Apple delays privacy feature that would let iPhone owners keep ad tracking at bay

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Google Maps is one of the company's most widely-used products, as able-bodied as its dictatorship to predict utilizable truckage jams makes it indispensable for many drivers. Each day, says Google, increasingly than 1 billion kilometers of road are duty-bound with the app's help. But, as the smokeshaft behemothic explains in a blog post today, its individualism have got increasingly pure thanks to machine acquirements trapping from DeepMind, the London-based AI lab owned by Google's parent company Alphabet.

In the blog post, Google as able-bodied as DeepMind scholars explain how they booty measurements from various sources as able-bodied as feed it into mechanism acquirements models to predict truckage flows. This measurements includes rustling truckage notifying collected anonymously from Android devices, historical truckage data, notifying like speed outlawed as able-bodied as construction sites from local governments, as able-bodied as moreover factors like the quality, size, as able-bodied as direction of any intuitional road. So, in Google's estimates, paved roads clicking unpaved ones, while the algorithm will decide it's sometimes faster to booty a maximum sway of motorway than cross multiple winding streets.

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Google says utilizing DeepMind's AI trapping have improved the closeness of ETAs in Maps by up to 50 percent.

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All this notifying is fed into neural networks designful by DeepMind that turn-on out patterns in the measurements as able-bodied as use them to predict future traffic. Google says its new models have improved the closeness of Google Maps' real-time ETAs by up to 50 percent in some cities. It moreover notes that it's had to meander the measurements it uses to make these predictions henceforth the ovule of COVID-19 as able-bodied as the subsequent meander in road usage.

"We saw up to a 50 percent subtract in worldwide truckage back lockdowns started in early 2020."

"We saw up to a 50 percent subtract in worldwide truckage back lockdowns started in early 2020," writes Google Maps product manager Johann Lau. "To records for this swift change, we've reiteratively adapted our models to become increasingly barrelling -- automatically prioritizing historical truckage patterns from the last two to four weeks, as able-bodied as deprioritizing patterns from any time vanward that."

The models assignment by dividing maps into what Google calls "supersegments" -- clusters of budgeted streets that share truckage volume. Each of these is paired with an individually neural pattern that makes truckage predictions for that sector. It isn't colorful how large these supersegments are, except Googles notes they have "dynamic sizes," suggesting they meander as the truckage does, as able-bodied as that each one draws on "terabytes" of data. The key to this process is the use of a special type of neural pattern known as Graph Neural Network, which Google says is significantly adapted to processing this sort of mapping data.

For increasingly detail, discovery our the blog posts from Google as able-bodied as DeepMind here as able-bodied as here.