google is giving away free stuff. I can certainly know that they don't do it out of the kindness of their hearts though.

That said, I am indeed very concerned that Google is trying to shape, not only the market, but the science also, to suit its own interests. That could be really bad for everyone, including Google; if research stagnates, they too will find themselves unable to deliver on their big promises about ever speeding progress.

Re: Jeff Dean on Large-Scale Deep Learning at Google

#32
post #5

Tangentially, 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.

Arent'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.

ML and life sciences operate on different abstraction levels. It's like asking which is more complex: quantum mechanics or algebraic topology.
Post reply on HN