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What Comes After Deep Learning

This article is by Bill Vorhies.

Summary: We’re stuck.  There hasn’t been a major breakthrough in algorithms in the last year.  Here’s a survey of the leading contenders for that next major advancement.

What Comes After Deep LearningWe’re stuck.  Or at least we’re plateaued.  Can anyone remember the last time a year went by without a major notable advance in algorithms, chips, or data handling?  It was so unusual to go to the Strata San Jose conference a few weeks ago and see no new eye catching developments.

As I reported earlier, it seems we’ve hit maturity and now our major efforts are aimed at either making sure all our powerful new techniques work well together (converged platforms) or making a buck from those massive VC investments in same.

I’m not the only one who noticed.  Several attendees and exhibitors said very similar things to me.  And just the other day I had a note from a team of well-regarded researchers who had been evaluating the relative merits of different advanced analytic platforms, and concluding there weren’t any differences worth reporting.

Why and Where are We Stuck?

Where we are right now is actually not such a bad place.  Our advances over the last two or three years have all been in the realm of deep learning and reinforcement learning.  Deep learning has brought us terrific capabilities in processing speech, text, image, and video.  Add reinforcement learning and we get big advances in game play, autonomous vehicles, robotics and the like.

Read the full article.

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