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Mapped out: This screenshot shows the software generating a list of likely destinations. A hybrid car’s power train can adjust its power use accordingly, to save fuel under the driving conditions on that route, or to meet pollution or noise restrictions.

If a driver’s commute to work contains a section near the end with a lot of stop-start urban driving, the car might decide to avoid using its battery early on in order to handle that section most efficiently later. The car could conceivably tap into advice or rules from local governments, says McGee, and juggle its power sources so as not to use gas in pollution control zones.

Google launched its prediction service last year, but this week the company will open it up to anyone, and even guarantee its reliability for a fee to encourage its use in real products. Google’s engineers have also made the system capable of refining an existing model on the fly, says Travis Green, product manager for the service. “You can then stream in training data,” says Green. “That means as a person drives around to new places, you can add that in to the predictive model of their habits.” 

McGee and colleagues are currently driving the modified Escape around Ford’s Dearborn campus, testing, among other things, how quickly Google’s prediction service can learn a person’s habits. “It would probably learn my daily commute very quickly,” says McGee. “But patterns like that I play soccer once a week in the winter is more complex.”

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Credit: Ford

Tagged: Computing, Google, artificial intelligence, machine learning, cars, Ford, Predictive Modeling, predictive software

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