The DARPA contracts will focus on helping machines operate in complex, real-world scenarios. They will also tackle one of the central conundrums in AI: something insiders like to call "explainability." Right now, what motivates the results that algorithms return and the decisions they make is something of a black box. That's worrying enough when it comes to policing posts on a social media site, but it is far scarier when lives are at stake. Military commanders are more likely to trust artificial intelligence if they know what it is "thinking," and the better any of us understands technology, the more responsibly we can use it.

Most of the attention on the relationship between the U.S. and China is about whether or not there will be a trade war, and, more generally, about how each country is trying to displace the other in a zero-sum game to be the world's dominant superpower. But the more important consequence of that relationship is how the two countries have created a duopoly -- as the dominant powers in AI -- which is going to change the world much more dramatically than any trade war has the potential to. It's one of those books you read and think, "Why are people reading any other book right now when this is so clearly the one they need to be reading?" His thesis -- urgent but hopeful -- goes something like this: Work on artificial intelligence has been going on since the 1950s. But in the last 5 or so years, advances have accelerated in deep learning, or what Kai-Fu Lee calls "narrow AI" technology, which can digest huge amounts of data from one particular domain and make decisions much more effectively or accurately than a human can.

A computer graphic image provided by Japan's space agency shows two drum-shaped and solar-powered rovers on an asteroid. A Japanese unmanned spacecraft released two small rovers on the asteroid Ryugu last week. A computer graphic image provided by Japan's space agency shows two drum-shaped and solar-powered rovers on an asteroid. A Japanese unmanned spacecraft released two small rovers on the asteroid Ryugu last week. Want to see what it would be like to stand on a asteroid? Well, if you were not a human but rather a seven-inch-diameter, just under 3-inch-tall, hopping robot?

Killing comes in degrees of intimacy. At one extreme there is the example of Freddie Oversteegen, a hero of the Dutch resistance, who as a 14-year-old-girl used to pick up German soldiers and collaborators in bars, lure them into the woods, and once in a secluded spot shoot them dead. Long after the war, she told an interviewer that when seeing a man she had just shot fall, "you want to help them to get up". At the far extreme, perhaps, were the crew of Enola Gay, who killed 80,000 civilians with one bomb, dropped on Hiroshima from miles above. Drone pilots are even safer and further from their victims than high-altitude bombers.

Matthew van Putten is a research analyst for GovernmentCIO Media and federal research manager for GovernmentCIO. A graduate of Johns Hopkins SAIS Strategic Studies and International Economics programs, he focuses on strategic affairs, Chinese financial markets, African politics and the impact of technology on international politics. With rumblings about "disruption" and automation-generated job insecurity, companies understand they have to look for opportunities to leverage emerging technology to stay relevant and generate returns. Companies increasingly rely on AI to bolster their cyberdefenses -- for offloading customer-service interactions, predicting likelihood of opioid overprescription and managing growing piles of data. AI increases output and efficiency, be it good or bad.

Artificial intelligence, or AI, will become an integral warfighter for the U.S. Army if the service's research arm has its way. Scientists at the Army Research Laboratory are pursuing several major goals in AI that, taken together, could revolutionize the composition of a warfighting force in the future. The result of their diverse efforts may be a battlefield densely populated by intelligent devices cooperating with their human counterparts. This AI could be self-directing sensors, intelligent munitions, smart exoskeletons and physical machines, such as autonomous robots, or virtual agents controlling networks and waging defensive and offensive cyber war. And it won't be just the virtual agents that wage direct combat. Intelligent devices will fight their counterparts on the enemy's side.

For years Google has warned users about natural disasters by incorporating alerts from government agencies like FEMA into apps like Maps and Search. Now, the company is making predictions of its own. As part of a partnership with the Central Water Commission of India, Google will now alert users in the country about impending floods. The service is only currently available in the Patna region, with the first alert going out earlier this month. As Google's engineering VP Yossi Matias outlines in a blog post, these predictions are being made using a combination of machine learning, rainfall records, and flood simulations.

Load up the homepage for e-commerce giant, Alibaba – a wholesale shopping site that's more or less China's answer to eBay – and you'll find images and descriptions of anything you could wish to buy, from kitchen sinks to luxury yachts. Every item has a short headline, but most are little more than lists of keywords: hand-picked search terms to ensure this USB phone charger or that pair of flame-resistant overalls float to the top in a sea of thousands upon thousands of similar items. It sounds simple, but there's an art to this copywriting. Yet Alibaba recently revealed that it is training an artificial intelligence to generate these item descriptions automatically – and they're not the only ones. Over the last few decades AIs have been taught to compose music, paint pictures and write (bad) poems. "Generative bots are the new chatbot," says Jun Wang at University College London.

Microsoft wants the Australian government to close loopholes in proposed data sharing-and-release legislation that it believes could be used to shut down or limit access to data without explanation. The software giant used a submission [pdf] to a Prime Minister & Cabinet-led consultation to outline concerns that data could be too easily withheld or not offered in the first place, despite assertions that "much of the Australian government's data is not personal or sensitive". Microsoft suggested that Australian laws should, in part, mimic the EU's reuse of public sector information directive, which requires agencies to explain why they deny access to data. "We note that the proposed process for sharing data does not appear to require Commonwealth data custodians to provide an explanation either when denying a data access request, or if they decide not to provide open access to data in the first instance," Microsoft said. "[We] suggest that the bill require data custodians to provide such an explanation.

With the rapid development of high-throughput technologies, parallel acquisition of large-scale drug-informatics data provides huge opportunities to improve pharmaceutical research and development. One significant application is the purpose prediction of small molecule compounds, aiming to specify therapeutic properties of extensive purpose-unknown compounds and to repurpose novel therapeutic properties of FDA-approved drugs. Such problem is very challenging since compound attributes contain heterogeneous data with various feature patterns such as drug fingerprint, drug physicochemical property, drug perturbation gene expression. Moreover, there is complex nonlinear dependency among heterogeneous data. In this paper, we propose a novel domain-adversarial multi-task framework for integrating shared knowledge from multiple domains. The framework utilizes the adversarial strategy to effectively learn target representations and models their nonlinear dependency. Experiments on two real-world datasets illustrate that the performance of our approach obtains an obvious improvement over competitive baselines. The novel therapeutic properties of purpose-unknown compounds we predicted are mostly reported or brought to the clinics. Furthermore, our framework can integrate various attributes beyond the three domains examined here and can be applied in the industry for screening the purpose of huge amounts of as yet unidentified compounds. Source codes of this paper are available on Github.