Killer robots might be coming for us, but not if Elon Musk has his way. In a letter to the United Nations, Musk championed the cause for 116 entrepreneurs and AI experts to set guidelines for future robots that can make decisions about killing humans. Of course, everyone went into hysterics. The "killer robots" phrase somehow became the norm for many headlines, even though Musk was actually hoping to form a committee (called the Group of Governmental Experts on Lethal Autonomous Weapon Systems). Lethal autonomous weapons threaten to become the third revolution in warfare.

Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic because of their dependence on the background knowledge of adversaries. On the other hand, rigorous methods such as the exponential mechanism for differential privacy are often computationally impractical to use for releasing high dimensional data or cannot preserve high utility of original data due to their extensive data perturbation. This paper presents a criterion called plausible deniability that provides a formal privacy guarantee, notably for releasing sensitive datasets: an output record can be released only if a certain amount of input records are indistinguishable, up to a privacy parameter. This notion does not depend on the background knowledge of an adversary. Also, it can efficiently be checked by privacy tests. We present mechanisms to generate synthetic datasets with similar statistical properties to the input data and the same format. We study this technique both theoretically and experimentally. A key theoretical result shows that, with proper randomization, the plausible deniability mechanism generates differentially private synthetic data. We demonstrate the efficiency of this generative technique on a large dataset; it is shown to preserve the utility of original data with respect to various statistical analysis and machine learning measures.

Six-year-old Jamil starts school on September 11. There will be no ISIS fighters in his first grade class in Ulm, Germany, but Jamil, haunted by nightmares, is still fighting the ISIS demons. The boy's ordeal began in northern Iraq on August 3, 2015. Then four-years-old, he was one of many Yazidis captured by ISIS, crammed into a bus, and taken to Mosul, the second largest city in Iraq, then under ISIS control. The Yazidi people are an ancient, non-Muslim religious community regarded by radical Islamists as infidels worthy of death.

Mixing artificial intelligence with climate science helps researchers to identify previously unknown atmospheric processes and rank climate models. Many of the latest climate models seek to increase the detail in simulations of cloud structure. As Earth-observing satellites become more plentiful and climate models more powerful, researchers who study global warming are facing a deluge of data. Some are now turning to the latest trend in artificial intelligence (AI) to help trawl through all the information, in the hope of discovering new climate patterns and improving forecasts. "Climate is now a data problem," says Claire Monteleoni, a computer scientist at George Washington University in Washington DC who has helped to pioneer the marriage of machine-learning techniques with climate science.

As Earth-observing satellites become more plentiful and climate models more powerful, researchers who study global warming are facing a deluge of data. Some are now turning to the latest trend in artificial intelligence (AI) to help trawl through all the information, in the hope of discovering new climate patterns and improving forecasts. "Climate is now a data problem," says Claire Monteleoni, a computer scientist at George Washington University in Washington DC who has helped to pioneer the marriage of machine-learning techniques with climate science. In machine learning, AI systems improve in performance as the amount of data that they analyse grows. This approach is a natural fit for climate science: a single run of a high-resolution climate model can produce a petabyte of data, and the archive of climate data maintained by the UK Met Office, the national weather service, now holds about 45 petabytes of information--and adds 0.085 petabytes a day.

When we were in the city of Danyang, China, we witnessed a real-life paradox. Danyang is best known for its explosive growth in optical lens manufacturing over the last decade, sprouting hundreds of factories with cleanrooms chock-full of gleaming, automated machinery. There is a lot that goes into manufacturing a lens, and this machinery performs the bulk of it: from lens curing, lens cleaning, to lens coating. As Stanford AI researchers and engineers wildly interested in Chinese manufacturing, we were impressed by this caliber of automation. But the paradox greeted us as soon as we walked into the rooms responsible for the most critical part of the entire process: quality control.

Kids with cerebral palsy (CP) can have limited movement (and therefore independence) throughout their lives. Some of them who experience the related set of neurological and movement disorders have what's called "crouch gait," which is characterized by excessive bending at the knee; up to 50 percent of people with cerebral palsy stop walking by adulthood. Researchers at the National Institutes of Health (NIH) have been testing robotic leg exoskeletons that help kids with CP walk more easily. According to the researchers, six of the seven study participants showed improved knee extension and were able to walk with robotic assistance after just six trials. The exoskeletons are more than just a brace; they actively support a walking posture in kids with cerebral palsy without taking control away from the children themselves.

More than 100 of the world's top robotics experts wrote a letter to the United Nations recently calling for a ban on the development of "killer robots" and warning of a new arms race. But are their fears really justified? Entire regiments of unmanned tanks; drones that can spot an insurgent in a crowd of civilians; and weapons controlled by computerised "brains" that learn like we do, are all among the "smart" tech being unleashed by an arms industry many believe is now entering a "third revolution in warfare". "In every sphere of the battlefield - in the air, on the sea, under the sea or on the land - the military around the world are now demonstrating prototype autonomous weapons," says Toby Walsh, professor of artificial intelligence at Sydney's New South Wales University. "New technologies like deep learning are helping drive this revolution. The tech space is clearly leading the charge, and the military is playing catch-up."

Small convoys of partially driverless lorries will be tried out on major British roads by the end of next year, the government has announced. A contract has been awarded to the Transport Research Laboratory (TRL) to carry out the tests of vehicle "platoons". Up to three lorries will travel in formation, with acceleration and braking controlled by the lead vehicle. But the head of the AA said platoons raised safety concerns. The TRL will begin trials of the technology on test tracks, but these trials are expected to move to major roads by the end of 2018.

How is computer security different in a high-performance computing (HPC) context from a typical IT context? On the surface, a tongue-in-cheek answer might be, "just the same, only faster." After all, HPC facilities are connected to networks the same way any other computer is, often run the same, typically Linux-based operating systems as are many other common computers, and have long been subject to many of the same styles of attacks, be they compromised credentials, system misconfiguration, or software flaws. Such attacks have ranged from the "wily hacker" who broke into U.S. Department of Energy (DOE) and U.S. Department of Defense (DOD) computing systems in the mid-1980s,42 to the "Stakkato" attacks against NCAR, DOE, and NSF-funded supercomputing centers in the mid-2000s,24,39 to the thousands of probes, scans, brute-force login attempts, and buffer overflow vulnerabilities that continue to plague high-performance computing facilities today. On the other hand, some HPC systems run highly exotic hardware and software stacks. In addition, HPC systems have very different purposes and modes of use than most general-purpose computing systems, of either the desktop or server variety. This fact means that aside from all of the normal reasons that any network-connected computer might be attacked, HPC computers have their own distinct systems, resources, and assets that an attacker might target, as well as their own distinctive attributes that make securing such systems somewhat distinct from securing other types of computing systems. The fact that computer security is context- and mission-dependent should not be surprising to security professionals--"security policy is a statement of what is, and what is not, allowed,"7--and each organization, will therefore have a somewhat distinctive security policy.