In winter, snowfall can rapidly disrupt daily life and impact on Japan's economy. Snowplowing is a considerable annual expense, and methods for co-ordinating plowing activity are needed to ensure an efficient, cost-effective service. Clever computer models are needed to manage such complex activities, which involve many agents and interactions. Now, Satoshi Takahashi at the University of Electro-Communications, and Tokuro Matsuo at the Advanced Institute for Industrial Technology in Tokyo have devised a computational method that combines task allocation and scheduling of individual snow-plows to maximize efficiency. The researchers aimed to identify the best routes for multiple snow-plows to take without replicating route paths, meaning their computer model had to allocate and schedule tasks simultaneously.

Russian Defence Minister Sergei Shoigu announced Friday that Moscow will deploy a range of missile systems on the Kuril islands, claimed by Japan, as part of its military build-up in the far-eastern region, Agence France-Presse (AFP) reported. The islands have been a reason of tense relations between Moscow and Tokyo. "The planned rearmament of contingents and military bases on Kuril islands is under way. Already this year they will get Bal and Bastion coastal missile systems as well as new-generation Eleron-3 unmanned aerial vehicles," Shoigu said during a ministry meeting, AFP reported. Russia has been investing in military infrastructure on the Kuril islands, which Japan considers its territory, leading to strained relations between the two nations.

A company that helps users to create their own websites now knows what kind of sites their 80 million users are building without pestering them with repeated questions. Wix, a Tel Aviv-based web development company, is using machine learning on Google's cloud platform to learn more about its users so it can help them find the images they need to build interesting and useful websites. That's just the beginning of how machine learning will be used in the cloud, according to industry analysts who say machine learning will be the biggest thing that's ever hit the cloud. David Zuckerman, head of developer experience for Wix, said machine learning in the cloud will be a boon to companies that don't have a major research division. "The cloud has brought this technology to everyone," he said.

In an unassuming, two-story Victorian town house in Bristol, people are being filmed, monitored, and tracked 24/7. Invisible sensors constantly keep a watchful eye as they go about their business. But what these folks lose in privacy could be our collective gain in life expectancy--that is, if the long-term data bears out. Pivotal to the 15-million ( 21M) Sensor Platform for Healthcare in a Residential Environment (SPHERE) project, this house has been invisibly fitted with dozens of cameras and sensors while its occupants are asked to don wearable devices. The aim is to research how health is related to everyday lifestyle and living conditions over time.

Dr. Quoc Viet Le is a research scientist at Google Brain known for his path-breaking work on deep neural networks (DNN). He is especially famous for his Ph.D work in image processing under Andrew Ng, one of the pioneers of the DNN revolution. Le's and Ng's work demonstrated how computers could be used to learn complicated features and patterns in a way similar to how the mammalian brain learns, with better performance than earlier neural network technology. One of their first breakthroughs was demonstrating the training of a large neural network to detect cats from YouTube videos. This revolutionized the interest in DNNs, and got the current giants of the computer industry such as Google, Facebook and Microsoft in a race to incorporate AI techniques into their software.

It was the unspooling of an unfortunate series of events involving artificial intelligence, human nature, and a very public experiment. Amid this dangerous combination of forces, determining exactly what went wrong is near-impossible. But the bottom line is simple: Microsoft has awful lot of egg on its face after unleashing an online chat bot that Twitter users coaxed into regurgitating some seriously offensive language, including pointedly racist and sexist remarks. On Wednesday morning, the company unveiled Tay, a chat bot meant to mimic the verbal tics of a 19-year-old American girl, provided to the world at large via the messaging platforms Twitter, Kik and GroupMe. According to Microsoft, the aim was to "conduct research on conversational understanding."

Human beings who design intelligent computers have a long history of getting those computers to beat other humans at games to prove how great their computers are. Think IBM's Deep Blue taking down chess legend Garry Kasparov, or the same company's Watson cleaning house on Jeopardy! But there is one game that artificial intelligence has long struggled to master: Go, a board game with roots in ancient China. Go players pick either black stones or white stones, with each player placing one stone of their color every turn. The idea is to capture and remove an opponent's stones by surrounding them with your own.

For human lip readers, context is key in deciphering words stripped of the full nuance of their audio cues. But a technology model for lip-reading developed at the University of East Anglia in the UK has been shown to be able to interpret mouthed words with a greater degree of accuracy than human lip readers, thanks to the application of machine learning tech to classify the visual aspect of sounds. And the kicker is the algorithm doesn't need to know the context of what you're discussing to be able to identify the words you're using. While the model remains a piece of research at this stage, there are scores of potential applications for technology that could automagically transform visual cues into accurate speech -- whether it's helping people who have audio impairments, or enhancing audio-less security video footage with additional speech data -- or even to try to figure out exactly what charged word one footballer spat at another in the heat of a match… Such a tech could also be applied as a fallback for poor audio quality on a mobile or video call. Or even perhaps to power a front-facing camera-based mobile'voice' assistant which you wouldn't actually have to speak to but could just discreetly mouth commands at (how cool would that be?).

Lee Sedol had seen all the tricks. He knew all the moves. As one of the world's best and most experienced players of the complex board game Go, it was difficult to surprise him. But halfway through his first match against AlphaGo, the artificially intelligent player developed by Google DeepMind, Lee was already flabbergasted. AlphaGo's moves throughout the competition, which it won earlier this month, four games to one, weren't just notable for their effectiveness.

LONDON/BRUSSELS – Facial recognition software, scanners that detect weapons and cameras that spot nervous people are some of the technologies that could be used more widely to secure public places, but some would require greater acceptance of surveillance in Europe. The deadly attacks in Brussels on Tuesday highlighted the vulnerability of Europe's airports and transport systems. European Union officials, grappling with the conundrum of how to increase security while retaining the openness of society, have convened meetings to discuss aviation and land transport security. Their goal is to be able to monitor passengers unobtrusively while minimizing additional hold ups that create crowds, which can themselves become new targets. Experts say technology cannot solve the problem on its own, but techniques such as facial recognition able to pick out known suspects can help if Europeans decide they want more surveillance.