Information theory

The D-Wave Two.

D-Wave

D-Wave's quantum optimizer has found a new customer in the form of a partnership created by Google, NASA, and a consortium of research universities. The group is forming what it's calling the Quantum Artificial Intelligence Lab and will locate the computer at NASA's Ames Research Center. Academics will get involved via the Universities Space Research Association.

Although the D-Wave Two isn't a true quantum computer in the sense the term is typically used, D-Wave's system uses quantum effects to solve computational problems in a way that can be faster than traditional computers. How much faster? We just covered some results that indicated a certain class of problems may be sped up by as much as 10,000 times. Those algorithms are typically used in what's termed machine learning. And machine learning gets mentioned several times in Google's announcement of the new hardware.

Machine learning is typically used to allow computers to classify features, like whether or not an e-mail is spam (to use Google's example) or whether or not an image contains a specific feature, like a cat. You simply feed a machine learning system enough known images with and without cats and it will identify features that are shared among the cat set. When you feed it unknown images, it can determine whether enough of those features are present and make an accurate guess as to whether there's a cat in it. In more serious applications machine learning has been used to identify patterns of brain activity that are associated with different visual inputs, like viewing different letters.

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Google and NASA have teamed up to launch a new laboratory focused on advancing machine learning. The Quantum Artificial Intelligence Lab — hosted at NASA's Ames Research Center in California — will contain a quantum supercomputer that will be used by researchers from the Universities Space Research Association (USRA) and all over the world to pioneer breakthroughs in artificial intelligence.

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Hi, I'm working on the National Vulnerability Database (NVD). I want to categorise the vulnerable software by category. I already have the categories and a good training set to feed into a machine learning algorithm.

The original idea was to use the description of the vulnerability in NVD to categorise the software, but this won't obviously work (because it doesn't describe the software).

Then we thought to download the first paragraph of the Wikipedia entry for that software. This works only 10% of the time, as many entries do not match any page. This is an example of a page that cannot load Further manual google queries seem to identify that software as a VOIP server. In some other cases, e.g. for the software Swift, the returned page is definitely not related to the software, and in the disambiguation page#Software_and_information_technology) it is not even clear which entry should be the one of interest.

Do you have suggestions to mitigate this problem? More reliable software-related databases other than wikipedia? Better ways to query the dataset instead of feeding the bare software name provided by NVD (e.g. up-ux_v, vendor:Nec)? Ways to include the vendor in the query, so to make the results more reliable?

Ever faced a problem like that?

Thanks!

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Hello, /r/compsci, I come to you today for advice on what I should do with my college career.

I am currently a sophomore at Purdue University majoring in computer science. I'm looking at focusing on graphics, but only due to lack of interesting alternatives. I am also minoring in theatrical design.

After Purdue, my goal is to attend Carnegie Mellon and get a Masters in HCI or Entertainment Technology. Beyond that, I may pursue a doctorate or go straight into the work force.

Professionally, I want to work in game development as a gameplay programmer, focusing mainly on how players interact with the world and how to use player feedback (both mined and surveyed) to improve the way a player interacts with their world post-release.

So my question is this: should I stay the course at Purdue in CS despite a lack of HCI? Or should I transfer to Purdue's sister school, IUPUI and get a major in Informatics with a focus on HCI and minor in cognitive science or psychology?

My fear is that a heavy focus on things like software engineering and algorithms will reduce my chances of getting into a field with a heavy focus on human psychology.

Additionally, if I make it into CMU, it won't matter that my undergrad was completed at a less-prestigious school, but that worries me regardless.

So, what are your thoughts on what I should do?

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