When 10-20% of vehicles are hacked, clusters of roads become inaccessible from each other. Even a small scale hack of automated cars could cause collisions and gridlock in Manhattan, hindering emergency services, according to the latest research. Researchers at Georgia Tech and Multiscale Systems Inc. investigated the'cyber-physical' risks of hacked Internet-connected vehicles, and this week will present their results to the 2019 American Physical Society March Meeting in Boston. The rise of connected cars, and the predicted future of automated cars have for some time being worrying regulators. However, until now most of the focus has been on preventing individual accidents, such as when a pedestrian was killed by a self-driving Uber in Arizona in 2018.

Human-robot interaction is easy to do badly, and very difficult to do well. One approach that has worked well for robots from R2-D2 to Kuri is to avoid the problem of language--rather than use real words to communicate with humans, you can do pretty well (on an emotional level, at least) with a variety of bleeps and bloops. But as anyone who's watched Star Wars knows, R2-D2 really has a lot going on with the noises that it makes, and those noises were carefully designed to be both expressive and responsive. Most actual robots don't have the luxury of a professional sound team (and as much post-production editing as you need), so the question becomes how to teach a robot to make the right noises at the right times. At Georgia Tech's Center for Music Technology (GTCMT), Gil Weinberg and his students have a lot of experience with robots that make noise of various sorts, and they've used a new deep learning-based technique to teach their musical robot Shimi a basic understanding of human emotions, and how to communicate back to those humans in just the right way, using music.

Tom is an analyst at the US Department of Defense (DoD).1 All day long, he and his team collect and process massive amounts of data from a variety of sources--weather data from the National Weather Service, traffic information from the US Department of Transportation, military troop movements, public website comments, and social media posts--to assess potential threats and inform mission planning. While some of the information Tom's group collects is structured and can be categorized easily (such as tropical storms in progress or active military engagements), the vast majority is simply unstructured text, including social media conversations, comments on public websites, and narrative reports filed by field agents. Because the data is unstructured, it's difficult to find patterns and draw meaningful conclusions. Tom and his team spend much of their day poring over paper and digital documents to detect trends, patterns, and activity that could raise red flags. In response to these kinds of challenges, DoD's Defense Advanced Research Projects Agency (DARPA) recently created the Deep Exploration and Filtering of Text (DEFT) program, which uses natural language processing (NLP), a form of artificial intelligence, to automatically extract relevant information and help analysts derive actionable insights from it.2 Across government, whether in defense, transportation, human services, public safety, or health care, agencies struggle with a similar problem--making sense out of huge volumes of unstructured text to inform decisions, improve services, and save lives.

Machine learning is one of those buzz words that gets thrown around as a synonym for AI (Artificial Intelligence). But this really is not accurate. Note that machine learning is a subset of AI. This field has also been around for quite some time, with the roots going back to the late 1950s. It was during this period that IBM's Arthur L. Samuel created the first machine learning application, which played chess. So how was this different from any other program?

SINGAPORE (Reuters) - China's Xinhua state news agency on Sunday used a lifelike robotic news anchor that mimics human facial expressions and mannerisms to present a story about delegates attending an annual parliament meeting arriving in Beijing.

Researchers at the Georgia Institute of Technology found that state-of-the-art object recognition systems are less accurate at detecting pedestrians with darker skin tones. Crash-testing: The researchers tested eight image-recognition systems (each trained on a standard data set) against a large pool of pedestrian images. They divided the pedestrians into two groups for lighter and darker skin tones according to the Fitzpatrick skin type scale, a way of classifying human skin color. Color coded: The detection accuracy of the systems was found to be lower by an average of five percentage points for the group with darker skin. This held true even when controlling for time of day and obstructed view.

There are already networks in place that can detect seismic activity and send an alert as soon as an earthquake is underway. But the current technology doesn't actually send the alert until all of the sensors in the network covering a given area have detected seismic waves. And it could take about a minute from the moment activity is initially detected until an alert hits the wire. A minute is a long time in an emergency. Government agencies, public works, and local utilities ideally need to alert the populace and do things like halt trains and shut off power lines to potentially mitigate damage – every second counts.

Intel has done pretty well for itself by consistently figuring out ways of making CPUs faster and more efficient. But with the end of Moore's Law lurking on the horizon, Intel has been exploring ways of extending computing with innovative new architectures at Intel Labs. Quantum computing is one of these initiatives, and Intel Labs has been testing its own 49-qubit processors. Beyond that, Intel Labs is exploring neuromorphic computing (emulating the structure and, hopefully, some of the functionality of the human brain with artificial neural networks) as well as probabilistic computing, which is intended to help address the need to quantify uncertainty in artificial intelligence applications. Rich Uhlig has been the director of Intel Labs since December of 2018, which is really not all that long, but he's been at Intel since 1996 (most recently as Director of Systems and Software Research for Intel Labs) so he seems well qualified to hit the ground running.

The killer machines are coming. Robotic weapons that target and destroy without human supervision are poised to start a revolution in warfare comparable to the invention of gunpowder or the atomic bomb.

A link has been posted to your Facebook feed. BMW and the maker of Mercedes-Benz have reached a deal to collaborate on the development of self-driving car technology. The partnership between BMW and Daimler is a tectonic shift for the rival German luxury automakers, reflecting their need to collaborate on extremely expensive and challenging autonomous vehicle systems. The companies had already formed a joint venture to collaborate on "mobility services," such as car sharing and ride-hailing services. Taken together, these moves suggest that you could one day share a ride in a car jointly produced by two companies whose history of fierce competition is akin to the rivalry between American automakers Ford and General Motors.