In 2015, car-and-rocket man Elon Musk joined with influential startup backer Sam Altman to put artificial intelligence on a new, more open course. They cofounded a research institute called OpenAI to make new AI discoveries and give them away for the common good. Now, the institute's researchers are sufficiently worried by something they built that they won't release it to the public. The AI system that gave its creators pause was designed to learn the patterns of language. It does that very well--scoring better on some reading-comprehension tests than any other automated system.

The United States' technological sophistication has long supported its military predominance. In the 1990s, the U.S. military started to hold an uncontested advantage over its adversaries in the technologies of information-age warfare--from stealth and precision weapons to high-tech sensors and command-and-control systems. Those technologies remain critical to its forces today. For years, China has closely watched the United States' progress, developing asymmetric tools--including space, cyber, and electronic capabilities--that exploit the U.S. military's vulnerabilities. Today, however, the Chinese People's Liberation Army (PLA) is pursuing innovations in many of the same emerging technologies that the U.S. military has itself prioritized. Artificial intelligence is chief among these.

San Francisco -- A group of researchers are quietly commercialising an artificial intelligence (AI)-driven lie detector, which they hope will be the future of airport security. Discern Science International is the start-up behind a deception detection tool named the Avatar, which features a virtual border guard that asks travelers questions. The machine, which has been tested by border services and in airports, is designed to make the screening process at border security more efficient, and to weed out people with dangerous or illegal intentions more accurately than human guards are able to do. But its development also raises questions about whether a person's propensity to lie can be accurately measured by an algorithm. The Avatar -- whose "face" appears on a screen -- asks travelers a series of pre-configured questions and decides whether they are lying.

The ride-hailing service wants to ferry the world around in self-driving cars and on electric scooters, deliver our takeouts and groceries by drone, and ship freight via robot trucks. But first Uber needs to answer a big question: will it ever make any money? This week, Wall Street will have the chance to ask that question. Uber went public in May in one of the most anticipated initial public offerings in years. To say it stalled would be an understatement.

Single-microphone, speaker-independent speech separation is normally performed through two steps: (i) separating the specific speech sources, and (ii) determining the best output-label assignment to find the separation error. The second step is the main obstacle in training neural networks for speech separation. Recently proposed Permutation Invariant Training (PIT) addresses this problem by determining the output-label assignment which minimizes the separation error. In this study, we show that a major drawback of this technique is the overconfident choice of the output-label assignment, especially in the initial steps of training when the network generates unreliable outputs. To solve this problem, we propose Probabilistic PIT (Prob-PIT) which considers the output-label permutation as a discrete latent random variable with a uniform prior distribution. Prob-PIT defines a log-likelihood function based on the prior distributions and the separation errors of all permutations; it trains the speech separation networks by maximizing the log-likelihood function. Prob-PIT can be easily implemented by replacing the minimum function of PIT with a soft-minimum function. We evaluate our approach for speech separation on both TIMIT and CHiME datasets. The results show that the proposed method significantly outperforms PIT in terms of Signal to Distortion Ratio and Signal to Interference Ratio.

We study the problem of automatic fact-checking, paying special attention to the impact of contextual and discourse information. We address two related tasks: (i) detecting check-worthy claims, and (ii) fact-checking claims. We develop supervised systems based on neural networks, kernel-based support vector machines, and combinations thereof, which make use of rich input representations in terms of discourse cues and contextual features. For the check-worthiness estimation task, we focus on political debates, and we model the target claim in the context of the full intervention of a participant and the previous and the following turns in the debate, taking into account contextual meta information. For the fact-checking task, we focus on answer verification in a community forum, and we model the veracity of the answer with respect to the entire question--answer thread in which it occurs as well as with respect to other related posts from the entire forum. We develop annotated datasets for both tasks and we run extensive experimental evaluation, confirming that both types of information ---but especially contextual features--- play an important role.

As the Veterans Affairs Department's inaugural Director of Artificial Intelligence, Gil Alterovitz aims to leverage the emerging technology and the agency's cornucopia of data to proactively anticipate and tackle problems afflicting veterans like never before. In a conversation with Nextgov, Alterovitz detailed his present efforts and future-facing vision to support VA in executing that mission. "Nowhere in the country is there such potential for research to be developed and translated into clinical care so quickly. In this case, it's to help our special population of veterans … and those patients have actually asked us to deal with their needs," Alterovitz said. "We really want to be the go-to place for veterans through AI research and development--so instead of reacting, we can really anticipate their needs."

You could be forgiven for thinking that AI will soon replace human physicians based on headlines such as "The AI Doctor Will See You Now," "Your Future Doctor May Not Be Human," and "This AI Just Beat Human Doctors on a Clinical Exam." But experts say the reality is more of a collaboration than an ousting: Patients could soon find their lives partly in the hands of AI services working alongside human clinicians. There is no shortage of optimism about AI in the medical community. But many also caution the hype surrounding AI has yet to be realized in real clinical settings. There are also different visions for how AI services could make the biggest impact.

At the final session of the 2019 Space Symposium in Colorado Springs, attendees straggled into a giant ballroom to listen to an Air Force official and a National Geospatial-Intelligence Agency (NGA) executive discuss, as the panel title put it, "Enterprise Disruption." The presentation stayed as vague as the title until a direct question from the audience seemed to make the panelists squirm. Just how good, the person wondered, had the military and intelligence communities' algorithms gotten at interpreting data and taking action based on that analysis? They pointed out that the commercial satellite industry has software that can tally shipping containers on cargo ships and cars in parking lots soon after their pictures are snapped in space. "When will the Department of Defense have real-time, automated, global order of battle?" they asked. "That's a great question," said Chirag Parikh, director of the NGA's Office of Sciences and Methodologies.

Cloud computing on a certified, compliant, properly-run cloud service like Microsoft Azure is likely to be far more secure than on-premise servers in your office or your data centre. Your data is encrypted at rest and in motion; cloud systems are probably patched more often and configured more securely than your servers; and admin access is locked down and only enabled for'just enough access, just in time' to run specific commands within specific time windows. Also, the admins will have gone through background checks and work in secure locations that require biometric credentials to access. Unlike nuclear weapons, cyberweapons can be proliferated more quickly and the threat from accidentally setting them off is even greater. There are still problems, though.