first_imgWe’ve all heard about Big Data and Deep Learning, and while I happen to know some scientists whose data volumes are legitimately “Big,” I meet far more programmers whose working sets should be able to fit in RAM than those that need distributed streams. (Fun fact: the Large Synoptic Survey Telescope will generate 20TB of data per night and use around 150TFLOPs of computing power.)As for Deep Learning, it’s a broad term, but it often refers to artificial neural nets with many hidden layers. I think it’s fair to say that not many of us have been so frustrated with tweaking the kernels in our Support Vector Machines that we’ve decided to take advantage of post-back-propagation techniques for setting the input weights to our sigmoidal activation functions. I mean, I dunno, but I haven’t heard a lot of chatter about that on Twitter.Machine Learning has had a great decade, but “How could it improve your product in the next few release cycles?” seems to be a question that, if asked at all, would be be hard for most of us to answer. Better analysis of patterns of use and failure spring to mind, but ML shines, not when it answers a question once, but when it’s a runtime function.Reasoning about static data, even if initially difficult, is wasteful when repeated indefinitely. I once developed an expert system for identifying seabirds (just now, 20 years too late, I realized that I missed the opportunity to name it “Rete Tern”). It worked, but even when flushed with success I was plagued by the knowledge that my troublesome memory requirements could be greatly reduced by replacing the chaining between the now-known rules with a bunch of “if” statements. I’ve similarly worked on optimization programs that were essentially one-shot wonders to generate “the” answer and never provided any additional leverage. It seems clear that we’re living in a golden age of capabilities. Satya Nadella’s “Bold Ambition and Our Core” memo boasts that “the Cortana app on my Windows Phone merges data from highway sensors and my own calendar and simply reminds me to leave work to make it to my daughter’s recital on time.” Siri, it turns out, can understand the question “What planes are over me right now?” and can answer, using Wolfram Alpha for the back-end processing.Speaking of Wolfram, they’ve recently released Mathematica 10, a product that includes built-in machine learning facilities, including “highly automated functions like Predict and Classify.”Cloud computing is now perfectly legitimate, both in terms of programming models and service providers. IBM’s Watson has moved on from Jeopardy into industrial use and now has a developer program.Yet the other day I laughed at the suggestion of a “House, M.D.,”-style drama about a development team: “The read more

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