Untapped opportunities in AI

Editor's note: this post is part of an ongoing series exploring developments in artificial intelligence. First, collect huge amounts of training data -- probably more than anyone thought sensible or even possible a decade ago. Second, massage and preprocess that data so the key relationships it contains are easily accessible (the jargon here is "feature engineering"). Finally, feed the result into ludicrously high-performance, parallelized implementations of pretty standard machine-learning methods like logistic regression, deep neural networks, and k-means clustering (don't worry if those names don't mean anything to you -- the point is that they're widely available in high-quality open source packages). Google pioneered this formula, applying it to ad placement, machine translation, spam filtering, YouTube recommendations, and even the self-driving car -- creating billions of dollars of value in the process.