NYU Data Science newsletter – July 2, 2015

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Machine learning is the science of credit assignment. The machine learning community itself profits from proper credit assignment to its members. The inventor of an important method should get credit for inventing it. She may not always be the one who popularizes it. Then the popularizer should get credit for popularizing it (but not for inventing it). Relatively young research areas such as machine learning should adopt the honor code of mature fields such as mathematics: if you have a new theorem, but use a proof technique similar to somebody else’s, you must make this very clear. If you “re-invent” something that was already known, and only later become aware of this, you must at least make it clear later.

As a case in point, let me now comment on a recent article in Nature (2015) about “deep learning” in artificial neural networks (NNs), by LeCun & Bengio & Hinton (LBH for short), three CIFAR-funded collaborators who call themselves the “deep learning conspiracy” (e.g., LeCun, 2015).

Here at Civis, we’re very interested in studying how events shape public opinion. Survey research is one great way to track public discourse, but ambient data from social media provides an excellent look at the organic conversation around an issue or event. We’ve been using Twitter to track US discussion on access to healthcare for months now, and the overall conversation seemed to be shifting in a productive direction, toward jobs and implementation and away from anti-administration partisan bickering.

Last Thursday our Twitter feeds went into overdrive over the Supreme Court ruling on the Affordable Care Act (ACA), which upheld ACA authorized federal tax credits for Americans in all 50 states, not just those living in states with their own healthcare exchanges. It inspired us to dig in and see what this meant for the trends we’d observed.

Think search advertising is boring, and that it’s all about programmatic and display these days? Microsoft’s move today to dispose of its display business but keep search shows how powerful and unique search remains.

As announced today, Microsoft is ridding itself of its display ad sales business. That’s something it feels can be handled just as well by farming it out to AOL and AppNexus. But search is different. Search is important. Search is something that Microsoft has no plans to dispose of.

… five years and 43,000 percent growth later, things have gotten a bit more complicated. I’m happy to say that we’re also more sophisticated in the way we leverage data, and there’s now a lot more of it. The trick has been to manage scale in a way that brings together the magic of those early days with the growing needs of the present — a challenge that I know we aren’t alone in facing.

Engineers and designers who refined Healthcare.gov are on a mission to tame the government’s unruly digital presence.

Tesla Motors Inc. Chief Executive Officer Elon Musk has turned his worries about the rise of artificial intelligence into an international research program.

The Musk-backed Future of Life Institute said Wednesday it awarded about $7 million to teams around the world to look at the risks and opportunities posed by the development of AI. The grants were funded by part of Musk’s $10 million donation to the group in January and $1.2 million from the Open Philanthropy Project.

Dartmouth’s Neukom Institute for Computational Science is launching AI contests to see if a robot can write a novel, a poem, or dance music.

When Chris J.C. Burges came to Microsoft Research in 2000, he knew he wanted to work on machine learning projects that would have a real impact on users.

Burges definitely succeeded: He ended up being part of a team that created the basis for the ranking system that is still used in Microsoft’s Bing search engine today.

At next week’s International Conference on Machine Learning, Burges, a research manager and principal researcher in Microsoft Research’s Machine Learning Intelligence Group, and his co-authors will receive the Test of Time Award for the 2005 paper that showed how that system works.

Calling all students, faculty and university researchers! Are you currently working on data-driven research relating to education, government innovation, public health or climate change? Are you currently collaborating with a nonprofit / NGO on data analysis for a civic cause? Do you believe in the power of data to connect ideas for social impact?

Deadline for Submissions: Saturday, August 1