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“Cold-start, back-propagation, hyper-parameters, sigmoid smoothing functions, batch normalization, yada, yada, yada.” I need an AI BS-Meter (bobg, Lab41.org)

“People worry that computers will get too smart and take over the world, but the real problem is that they’re too stupid and they’ve already taken over the world.” There is a blind spot in AI research (Kate Crawford & Ryan Calo, Nature)

“We're moving away from obscure mathematical derivatives to teaching surface area to 4th graders.” The Commoditization of Machine Learning (Niraj Pant, Partially Pondered)

“When we use a voice command to call our spouses, we reach them now. We aren’t connected to Amtrak or an angry ex.” Why Deep Learning Is Suddenly Changing Your Life (Roger Parloff, Fortune)

“At this stage we're sort of in the Wild West of self-driving cars' ethics.” Why Mercedes plans to let its self-driving cars kill pedestrians in dicey situations (Lindsay Dodgson, Business Insider)

"This started to feel like one of those 'fuck it, I'll do it myself' things. And so I did." Likes Out! Guerilla Dataset! (Ethan Rosenthal, Data Piques)

“Just one year ago, we pulled the hype hat over our eyes to some extent -- after all, [deep learning] was most useful in tagging images... We are, it is safe to say, at the real beginning of mainstream applications for deep learning.” The Next Wave of Deep Learning Applications (Nicole Hemsoth, The Next Platform)

“Winging it will work about as well in this arena [proprietary data sharing and licensing] as it does in, say, presidential debates.” How researchers lock up their study data with sharing fees (Ivan Oransky & Adam Marcus, Stat)

“Why spend countless hours writing a blog like this? What do I get out of it? What do I hope to accomplish? What is the purpose?” Why I Do This (Robert Kosara, EagerEyes)

‘“I’m comfortable saying that [command-line git's] interface is unnecessarily non-intuitive,” Huff says.’ Democratic databases: science on GitHub (Jeffrey Perkel, Nature)