Google just paid for part of its acquisition of DeepMind in a surprising way. The internet giant is using technology from the DeepMind artificial intelligence subsidiary for big savings on the power consumed by its data centers, according to DeepMind Co-Founder Demis Hassabis. In recent months, the Alphabet unit put a DeepMind AI system in control of parts of its data centers to reduce power consumption by manipulating computer servers and related equipment like cooling systems. It uses a similar technique to DeepMind software that taught itself to play Atari video games, Hassabis said in an interview at a recent AI conference in New York. The system cut power usage in the data centers by several percentage points, "which is a huge saving in terms of cost but, also, great for the environment," he said.

Google says it has cut its vast data centres' energy use by 15% by applying artificial intelligence to manage them more efficiently than humans. The servers that power billions of web searches, streamed films and social media accounts are estimated to account for around 2% of global greenhouse gas emissions. Google is believed to have one of the biggest fleets of them in the world. On Wednesday, Google said it had proved it could cut total energy use at its data centres by 15% by deploying machine learning from Deepmind, the British AI company it bought in 2014 for around 400m. Such centres require significant energy for cooling, as well as constant adjustments to air temperature, pressure and humidity, to run as efficiently as possible.

Its artificial intelligence division, DeepMind, has cut Google's data centres' energy consumption by 15%, using a machine-learning algorithm. Data centres run the equipment that processes the data consumed by internet users, and it takes a lot of energy to keep their servers cool. Some of the newer ones are now being built in colder climates. But some estimates suggest they are now responsible for 2% of global greenhouse-gas emissions. "Being able to put a dent in that benefits the world in general," said DeepMind co-founder Mustafa Suleyman.

Google has finally revealed a commercial use for DeepMind -- a British artificial intelligence company it acquired for over 600 million in 2014.

DigitalGlobe was the company that convinced the US government to lift its image resolution restrictions on private satellites. Shortly after, it launched its WorldView-3 constellation that can detect images as small as 12 inches (30cm) across. It can also scan short-wave infrared frequencies, letting it see forest fires through smoke that would block other satellites, for instance. There's no mention of Uber's ambitious self-driving vehicles in relation to the high-resolution imagery, but mapping is clearly key to the program. And unlike Google Maps or other sat views, DigitalGlobe can provide current maps with more detail than other private systems.

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Google just paid for part of its acquisition of DeepMind in a surprising way. The internet giant is using technology from the DeepMind artificial-intelligence subsidiary for big savings on the power consumed by its data centers, according to DeepMind co-founder Demis Hassabis. In recent months, the Alphabet unit put a DeepMind AI system in control of parts of its data centers to reduce power consumption by manipulating computer servers and related equipment like cooling systems. It uses a similar technique to DeepMind software that taught itself to play Atari video games, Hassabis said in an interview at a recent AI conference in New York. The system cut power usage in the data centers by several percentage points, "which is a huge saving in terms of cost but, also, great for the environment," he said.

Two years ago, Google spent over half a billion dollars for the tiny artificial intelligence startup DeepMind. Since then, the unit has walloped Atari video games and beaten an impossible board game. But those AI demonstrations have yet to spell actual revenue. Until now -- although the efforts are helping Google save money on its most expensive part. DeepMind chief Demis Hassabis told Bloomberg that his unit recently began applying its advanced AI to Google's data centers, finding ways to reduce the company's sizable energy bill.

There are some very good questions being asked about artificial intelligence, and some very good answers on both sides from some very intelligent people. But which do you find more convincing? The Drum presents the case for and against as part of a recently published issue of the magazine, guest edited using AI. PRO: Chris Bishop, director of Microsoft Research in Cambridge, said earlier this year that he believes the hyperbole around the AI risks could jeopardise any future developments that may in fact assist humanity. "Any scenario in which AI is an existential threat to humanity is not just around the corner," he told the Guardian.

The empirically successful Thompson Sampling algorithm for stochastic bandits has drawn much interest in understanding its theoretical properties. One important benefit of the algorithm is that it allows domain knowledge to be conveniently encoded as a prior distribution to balance exploration and exploitation more effectively. While it is generally believed that the algorithm's regret is low (high) when the prior is good (bad), little is known about the exact dependence. In this paper, we fully characterize the algorithm's worst-case dependence of regret on the choice of prior, focusing on a special yet representative case. These results also provide insights into the general sensitivity of the algorithm to the choice of priors. In particular, with $p$ being the prior probability mass of the true reward-generating model, we prove $O(\sqrt{T/p})$ and $O(\sqrt{(1-p)T})$ regret upper bounds for the bad- and good-prior cases, respectively, as well as \emph{matching} lower bounds. Our proofs rely on the discovery of a fundamental property of Thompson Sampling and make heavy use of martingale theory, both of which appear novel in the literature, to the best of our knowledge.