The No. 1 Predictor Of Career Success According To Network Science
Michael Simmons, Forbes, 2015/01/16

While there is merit to Michael Simmons's suggestion that size and openness of one's network is a predictor of success, I think it is vastly overstated to say that this is revealed to us by Network Science (as though it were some sort of oracle) and that it is " the blueprint for creating career success." Indeed, the major elements cited as factors in Steve Job's success - "tinkering with machinery with his father, dropping out of college and sitting in on a calligraphy class, exploring India and buddhism, living on an Apple orchard" - are more likely predictors of failure. It is more likely, in my estimation, that his career was started when he started hacking phones (this is known as phreaking). As I've often said: great wealth is prima facie evidence of criminality. That's not to say opening your network isn't a good idea - it is. But don't think that it will turn you into Steve Jobs. That's just magical thinking, and Forbes should (but apparently doesn't) know better than to publish it.

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Software updates, installations now require consent
Unattribted, CBC News, 2015/01/16

I will be curious to see how software companies adapt to this new Canadian law. Companies must gain explicit permission in order to update software, and in addition "clearly disclose to users if its software could collect personal information, interfere with the normal operation of a computer, alter settings or preferences or data on a computer or allow a third party to access a computer." This would have made the Sony rootkit illegal (though Sony could have paid the $10 million fine with loose change found in its couch). What I wonder is whether burying the information in an end-user license agreement (EULA) would be sufficient. You can read the full documentation here on the Canadian government web site.

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Public Sales Of Google Glass To End Later This Month
Sam Sanders, NPR, 2015/01/16

We can learn all kinds of lessons from the Google Glass public trial, which is now coming to an end. This article does a good job at articulating some of those lessons (and I have no doubt there are technical lessons behind the scenes as well). Google Glass (perhaps unexpectedly) created privacy issues (I know it sounds obvious in retrospect but I don`t recall any sort of widespread concern when the product launched). Also, it showed how important it is to (try to) frame the public discourse about a new product. And it illustrated the value of price point and users. "The thinking was that they'd generate a community of developers to develop applications for Glass,' he said. But the high price point of Glass kept the market too small."

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Toronto Deep Learning Demos
Yichuan Tang, Tianwei Liu, University of Toronto, 2015/01/16

'Deep Learning' is a form of machine learning that generates clusters or categorizations without the aid of a training set - the machine learns to recognize things by itself. This set of demonstrations from Toronto apply descriptions and captions to images. Most of the results are quite good, though you can fool it still with specific examples, like the Taj Mahal. Deep learning is important for a couple of reasons: it demonstrates that neural networks can learn abstractions without a priori knowledge, and it creates a set of applications that can be useful for e-learning analytics, such as resource classification for intelligent recommendation systems. The Toronto site has other resources that are equally applicable to e-learning. I've talked about Boltzmann machines in the past; Multimodal Deep Learning With Boltzmann Machines illustrates aspects of this. Also: Quantitative Structure-Activity/Property Relationship (QSAR/QSPR). And Multimodal Neural Language Models.

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ProjectCampus
Various authors, Website, 2015/01/16

Connecting applications is the new black. No doubt many people have been looking at sites like IFTTT and thinking about how they could emulate the same approach. So here we have ProjectCampus, which enables groups of students to form 'projects' within a course, where these projects become a part of the student's eportfolio, and which integrates with a set of applications including Blackboard, Moodle, Canvas, YouTibe, Dropbox, and more. Meanwhile, Workatoo is the same sort of application, but designed for the enterprise workspace.

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Why You Need To Leave Academia In 2015
Unattributed, Cheeky Scientist, 2015/01/16

This is the decision I made en passant maybe a decade and a half ago - around the time that I realized further investment in a PhD was counterproductive. Now my reasons were a bit different. But I knew at the time there was no compelling economic case for continuing; I had learned what I needed to learned and the actual piece of paper wasn't going to produce a lot of additional value. "The Fairy Tale Is Over. Academia is broken. The time to leave it is now. If you don’t leave, you will be poor, mistreated, and unhappy." The funny thing about this article is that it's actually trying to entice people to leave academia and move to an industry career (where there is actually a shortage of skilled professionals).

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