For predictive analytics, that doesn’t go so far as out-of-the-box thinking but does go to identifying and accepting unusual patterns and outcomes. That’s hard, because pattern-based “intelligence”—from what search result to display to what route take to what moves to make in chess—is based on the assumption that the majority patterns and paths are the best ones. Otherwise, people wouldn’t use them so much.
Most assistive systems use current conditions to steer you to a proven path. Predictive systems combine current and derivable future conditions using all sorts of probablistic mathematics. But those are the easy predictions. The ones that really matter are the ones that are hard to see, usually for a handful of reasons: the context is too complex for most people to get their heads around, or the calculated path is an outlier and thus rejected as such—by the algorithm or the user.
As you can see, there’s a lot to be done, so take the gee-whiz future we see in the popular press and at technology conferences with a big grain of salt. The future will come, but slowly and unevenly.
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