As emerging technologies go, Artificial Intelligence has certainly taken its time in making its presence felt on the world. Surprising as it may be (to some), the term AI has existed for almost 70 years, having first been uttered back in 1956 (the same year IBM invented the first hard drive) by computer scientist John McCarthy - AKA the'father of AI'. Since then, AI has experienced a largely stop-start existence due to, in part, sporadic funding and below-par technology. In truth, the term AI has (arguably) gained more notoriety for storylines of killer robots (and the occasional Wall-e) hell-bent on destroying mankind than for its practical use and business benefits. "We are at the cusp of a new revolution, one that will ultimately transform every organisation, every industry and every public service across the world" Thanks to breakthroughs in computing power, the advent and availability of big data, cloud hosting/storage, highly sophisticated software, complex algorithms and a big dollop of imagination, the potential of AI is now starting to be fulfilled – with the business world being the biggest benefactors. As the late great Professor Stephen Hawkins said on AI: "The genie is out of the bottle. AI, could be the biggest event in the history of our civilisation." The market has reacted at pace.

Siri doesn't appear to think highly of Donald Trump. At least the virtual assistant didn't on Thanksgiving, when asking about the president showed an image of a penis instead. The error seems to be the result of someone editing Mr Trump's Wikipedia page and inserting the image. That meant it pulled through when Siri accessed the article to show more information – showing the explicit picture to the vast number of people who use the voice assistant on their iPhones. Uber has halted testing of driverless vehicles after a woman was killed by one of their cars in Tempe, Arizona.

IBM and the other vendors who are bidding on the CORAL2 systems for the US Department of Energy can't talk about those bids, which are in flight, and Big Blue and its partners in building the "Summit" supercomputer at Oak Ridge National Laboratory and "Sierra" at Lawrence Livermore National Laboratory – that would be Nvidia for GPUs and Mellanox Technologies for InfiniBand interconnect – are all about publicly focusing on the present, since these two machines are at the top of the flops charts now. We know they are actually working hard to win the next deal for the exascale successors to these two machines, but when we had a chat at the SC18 supercomputer conference with Dave Turek, vice president of technical computing and OpenPower, we didn't even bother to bring CORAL2 up. There were other interesting things to discuss. But as an aside: We did talk to Turek about CORAL2 back in June at ISC18, just after the bids for the systems had been turned in to the Department of Energy, and he couldn't say much then except that IBM should get credit for delivering Summit and Sierra more or less as planned and that this should mean a lot when it comes to the CORAL2 bids. But maybe it wouldn't because with each generation of machines, the major labs have to do an architecture survey and take into account any new developments – or lack thereof – that could offer better performance, wider application support, lower prices, or any combination of the above. In a sense, it is always back to square one on these big systems deals, which is good for driving innovation but perhaps something to make the major suppliers a bit testy until they win the deals. It seems inconceivable that the combination of the IBM Power10 chip and a future Nvidia GPU and possibly 400 Gb/sec NDR or 800 Gb/sec XDR InfiniBand won't win the CORAL2 bids, but with Cray back in the game with its own Slingshot interconnect, there is a chance that it could win at least one of the three CORAL2 machines.

You can simulate it, sure, but the most valuable lessons are learned during actual attempts. When things go poorly, those lessons are also the most expensive. The fact is, most missions to Mars don't make it, though NASA has a better track record than most. The agency has executed seven successful touchdowns on the red planet. On Monday, November 26th, it will attempt its eighth, when it endeavors to land the $830-million InSight spacecraft on Elysium Planitia, a vast plain just north of the Martian equator.

But despite extensive company-government cooperation--spurred by White House pledges to fast-track decisions--trade-association leaders now see final FAA regulatory action stretching past the end of the decade. Some experts say 2022 is more likely. That would be up to three years later than some of the agency's initial projections, and many months longer than a revised timetable the FAA and its parent agency, the U.S. Department of Transportation, shared informally just months ago. "I'm not happy about it," said Brian Wynne, president and chief executive of the Association for Unmanned Vehicle Systems International, the industry's leading trade group. The process needs to move forward, he said in an interview, because so many commercial applications are in a holding pattern until new rules are approved.

This timely seminar will bring together key policymakers and stakeholders to discuss the next steps in the UK's AI policy. It takes place in the context of the Government's ambition for the UK to be a world leader on AI, and plans for investment in skills, infrastructure and research, as well as new finance for start-ups and scale-ups, and support for creating'clusters' of AI-focused businesses across the country outlined in the AI Sector Deal, and the Artificial Intelligence and Data Grand Challenge in the Industrial Strategy.

Most conservation initiatives require changes in human behavior. For example, the establishment of a protected area will typically require some people to change their land-use or fishing practices. Yet conventional attempts to encourage proenvironmental behavior through awareness campaigns, financial incentives, and regulation can prove ineffective (1, 2). Insights into inducing behavior change from the social and behavioral sciences are therefore of critical importance for conservation scientists and practitioners (2–4). Conservation initiatives have begun to leverage a wide range of such behavioral insights (5) particularly regarding cognitive biases and social influence (see the figure). However, their application in the diverse socioeconomic and cultural contexts in which many conservation programs operate raises important ethical and implementation-related challenges.

Politics are bound to be a topic of conversation at Thanksgiving meals across the country today. Whether someone at your meal decides to share their thoughts on the state of America in 2018 is up to them. One thing you shouldn't do: ask Siri about Donald Trump or how old the president is. In an apparent glitch first spotted by The Verge, asking Siri the question "who is Donald Trump" or "how old is Donald Trump" returns an image of male genitalia in place of a picture of the 45th president of the United States. USA TODAY was able to replicate the glitchwhen asking Siri "who is Donald Trump" on an iPhone X on Thursday evening.

The Regional Transportation Commission of Southern Nevada, the Nevada Department of Transportation and the Nevada Highway Patrol teamed up for the pilot program with Waycare. The Israeli startup already carried out a similar program in Tel Aviv, and it started a crash prevention program last year in Tampa, Florida.

Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data. Applications of spectral GCNs have been successful, but limited to a few problems where the graph is fixed, such as shape correspondence and node classification. In this work, we address this limitation by revisiting a particular family of spectral graph networks, Chebyshev GCNs, showing its efficacy in solving graph classification tasks with a variable graph structure and size. Chebyshev GCNs restrict graphs to have at most one edge between any pair of nodes. To this end, we propose a novel multigraph network that learns from multi-relational graphs. We model learned edges with abstract meaning and experiment with different ways to fuse the representations extracted from annotated and learned edges, achieving competitive results on a variety of chemical classification benchmarks.