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Ask HN: What are the hottest areas in computer science today?
I have some spare time and am looking to learn/immerse myself in a new topic. Machine learning is something I am interested in but before I jump in I am looking to get a list of topics and areas in the field of comp sci that are used to solve todays toughest problems. They can be possibly least understood and on bleeding edge. Thanks in advance.
- CyberFonic 13y agoI'm intrigued by your question. The toughest CS problems are challenging for the most experienced CS researchers. If you are one of them, then why do you ask? If you are not, then how do you plan to beat them at their game? The best snapshot would be to read the conference proceedings from SPLASH 2013. Then you could read the last 12 month's transactions and conference papers put out by the IEEE and ACM. These sources should give you a clear picture of what is leading edge research and what the open questions are. Good luck! and keep us on HN posted about your work.
- Dewie 13y agoMaybe they is asking about things like using some of the newest neural network techniques to implement image recognition; applying established research, not actually doing the research itself.
- tristanz 13y agoDARPA and others think probabilistic programming: http://probabilistic-programming.org/wiki/Home http://probabilistic-programming.org/wiki/Home You need to understand Bayesian statistics enough to understand this paper: http://www.stanford.edu/~ngoodman/papers/WSG-AIStats11.pdf http://www.stanford.edu/~ngoodman/papers/WSG-AIStats11.pdf
- espeed 13y agoSpark (http://spark.incubator.apache.org/ http://spark.incubator.apache.org/), which is part of the Berkeley Data Analytics Stack (BDAS - https://amplab.cs.berkeley.edu/software/ https://amplab.cs.berkeley.edu/software/), will likely emerge as Hadoop's successor (see https://news.ycombinator.com/item?id=6466222 https://news.ycombinator.com/item?id=6466222). Graph databases and graph analytics is an emerging space (see https://news.ycombinator.com/item?id=6786563 https://news.ycombinator.com/item?id=6786563).
- alok-g 13y agoIn the machine learning cheat sheet [1], one of the boxes is "tough luck". [1] http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html http://peekaboo-vision.blogspot.com/2013/01/machine-learning...
- tostitos1979 13y agoI thought the graphic was very cute! As an aside, I disagree with at least one of the paths to "tough luck" (in clustering). Trial and error with different #s of K helps. There is also something called x-means that might be useful.
- lsiebert 13y agoServer Rooms can get pretty toasty, lol. But seriously, I think Encryption, proof systems, and other crypto stuff continue to be important and interesting. I think part of this renewed interest is based on current events (The rise of crypto currencies, NSA leaks) but just because that has driven interest doesn't mean that their isn't interesting work being done... just the opposite.