Retired U.S. Air Force Col. Gene Lee (seated) is operating a simulator developed by Nick Ernest (standing left) for military training and research. Ernest, a University of Cincinnati graduate and Psibernetix president and CEO, is standing next to David Carroll of Psibernetix. Gene Lee has decades of experience as a fighter pilot and aerial combat instructor. The talent Lee, a retired U.S. Air Force colonel, has as a pilot allowed him to easily and consistently defeat other computer programs used in research and training. That was until he began working with a system developed by a University of Cincinnati doctoral graduate in collaboration with the Air Force Research Laboratory based at Wright-Patterson Air Force Base.

There's been an election in Australia, so once more, with all the regularity of a cuckoo clock, politicians and pundits alike are proposing that electronic voting is the answer. So, here we go again, explaining why it's a bad idea. First, if e-voting is the answer, what is the actual question? "We're a grown-up democracy, it shouldn't take eight days to find out who's won," said Malcolm Turnbull, who is likely to continue as prime minister. "[E-voting is] something we must look at", he said.

Drone technology is developing so quickly--and morphing into commercial uses never before contemplated--that aviation regulators are having trouble keeping pace. Air-safety authorities on both sides of the Atlantic have acknowledged that traditional rule making is too slow and rigid to cope with the rapidly expanding applications of the flying machines, from bridge inspections to land surveys to news photography. And the pressure to spell out exactly what's allowed and what isn't is growing as the industry booms. Millions of hobbyists already operate drones, and over the next few years businesses are projected to begin flying millions more in the U.S. alone. Now regulators are scrambling to draft new, more-nimble rules and procedures.

We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) potential misspecification, overfitting, and otherwise bias-prone or inefficient use of a large identifying covariate set in the estimation of causal effects. Our method targets the analysis toward a well defined ``retrospective intervention effect'' (RIE) based on hypothetical population interventions and applies a machine learning ensemble that allows data to guide us, in a controlled fashion, on how to use a large identifying covariate set. We illustrate with an analysis of policy options for reducing ex-combatant recidivism in Colombia.

Artificial intelligence (AI) is intelligence created by machines or software. The artificial intelligence and robotics market was worth US 10.7 billion in 2014 and is expected to be worth US 153 billion by 2020, and to have a disruptive impact of between US 14 to US 33 trillion. The component for artificial intelligence alone is worth US 70 billion. There are perils with artificial intelligence. Don't let anyone tell you there aren't.

The suspect in the deadly attack on Dallas police taunted authorities during two hours of negotiations, laughing at them, singing and at one point asking how many officers he had shot, the police chief said Sunday. Micah Johnson, a black Army veteran, insisted on speaking with a black negotiator and wrote in blood on the wall of a parking garage where police cornered and later killed him, David Brown told CNN's State of the Union. Johnson, who was apparently wounded in a shootout with police, wrote the letters "RB" and other markings, but the meaning was unclear. Investigators are trying to decipher the writing by looking through evidence from Johnson's suburban Dallas home, Brown said. The chief defended the decision to kill Johnson with a bomb delivered by remote-controlled robot, saying negotiations went nowhere and that officers could not approach him without putting themselves in danger.

Nobel economics laureate and psychologist Daniel Kahneman -- considered the father of behavioral economics – retired from his teaching position at Princeton a few years ago to co-found a consulting firm in New York. In a talk at the recent Wharton People Analytics Conference, he said of his consulting experience that he had "expected to be awed" by the quality of the decision-making in organizations "that need to make profits to survive in a competitive world." "You look at large organizations that are supposed to be optimal, rational. And the amount of folly in the way these places are run, the stupid procedures that they have, the really, really poor thinking you see all around you, is actually fairly troubling," he said, noting that there is much that could be improved. Figuring out how to make the act of decision-making "commensurate with the complexity and importance of the stakes" is a huge problem, in Kahneman's view, to which the business world does not devote much thought.

By: HQAnon / (AnonHQ) The evolution of humanoid robots is well into the concerning stage at this point. DARPA's latest incarnation of its Atlas robot is seen in the following video beginning to walk at a pace with a sense of balance equal to most humans. Strangely, toward the end of the video, it is being "abused" by its human handler, which begs the question if a true artificial intelligence is permitted to flourish in this robot, if it might strike back at some point. At the very least, this robot's demonstration of dexterity in the warehouse is likely to threaten humans economically as humans continue to be outsourced to machine labor at record levels. But it's the latest humanoid robot from Hanson Robotics that might further heighten the level of concern.

DeepMind is an artificial intelligence lab in London that creates what are known as general purpose self-learning algorithms. The company, acquired by Google in 2014 for a reported 400 million, is best-known for creating software „agents" that have mastered games like…

DeepMind is an artificial intelligence lab in London that creates what are known as general purpose self-learning algorithms. The company, acquired by Google in 2014 for a reported 400 million, is best-known for creating software "agents" that have mastered games like Go and Space Invaders but it also wants to apply its technology to healthcare. Mustafa Suleyman, DeepMind cofounder and head of DeepMind Health, gave a talk at the King's Fund in London this week where he explained how the company is working with the NHS and what kind of benefits patients can expect to see in the long run. The company operates independently of Google and creates software that can think for itself. In order to create this kind of AI software, DeepMind draws on huge data sets that can help to teach DeepMind's AI how to perform certain tasks.