Strictly speaking, every single week is Robotics Week around here, but this week (or, technically, April 8-16), also happens to be U.S. National Robotics Week. This is an Official Thing--the U.S. House of Representatives declared the second week in April to be National Robotics Week starting back in 2010 and continuing until the End of Time. There are hundreds of events going on all over the country for the eighth edition of this event, ensuring that you'll be able to find something awesome and roboty to do near you. There's a list of everything (organized by state) at the official website here. IEEE Spectrum is a big supporter of National Robotics Week, and like in previous years, we teamed up with iRobot and Georgia Tech to put together a new set of robot trading cards for 2017, which is obviously by far the most exciting thing about NRW every year, and you can see those here.

The evidence that Russia hacked the US to influence the outcome of the 2016 presidential election continues to grow. The latest comes from AFP, which says that that a Russian "computer expert" was arrested in Spain today at the Barcelona airport on suspicions of hacking the US presidential election campaigns. Furthermore, the US has already put in an extradition request so that the subject Piotr Levashov would have to stand trial here for his alleged crimes. The US has 40 days to present its extradition request to Spain; given that Levashov's arrest reportedly was the result of an "international complaint," it's reasonable to guess that the US is the one who asked the arrest to be made. Indeed, Piotr Levashov's wife Maria told Russian TV that her husband was detained at the request of American authorities.

The next 20 years will see a significant departure from what we currently call cognitive and deep AI technologies and progress toward what would arguably be called conscious machine intelligence. There will be a dichotomy in commercial efforts, the former concentrating on rivalling human intelligence, the biggest moneymakers being forms of AI broader than those currently used in cybersecurity, medicine, and financial modeling. The latter breakaway effort would concentrate on artificial neural architectures emulating all aspects of brain function, allowing researchers to better understand mind and consciousness, the most lucrative venture therein being the creation of the necessary download vessels for the preservation of human consciousness. On the military front, generals and admirals will abandon the concept of one-to-many control and embrace a zero-to-many tack that allows sufficiently'indoctrinated' AI to autonomously make and act upon its own ideologically-based battlefield decisions, with or without a declaration of war.

Enjoy it while it lasts. US Treasury Secretary Steven Mnuchin may think artificial intelligence (AI) isn't going to start taking humans' jobs for 50 to 100 years, but most experts believe a revolution in automation is coming far sooner, promising massive increases in efficiency -- and job losses on a huge scale. A recent study put out by PwC estimated that as many as 30% of UK jobs could be "susceptible to automation by robots and AI" by the early 2030s -- with 38% in the US at risk, 35% in Germany, and 21% in Japan -- although it believes jobs will be created elsewhere in the economy to help offset this. Are we doing enough to prepare? Absolutely not, says Bob Moritz, global chairman of consultancy firm PwC.

At a demonstration of Amazon Web Services' new artificial intelligence image recognition tool last week, the deep learning analysis calculated with near certainty that a photo of speaker Glenn Gore depicted a potted plant. "It is very clever, it can do some amazing things but it needs a lot of hand holding still. AI is almost like a toddler. They can do some pretty cool things, sometimes they can cause a fair bit of trouble," said AWS' chief architect in his day two keynote at the company's summit in Sydney. Where the toddler analogy falls short, however, is that a parent can make a reasonable guess as to, say, what led to their child drawing all over the walls, and ask them why.

Matching datasets of multiple modalities has become an important task in data analysis. Existing methods often rely on the embedding and transformation of each single modality without utilizing any correspondence information, which often results in sub-optimal matching performance. In this paper, we propose a nonlinear manifold matching algorithm using shortest-path distance and joint neighborhood selection. Specifically, a joint nearest-neighbor graph is built for all modalities. Then the shortest-path distance within each modality is calculated from the joint neighborhood graph, followed by embedding into and matching in a common low-dimensional Euclidean space. Compared to existing algorithms, our approach exhibits superior performance for matching disparate datasets of multiple modalities.

It's obvious that messaging as a new UI is becoming one of the new mega trends this year. Two weeks ago Apple announced with iOS 10 that it's opening up iMessages to outside developers, while Facebook's recent F8 developer conference kicked off both days with keynotes on bots and messaging. Microsoft, too, has opened up Skype messaging to third party developers through Cortana. With all these big American aircraft carriers all moving to get messaging and bot infrastructure on their platforms, one thing is clear: there is now enough widespread support to give messaging its proper place as a ubiquitous, smart, and natural UI for harnessing technology and getting things done. So why are the big platforms suddenly so interested in'opening up' their messaging infrastructure? This inspiration seems to be coming straight from WeChat.

Artificial intelligence is one of the most prominent technologies currently being advanced. Not only is it a hot topic for researchers, but the world's greatest technological minds are fearful of its potential. Bill Gates, Stephen Hawking, Elon Musk, hundreds of the world's top minds have signed papers stating their fear about the destructive potential of AI systems. Regardless of the top minds in opposition, advances in the industry continue. Integrated AI systems today are already helping us get through daily life, according to Wired.

This is a guest post by the Secretary of State for Culture, Media and Sport, Karen Bradley, coinciding with the launch of the UK's Digital Strategy. Digital technology already underpins innovation and improvements in productivity across the whole economy, and the government's new Digital Strategy I am publishing today aims to harness this power and make Britain the best place in the world to start and grow a digital business. The strategy will also ensure the benefits are felt right across the country and throughout all sections of society – with nobody left behind. We already have some of the most exciting startups in the world, and some of the strongest technology clusters. But as we prepare to leave the European Union, the UK's global competitiveness will increasingly depend on not only a vibrant digital sector but also on all our businesses using the best digital technology and data to drive innovation and productivity.

As society enters an era where AI will take life-or-death decisions--spotting whether moles are cancerous and driving us to work--trusting these machines will become ever more important. The difficulty is that it's almost impossible for us to understand the inner workings of many modern AI systems that perform human-like tasks, such as recognizing real-life objects or understanding speech. The models produced by the deep-learning systems that have powered recent AI breakthroughs are largely opaque, functioning as black boxes that spit out a result but whose operation remains mysterious. This inscrutability stems from the complexity of the large neural networks that underpin deep-learning systems. These brain-inspired networks are interconnected layers of algorithms that feed data into each other and can be trained to carry out specific tasks.