A stunning video released by the Department of Defense shows a swarm of micro-drones being released by fighter jets. The demonstration of the Perdix micro-UAV swarm was held at China Lake, Calif. on Oct. 26, 2016. Three F/A-18's were deployed to launch the swarm of tiny drones, according to the Department of Defense. The drones, which have a wingspan of 12 inches, can operate autonomously and share a distributed brain, the BBC reports, and could be used for surveillance. In the Department of Defense video, the drones can also be seen flying through the air like a swarm of high-tech bees.

Crowdsourcing systems are popular for solving large-scale labelling tasks with low-paid workers. We study the problem of recovering the true labels from the possibly erroneous crowdsourced labels under the popular Dawid-Skene model. To address this inference problem, several algorithms have recently been proposed, but the best known guarantee is still significantly larger than the fundamental limit. We close this gap by introducing a tighter lower bound on the fundamental limit and proving that Belief Propagation (BP) exactly matches this lower bound. The guaranteed optimality of BP is the strongest in the sense that it is information-theoretically impossible for any other algorithm to correctly label a larger fraction of the tasks. Experimental results suggest that BP is close to optimal for all regimes considered and improves upon competing state-of-the-art algorithms.

This paper studies the problem of multivariate linear regression where a portion of the observations is grossly corrupted or is missing, and the magnitudes and locations of such occurrences are unknown in priori. To deal with this problem, we propose a new approach by explicitly consider the error source as well as its sparseness nature. An interesting property of our approach lies in its ability of allowing individual regression output elements or tasks to possess their unique noise levels. Moreover, despite working with a non-smooth optimization problem, our approach still guarantees to converge to its optimal solution. Experiments on synthetic data demonstrate the competitiveness of our approach compared with existing multivariate regression models. In addition, empirically our approach has been validated with very promising results on two exemplar real-world applications: The first concerns the prediction of \textit{Big-Five} personality based on user behaviors at social network sites (SNSs), while the second is 3D human hand pose estimation from depth images. The implementation of our approach and comparison methods as well as the involved datasets are made publicly available in support of the open-source and reproducible research initiatives.

A few months into my time as an insurance claims adjuster, a customer called and said he thought his house was breaking apart. He'd heard what sounded like wood beams snapping in the basement, so he went downstairs and crept into the crawl space to investigate. While he was lying in the dark surrounded by cement, he told me, he started to panic. It brought him back to years earlier when, as a firefighter with the New York City Fire Department, he served as a first responder on September 11, crawling through the giant blocks of brick and mortar that had collapsed hours earlier. This revelation came 10 or 15 minutes into our conversation, after I'd gathered his basic information and logged the details of the case.

This one is simple, it's when you completely drive yourself. Cars that we mostly drive today belong here, those are the ones that have anti-lock brakes and cruise-control, so they can take over some non-vital processes involved in driving. When the system can take over control in some specific use cases but driver still has to monitor system all the time is here, it's applicable to situations when the car is self-driving the highway and you just sit there and expect it to behave well. This level means that driver doesn't have to monitor the system all the time but has to be in a position where the control can quickly be resumed by a human operator. That means no need to have hands on a steering wheel but you have to jump in at the sounds of the emergency situation, which system can recognize efficiently. When your car drives you to the parking lot you get to the level four, when there is no need for a human operator for a specific use case or a part of a journey.

If you can't find Intel's Joule developer boards in your country, it's because shipments have been held up. Intel's Joule 570x and 550x are powerful computer boards that can be built as a PC, or be used to build robots, drones, or smart devices. But Intel is now seeking government certification so the boards can be cleared for shipment in those countries. Joule shipments have currently been blocked in a number of countries, including Taiwan, Japan, and Israel, all of which have active technology markets where hobbyists design hardware. Users that have ordered Joule boards from retailers abroad can't receive shipments in the blocked countries.

Nier: Automata should be on your games-to-watch list for 2017. I played the Nier: Automata demo the other day and was immediately smitten. The demo is a little over 30 minutes long, but in that half hour or so is a wealth of striking gameplay, clever use of perspective, and surprisingly deep world-building. Indeed, when it came to the game's terrific combat I only barely scratched the surface, largely button-mashing my way through until I began to figure out the more involved systems of combos and special moves available. As the human-looking robot 2B, you fight your way through an industrial area filled with hostile machines.

Edited by Michael F. Goodchild, University of California, Santa Barbara, CA, and approved November 22, 2016 (received for review July 20, 2016) Ride-sharing services can provide not only a very personalized mobility experience but also ensure efficiency and sustainability via large-scale ride pooling. Large-scale ride-sharing requires mathematical models and algorithms that can match large groups of riders to a fleet of shared vehicles in real time, a task not fully addressed by current solutions. We present a highly scalable anytime optimal algorithm and experimentally validate its performance using New York City taxi data and a shared vehicle fleet with passenger capacities of up to ten. Our results show that 2,000 vehicles (15% of the taxi fleet) of capacity 10 or 3,000 of capacity 4 can serve 98% of the demand within a mean waiting time of 2.8 min and mean trip delay of 3.5 min. Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics).

Artificial Intelligence is a buzzword that is impossible to ignore in the world of technology. However, not all talk about the area has been positive. Many believe that, in the foreseeable future, AI computer systems will match human intelligence and may even be better at certain tasks. This introduces the fear that the technology could even replace mankind. While there's certainly no denying that AI is a force to be reckoned with, we're still relatively far from this outcome.

Whether they're in marketing or some other discipline, it's odds-on that when artificial intelligence in the workplace is mentioned to somebody, one of the first thoughts to leap into his or her head is, "Will it take away my job?" And people have been asking it for a really long time. The tension between machine learning and human intellect has been routinely reflected in popular media: In our past two entries in this series, we did call-outs to an Avengers movie, Star Trek and Mad Men for their takes on AI as a nefarious change agent who'll suck the humanism out of our lives. But as far back as the 1950s, the notion that computers might supplant us in the workplace was very much on people's minds. So much so it was a comic premise of a 1957 Tracy-Hepburn comedy, Desk Set.