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Google Maps currently provides data about traffic conditions, labeling congested routes in red and open ones in green. But those data would be much more accurate and timely if cars themselves acted as sensors.

Cars as traffic sensors

A new algorithm optimizes the dissemination of information about traffic and road conditions through networks of wirelessly connected cars.

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If the relationships between data can be thought of as lines connecting points — or “graphs” — then machine learning is a matter of inferring the lines from the points. MIT researchers have shown that graphs shaped like stars and chains establish, respectively, the worst- and best-case scenarios for computers doing pattern recognition.

Sizing samples

Many scientific disciplines use computers to infer patterns in data. But how much data is enough to ensure that the inferences are right?

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Explaining the relationships between observable data (blue dots) can involve complicated mathematics that correlates each data point with each of the others (blue lines). But a “hidden variable” that describes general properties of all the data points (green dot) can make the mathematics much simpler (green lines).

More is less

Complex computer models can involve thousands of variables. But paradoxically, adding more variables can sometimes make them easier to work with.

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