Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently. These differences can be inherent to the inference task or present for other reasons: sensors in a sensor network may be placed far apart, affecting their individual measurements. Conversely, it is computationally advantageous to split Bayesian inference tasks across subsets of data, but data need not be identically distributed across subsets. One principled way to fuse probability distributions is via the lens of optimal transport: the Wasserstein barycenter is a single distribution that summarizes a collection of input measures while respecting their geometry. However, computing the barycenter scales poorly and requires discretization of all input distributions and the barycenter itself. Improving on this situation, we present a scalable, communication-efficient, parallel algorithm for computing the Wasserstein barycenter of arbitrary distributions. Our algorithm can operate directly on continuous input distributions and is optimized for streaming data. Our method is even robust to nonstationary input distributions and produces a barycenter estimate that tracks the input measures over time. The algorithm is semi-discrete, needing to discretize only the barycenter estimate. To the best of our knowledge, we also provide the first bounds on the quality of the approximate barycenter as the discretization becomes finer. Finally, we demonstrate the practical effectiveness of our method, both in tracking moving distributions on a sphere, as well as in a large-scale Bayesian inference task.

The MPs behind this independent review certainly seem to think so. Culture Secretary Karen Bradley suggested AI has "the potential to improve our everyday lives", while Business Secretary Greg Clark praised the "huge social and economic benefits its use can bring". More than that: they want to make Britain the world leader in AI and add £630bn to the UK economy. With UK productivity having remained largely stagnant since the 2008 financial crisis, we need to start innovating in areas like AI to create bold new ways of working. But to make this work we need more than government reports.

WASHINGTON (AFP) - Police in the US state of Delaware are poised to deploy "smart" cameras in cruisers to help authorities detect a vehicle carrying a fugitive, missing child or straying senior. The video feeds will be analyzed using artificial intelligence to identify vehicles by license plate or other features and "give an extra set of eyes" to officers on patrol, says David Hinojosa of Coban Technologies, the company providing the equipment. "We are helping officers keep their focus on their jobs," said Hinojosa, who touts the new technology as a "dashcam on steroids." The program is part of a growing trend to use vision-based AI to thwart crime and improve public safety, a trend which has stirred concerns among privacy and civil liberties activists who fear the technology could lead to secret "profiling" and misuse of data. US-based startup Deep Science is using the same technology to help retail stores detect in real time if an armed robbery is in progress, by identifying guns or masked assailants.

At this year's Notting Hill Carnival, the Metropolitan Police used facial recognition technology for the first time. Paul Wiles, the biometrics commissioner, reported that it was a test to see how the technology performed in such a bustling scenario. In theory, police records of 20 million faces can be cross-referenced with other crime data to identify likely offenders. In fact, we are seeing an explosion in new tech across the public sphere. Until now the model has been somewhat conservative, digitising processes humans once did.

"U.S. forces will continue to use all authorized and appropriate measures to protect Americans and to disable terrorist threats," said the spokeswoman. The U.S. forces are working with Somalia's security forces and the 22,000-strong African Union force of soldiers from neighboring countries and they are "targeting terrorists, their training camps and safe havens throughout Somalia, the region and around the world," she said.

In a recent interview with us, Vijay D'Souza, Director of the Center for Enhanced Analytics at the US Government Accountability Office, noted that, 'Regardless of the goals, it's important to understand the quality of the data you have. The quality determines how much you can rely on the data to make good decisions.' As it stands, bad data is ruining companies' data initiatives. This is one of the primary reasons why just 25% of businesses are successfully using their data to optimize revenue, despite the tremendous resources being pumped into them. IBM estimates that bad data is costing organizations some $3.1 billion a year in the US alone, while in Experian's Data Quality survey, 83% of companies said their revenue is affected by inaccurate and incomplete customer or prospect data.

MOGADISHU, Somalia – U.S. forces say they have carried out three drone strikes within 24 hours in Somalia, stepping up their campaign against the Islamic extremist rebels of al-Shabab and the Islamic State group. The strikes by unmanned drones killed several extremist fighters, a spokeswoman for the U.S. military command in Africa told The Associated Press Sunday. With these three attacks, the U.S. has now carried out 26 attacks in Somalia against extremist targets in 2017, she said. The latest U.S. strikes were carried out in coordination with Somalia's government, she said. The first strike happened Saturday at approximately 4:30 p.m. local Somalia time and it killed one fighter for the extremists group, al-Shabab, said a U.S. Africa command statement.

Bias is how far removed a model's predictions are from correctness, while variance is the degree to which these predictions vary between model iterations.

Two open letters have been sent by Artificial Intelligence experts to the Australian Prime Minister Malcolm Turnbull, which was signed by 122 AI researchers, and to the Canadian Prime Minister Justin Trudeau, which had 216 signatures on it.

Roundup It's been an interesting fortnight, sorry, two weeks in AI. In addition to what we've already reported, we have news about HPE developing what looks like a neural network accelerator chip, TensorFlow updates, Google's effort to teach software to make software, and other bits and pieces. The next biz in line claiming to be working on developing a fast custom-designed chip supposedly for neural networks is HPE. It was first reported on our sister site, The Next Platform, this week, although there are scant details on how the hardware works, its specs or even if it can deal with stuff like deep learning. That's mainly because our colleagues got wind of the secret R&D effort before it was due to be made public, and the enterprise IT giant is keeping schtum for now. The mysterious chip's "dot product engine" (DPE) architecture is apparently geared toward carrying out matrix operations at speed, which is useful for executing AI algorithms quickly.