One computer forecasting system predicted Trump’s victory—the one with the least human input.

From Next Gov.com (Nov. 10, 2016):

Donald Trump’s win surprised many around the world, but none more than the modelers and big league prognosticators who were calling for a likely Clinton victory. That outcome doesn’t mean data-driven forecasting died on Tuesday. In fact, the best performance went to an artificial intelligence able to crunch more data than its human rivals.

The takeaway? Forecasters need new ways to talk about the uncertainty of their models and need to expand beyond “polling data.” The Defense Department and the intelligence community, who have also grown fond of machine-aided prediction, would do well to heed that lesson.

There’s an important and overlooked distinction between a prediction (which suggests certainty) and a forecast, which acknowledges more than one possible outcome and then weighs multiple outcomes in terms of their relative probability. But humans, and particularly media types who report on polling information, like certainty. So forecasts are cast as predictions.

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It’s thus not surprising the big winner in the polling contest last night was not a human at all but an artificial intelligence named MogIA. Developed by Indian entrepreneur Sanjiv Rai, it takes in 20 million data points from a variety of open public websites such as YouTube, Google, Twitter and Facebook to uncover trends in “user engagement.”

Rai said his exclusive arrangement with CNBC prevents him from talking about how the system works or crunches the data points.

“While most algorithms suffer from programmers/developer’s biases, MoglA aims at learning from her environment, developing her own rules at the policy layer and develop expert systems without discarding any data,” Rai told CNBC reporter Arjun Kharpal. [read more]

If that AI program can predict the next presidential election it’s a darn good program.