There's been a recent hullabaloo over the US Army developing killer robots, but we're here to set you straight. The US Army's "Advanced Targeting and Lethality Automated System" (ATLAS) project isn't what you think. I know it sounds bad, but if you ignore every other word it's actually quite palatable. "Advanced and Automated" sounds pretty good doesn't it? Those are probably the only words you should focus on. The US Army just happens to be simultaneously developing "optionally-manned" tanks and soliciting white papers for a fully autonomous targeting system capable of bringing a weapon to bear on both vehicle and individual personnel targets.

Nearly a year after one of Uber's autonomous SUVs struck and killed a pedestrian, Elaine Herzberg, Arizona prosecutors said they did not find the company criminally liable in the incident. Reuters published parts of the letter from Yavapai County Attorney Sheila Polk the collision video, as it displays, likely does not accurately depict the events that occurred. The case was referred from Maricopa County, where it occurred, due to a conflict. Uber has not commented on the letter, however the prosecutor's office has referred the case back to Maricopa County's office to see if the back-up driver -- who was apparently streaming Hulu at the time -- will face charges. NHTSA and the NTSB are still investigating the crash, even as Uber has resumed some testing.

The Defense Advanced Research Projects Agency is pursuing an unprecedented machine-learning "breakthrough" technology -- and pioneering a new cybersecurity method intended to thwart multiple attacks at one time and stop newer attacks less recognizeable to existing defenses. A DARPA-led "Lifelong Learning Machines" (L2M) program, intended to massively improve real-time AI and machine learning, rests upon the fundamental premise that certain machine-learning-capable systems might struggle to identify, integrate and organize some kinds of new or complicated yet-to-be-seen information. "If something new is different enough, the system may fail. This is why I wanted to have some kind of machine learning that learns during experiences. Systems do not know what to do in some situations," Hava Siegelmann, DARPA program manager at the Information Innovation Office and Professor of Computer Science at the University of Massachusetts.

Facial recognition and eye scanning have been deployed at prisons to prevent drug smuggling. The Ministry of Justice said the biometric scans for visitors were designed to help staff identify people bringing in contraband. At one prison, there were more "no shows" from visitors than usual after they learned the scans were being used. But prison campaigners said if families were deterred from visiting, then it would be "counter-productive". In the trials, facial recognition technology was used at HMP Humber; iris scanners at HMP Lindholme; and identity document verification at HMP Hull.

The U.S. Army is funding research on the skin of the mako shark that was presented Monday and could impact how aircraft are built. The skin of the mako shark is being studied by the U.S. Army to help build faster aircraft, according to research presented on Monday. Makos, the world's fastest sharks, have rows of millions of tiny raised scales along their sides and fins that researchers at the University of Alabama (UA) believe could be the reason for that lightning speed. Dr. Amy Lang, a UA aeronautical engineer, is leading the research that she presented at a meeting of the American Physical Society Meeting on Monday, The Independent reported. The mako's scales, called denticles, are translucent, flexible and shaped like tiny shark teeth.

A new report has called into question the conclusion of an investigation launched by the US National Highway Traffic Safety Administration in 2016 following a fatal Tesla crash. The NHTSA looked into the safety of Tesla's autonomous assistance features after a Model S operating with Autopilot struck a tractor trailer that year, killing the Tesla driver in the first deadly accident of its kind. Ultimately, the NHTSA determined that the system wasn't just safe, but actually slashed crash rates by nearly 40 percent. A new investigation using data obtained through a Freedom of Information Act (FOIA) lawsuit, however, shows that the reality is a lot more complicated. According to Quality Control Systems Corporation, which conducted the new analysis, the NHTSA misinterpreted the data it was provided; instead of reducing crashes, the findings suggest autosteer may have made accidents more common.

Last month, the U.S. Army put out a call to private companies for ideas about how to improve its planned semi-autonomous, AI-driven targeting system for tanks. In its request, the Army asked for help enabling the Advanced Targeting and Lethality Automated System (ATLAS) to "acquire, identify, and engage targets at least 3X faster than the current manual process." But that language apparently scared some people who are worried about the rise of AI-powered killing machines. In response, the U.S. Army added a disclaimer to the call for white papers in a move first spotted by news website Defense One. Without modifying any of the original wording, the Army simply added a note that explains Defense Department policy hasn't changed.

You can no longer believe what you see. Deepfakes, which use artificial intelligence to make people appear to say and do things in videos that they haven't said or done, have been growing more realistic at an alarming rate. And it's a matter of time before they're used to try to circumvent cybersecurity. Steve Grobman, chief technology officer at cybersecurity firm McAfee, and Celeste Fralick, chief data scientist, warned in a keynote speech at the RSA security conference in San Francisco that the tech has reached the point where you can barely tell with the naked eye whether a video is fake or real. They showed a video where Fralick's words were coming out of a video of Grobman's face, even though Grobman never said those words.

Before executing an attack, adversaries usually explore the victim's network in an attempt to infer the network topology and identify vulnerabilities in the victim's servers and personal computers. Falsifying the information collected by the adversary post penetration may significantly slower lateral movement and increase the amount of noise generated within the victim's network. We investigate the effect of fake vulnerabilities within a real enterprise network on the attacker performance. We use the attack graphs to model the path of an attacker making its way towards a target in a given network. We use combinatorial optimization in order to find the optimal assignments of fake vulnerabilities. We demonstrate the feasibility of our deception-based defense by presenting results of experiments with a large scale real network. We show that adding fake vulnerabilities forces the adversary to invest a significant amount of effort, in terms of time and exploitability cost.

In recent years, we have been faced with a series of natural disasters causing a tremendous amount of financial, environmental, and human losses. The unpredictable nature of natural disasters' behavior makes it hard to have a comprehensive situational awareness (SA) to support disaster management. Using opinion surveys is a traditional approach to analyze public concerns during natural disasters; however, this approach is limited, expensive, and time-consuming. Luckily the advent of social media has provided scholars with an alternative means of analyzing public concerns. Social media enable users (people) to freely communicate their opinions and disperse information regarding current events including natural disasters. This research emphasizes the value of social media analysis and proposes an analytical framework: Twitter Situational Awareness (TwiSA). This framework uses text mining methods including sentiment analysis and topic modeling to create a better SA for disaster preparedness, response, and recovery. TwiSA has also effectively deployed on a large number of tweets and tracks the negative concerns of people during the 2015 South Carolina flood.