

Ask HN: What are the hottest areas in computer science today? - quietthrow

I have some spare time and am looking to learn&#x2F;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.
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espeed
Spark
([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)).

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CyberFonic
I'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.

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Dewie
Maybe 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.

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tristanz
DARPA 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)

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lsiebert
Server 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.

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alok-g
In the machine learning cheat sheet [1], one of the boxes is "tough luck".

[1] [http://peekaboo-vision.blogspot.com/2013/01/machine-
learning...](http://peekaboo-vision.blogspot.com/2013/01/machine-learning-
cheat-sheet-for-scikit.html)

~~~
tostitos1979
I 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.

