ABOARD THE USS NIMITZ -- High above the Persian Gulf, an Iranian drone crosses the path of American fighter jets lining up to land on the USS Nimitz. The drone buzzes across the sky more than a mile above the massive aircraft carrier and is spotted by the fighters. But for the senior Navy commanders on the ship, the presence of the enemy drone so close is worrying. Their biggest fear is the surveillance aircraft will start carrying weapons, posing a more direct threat to U.S. vessels transiting one of the world's most significant strategic and economic international waterways. "It's just a matter of time before we see that," said Navy Rear Adm. Bill Byrne, commander of the carrier strike group that includes the Nimitz.

John Taylor, a Stanford University economist famous for the "Taylor Rule" that lays out a rules-based approach to setting monetary policy, participated in all the conference discussions. Also present was Glenn Hubbard, dean of the Columbia University Business School, who chaired the Council of Economic Advisers in the George W. Bush administration and has often been mentioned when the Fed's top job has come open. In his July interview with the Wall Street Journal, Trump said he had other candidates in addition to Yellen and Cohn but he declined to name them.

In a refreshing change of pace, this week's security news included little to no escalation of nuclear rhetoric. Let's count that as a win! Digital financial services provider Enigma, for instance, lost its supporters almost $500,000 in cryptocurrency thanks to bad password habits. Domestic helper robots lost security cred by being hacked into tiny robotic Chucky dolls. The US government nearly lost a security-focused ranking of industries, coming in 16 out of 18.

If you receive post in London this month, there's a good chance it arrived to your door via an electric postal truck. Royal Mail is trialling nine British-made fully electric commercial vehicles this month, transporting packages between mail and distribution centres in the capital. The firm hopes that the trial will increase Royal Mail's efficiency, and reduce vehicle emissions. While the trucks will have human drivers, they are'autonomous-ready', which suggests that Royal Mail deliveries could one day be made by driverless vehicles. However, Royal Mail says it has'no current plans' to roll out the technology.

Over the last few years, there has been a worldwide resurgence of Artificial Intelligence (AI) to the point where it dominates almost all business, investment, and ethical narratives. There have formed two extreme views of AI: In one it is believed that AI will augment humans, and in the other it is believed that AI will diminish them, to the point of even threatening humanity's existence. The truth will lie somewhere in between. Many of the arguments on both sides are informed by the results stemming from a technique called Generative Adversarial Networks (GANs) that has given AI anthropomorphic qualities often associated with human motivations. The techniques used to train AI algorithms, broadly called Machine Learning, essentially mimic Operant Conditioning, which includes positive and negative reinforcement methods to increase the rate of a particular desired outcome or decrease the rate of an undesired outcome.

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.