We may soon have categorical evidence that living in San Francisco is a terrible idea. IBM announced on Nov. 20 that it had created an award-winning simulation of the Earth's tectonic plates that could soon be used to make predictions about when the next great earthquakes and volcanic eruptions will occur. And its artificial intelligence system, Watson, may prove to be the computer brain that can tell us when it's time to get out of the Bay Area. A team of computer scientists at IBM, in partnership with researchers from the University of Texas at Austin, New York University and the California Institute for Technology, created a model that simulated the entire flow of mantle under the Earth's surface. The model is so complex that it had to run on the Sequoia supercomputer (the third-fastest computer in the world), which the company built for the US government.

British military drones bringing death from above could be capable of firing on targets without the need for a human operator. A new drone being developed by French and British military contractors for use by the RAF, is being built with capabilities of selecting and engaging targets using artificial intelligence. While human intervention is required under international law, the Taranis drone could potentially become fully autonomous if the laws change, taking humans out of the loop and leaving the decision-making to the machines. A new drone being developed by French and British military contractors for use by the RAF, is being built with capabilities of selecting and engaging targets using artificial intelligence, removing the need for humans in the decision-making process. Developed by BAE Systems, the Taranis drone is named after the Celtic god of thunder and is designed to stealthily approach and attack targets, without being detected.

While the idea of tinkering with asteroids might seem like a bad idea, countries around the world are thinking of doing just that. Asteroid mining could be the next big way to harvest resources such as water, oxygen and metals. Now Nasa has awarded 100,000 ( 70,000) to a company to develop technology to direct asteroids towards Earth in order to more easily access them. Nasa has awarded the funding to Made in Space - a California-based company who manufacture technology for space environments. Nasa has awarded the funding to Made in Space - a California-based company who manufacture technology for space environments. The initial 100,000 funding gives the company nine months to prove whether a system to turn asteroids into self-propelled AI spaceships, is viable.

Artificial intelligence has had a bad rap (see: Terminator), and public confidence has waned. Would you trust a machine to diagnose you? Medical professionals today faced off against health firm Babylon's'Check a Symptom' app feature, in a bid to find out who's best equipped to triage patients: man or machine? The challenge saw Professor Irwin Nazareth, former head of primary care at UCLH, examining patients with the help of either: (1) one of Britain's most senior A&E nurses, (2) an Oxford-educated Junior Doctor, or (3) Babylon's'Check' feature. It turns out that that the app came to "the same accurate assessment" as both the nurse and junior doctor, but faster, and at no cost.

In a statement following their meeting, Sartaj Aziz, Pakistan's special adviser on foreign affairs, said the discussions were candid. According to the statement, the two sides restated their positions. Pakistan affirmed that the drone strike breached its sovereignty and compromised an already stalled Afghan peace process; and the United States reiterated its accusation that Pakistan is providing safe havens for the Taliban in Pakistan.

Amazon.com, Inc. (NASDAQ: AMZN) has been talking about the commercial use of drones for quite some time now. However, in a recent interview, CEO Jeff Bezos assured artificial intelligence (AI) is the next big thing. Nonetheless, Chinese firm Ehang might disagree with this idea; in fact, the company is betting on drone taxis as the next attention-grabbing innovation in a move that could remind readers of Alphabet Inc (NASDAQ: GOOGL) (NASDAQ: GOOG)'s autonomous car initiative or Zee.Aero's flying car. Back in January, Ehang, a cameras and drones maker, presented its electric passenger drone, the Ehang 184. Now, the Chinese corporation has partnered with the Nevada Institute for Autonomous Systems (NIAS) and the Governor's Office of Economic Development (Goed) to test the vehicle and, ultimately, get regulatory approval.

With appropriate policies in place, robots should become our "best friends," not our "worst nightmare," experts said at the 41st Annual AAAS Forum on Science & Technology Policy on 14 April. During a panel, entitled "Best Friend or Worst Nightmare? Autonomy and AI in the Lab and in Society," experts on artificial intelligence (AI) spoke about the role of policy in integrating new technologies into people's lives. They both praised current AI advancements, and urged more policymaking in the arena of autonomous systems, particularly related to disaster relief, sustainability, and the military, among other applications. The panel, co-organized by AAAS staff member Jonathan Drake and retired Vice President of Sandia's California Laboratory Miriam John, urged a stronger focus on the promise of AI, rather than its perils.

A series of emails between American diplomats in Pakistan and Washington over drone strikes are the focus of the criminal probe involving presumptive Democratic presidential nominee Hillary Clinton's handling of classified information, according to a report Thursday by The Wall Street Journal. The emails in 2011 and 2012 were sent through a "computer system for unclassified matters" that gave the State Department input into whether a Central Intelligence Agency drone strike went forward, congressional and law enforcement officials briefed on the FBI probe told the Journal. Some of those emails were then sent by then-Secretary of State Clinton's aides to her personal email account and private server, officials told the Journal. The vaguely worded messages, however, didn't mention the "CIA," "drones" or details about the targets, the Journal reported. The emails were written within the often-narrow time frame in which State Department officials had to decide whether or not to object to drone strikes before the CIA pulled the trigger, officials told the newspaper.

Objective: Patient notes in electronic health records (EHRs) may contain critical information for medical investigations. However, the vast majority of medical investigators can only access de-identified notes, in order to protect the confidentiality of patients. In the United States, the Health Insurance Portability and Accountability Act (HIPAA) defines 18 types of protected health information (PHI) that needs to be removed to de-identify patient notes. Manual de-identification is impractical given the size of EHR databases, the limited number of researchers with access to the non-de-identified notes, and the frequent mistakes of human annotators. A reliable automated de-identification system would consequently be of high value. Materials and Methods: We introduce the first de-identification system based on artificial neural networks (ANNs), which requires no handcrafted features or rules, unlike existing systems. We compare the performance of the system with state-of-the-art systems on two datasets: the i2b2 2014 de-identification challenge dataset, which is the largest publicly available de-identification dataset, and the MIMIC de-identification dataset, which we assembled and is twice as large as the i2b2 2014 dataset. Results: Our ANN model outperforms the state-of-the-art systems. It yields an F1-score of 97.85 on the i2b2 2014 dataset, with a recall 97.38 and a precision of 97.32, and an F1-score of 99.23 on the MIMIC de-identification dataset, with a recall 99.25 and a precision of 99.06. Conclusion: Our findings support the use of ANNs for de-identification of patient notes, as they show better performance than previously published systems while requiring no feature engineering.

Gibbs sampling is a Markov Chain Monte Carlo sampling technique that iteratively samples variables from their conditional distributions. There are two common scan orders for the variables: random scan and systematic scan. Due to the benefits of locality in hardware, systematic scan is commonly used, even though most statistical guarantees are only for random scan. While it has been conjectured that the mixing times of random scan and systematic scan do not differ by more than a logarithmic factor, we show by counterexample that this is not the case, and we prove that that the mixing times do not differ by more than a polynomial factor under mild conditions. To prove these relative bounds, we introduce a method of augmenting the state space to study systematic scan using conductance.