Shot down: A retired Air Force colonel takes a hit in the simulator from the ALPHA AI warplane. Every time artificial intelligence (AI) beats a human in something, the world cries "Skynet" and assumes the robot takeover is about to happen. Whereas we definitely don't have to worry about that happening anytime soon, a new AI has wandered onto the stage and, for once, a Terminator film series-inspired machine revolution appears to be somewhat possible. Writing in the Journal of Defense Management, a team of researchers describe how their AI – dubbed "ALPHA" – has recently bested its most formidable opponent, retired United States Air Force Colonel Gene Lee, in a series of war games. Specifically, this AI was designed to engage in simulated air combat against human opponents, and this particular ranking officer has considerable aerial combat experience.

Since taking office, President Obama has sent U.S. troops into action on land or in the skies of seven countries on two continents. Obama's administration has authorized Navy SEALs to kill Osama bin Laden in Pakistan and approved the fatal drone strike on an American cleric in Yemen. Here is a look at targeted killings under the Obama administration. Mansour was killed when a drone strike hit his vehicle as he traveled in Baluchistan, Pakistan. Mansour, known for his mercurial leadership, had been in the U.S. military's crosshairs for years.

We are interested in estimating individual labels given only coarse, aggregated signal over the data points. In our setting, we receive sets ("bags") of unlabeled instances with constraints on label proportions. We relax the unrealistic assumption of known label proportions, made in previous work; instead, we assume only to have upper and lower bounds, and constraints on bag differences. We motivate the problem, propose an intuitive formulation and algorithm, and apply our methods to real-world scenarios. Across several domains, we show how using only proportion constraints and no labeled examples, we can achieve surprisingly high accuracy. In particular, we demonstrate how to predict income level using rough stereotypes and how to perform sentiment analysis using very little information. We also apply our method to guide exploratory analysis, recovering geographical differences in twitter dialect.

The aggregation and denoising of crowd labeled data is a task that has gained increased significance with the advent of crowdsourcing platforms and massive datasets. In this paper, we propose a permutation-based model for crowd labeled data that is a significant generalization of the common Dawid-Skene model, and introduce a new error metric by which to compare different estimators. Working in a high-dimensional non-asymptotic framework that allows both the number of workers and tasks to scale, we derive optimal rates of convergence for the permutation-based model. We show that the permutation-based model offers significant robustness in estimation due to its richness, while surprisingly incurring only a small additional statistical penalty as compared to the Dawid-Skene model. Finally, we propose a computationally-efficient method, called the OBI-WAN estimator, that is uniformly optimal over a class intermediate between the permutation-based and the Dawid-Skene models, and is uniformly consistent over the entire permutation-based model class. In contrast, the guarantees for estimators available in prior literature are sub-optimal over the original Dawid-Skene model.

Even top guns do not have the ability to predict poor weather or foresee obstacles in their flight path. But a new helmet-mounted display could be a pilot's'eye in the sky', as it has the ability to see through even the thickest fog and create digital images of on coming obstacles The technology combines terrain information and sensor readings to show speed, altitude and position - all displayed alongside digitally outlines of the landscape in front of the pilot's line of sight. Using augmented reality, researcher developed a system that combines terrain information and sensor readings to display speed, altitude and position alongside digital images of any obstacles. All of the data from outside is processed aboard the aircraft and projected directly to the see-through head-mounted display. This gives the pilot an ability to see obstacles with his own eyes and also view digitally generated outlines of the landscape and other potential obstacles.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

As yet another deadly attack on Tuesday tore through Turkey's biggest airport, Mark Cuban is demanding action. The billionaire businessman, appearing on Megyn Kelly's Fox Business show the same night, called for both presumptive presidential candidates to do more than just say'this is bad.' Cuban proposed that the country look to technological innovations for a solution. "How do you deal with immigration? How do you deal with radical Islamic terrorists trying to come into the country? You're going to need to use tech," said Cuban, in a Bloomberg interview earlier this week. Both the Republican and Democrat would-be nominees are technologically illiterate, he said to Bloomberg's Cory Johnson, which is the reason they are blind to technological solutions to the country's biggest issues.

In the future, the US Air Force hopes to have armed drones flying in formation with human pilots, responding to their verbal and digital commands to fight the enemy and strike targets. That would require an artificial intelligence capable of interpreting commands and applying knowledge of combat tactics--something that is already being proven in a project funded by the Air Force Research Lab. ALPHA, an artificial intelligence trained by a retired Air Force expert in air combat, was originally developed as what amounts to ultimate video game AI--an autonomous simulated enemy for use in training fighter pilots. The AI is so good that it has consistently beaten human pilots in simulated air combat--even when heavily handicapped by simulated physics. And now AFRL is investigating using ALPHA as the AI for Unmanned Combat Aerial Vehicles (UCAVs) in the physical world, potentially flying missions alongside human pilots.

We wish we could say you look good for 60 years old, but real talk: You do not. Sure, you've grown--when President Eisenhower authorized you in 1956, you were just a glimmer of asphalt. Look at you now! Fully 47,662 miles of roads, bridges, ramps, and curves, the meshwork that defined American post-war expansion and exceptionalism. The Department of Transportation estimates that by 2030, you might have an annual 86 billion funding gap--and that's just to keep your flabby highways and bridges functioning. Actually improving the darn things could cost up 150 billion per year.

In June of 1975, the Office of the White House Press Secretary announced President Gerald R. Ford's picks for the National Medal of Science. One went to the Austrian-born mathematician and logician Kurt Gödel. Nicknamed Mr. Why by his parents, Gödel was known to a subset of his constituents as, simply, God. He received fan mail from all over the world, archiving it into files of "autograph requests," "inquiries from students and amateurs," "letters of appreciation," and "crank correspondence." A self-described "dunce fool of Mathematics" in West Bengal wrote seeking Gödel's "Guruship," and a svelte math teacher in California confessed that she'd taken the liberty of enlarging a photo of Gödel to make a poster for her classroom.