Useful quantum computers are closer to becoming a reality as some of the world's biggest corporations try to bring the technology from the lab into the practical world. A quantum computer utilizes subatomic particles called qubits to speed up the solving of complex computations. Near-term expectations for quantum computers range from solving optimization problems to quantum-encrypted communications, and more. With the help of CB Insights' investment, acquisition, and partnership data, we identified 18 corporate groups involved in the development of commercialized quantum computing hardware and software. They are a diverse group of players, ranging from tech industry behemoths to defense contractors to national telecommunications companies.

Participants in Europe's largest technology conference next week will swap bracing, Guinness-fueled pub crawls by the River Liffey for some hilly, "Gothic grit and glamour" by the beach in mainland Europe's westernmost capital. The Web Summit, held in Dublin since its founding in 2010, opens in Lisbon on Monday and runs through Thursday. Often described as a "Davos for Geeks," the brainchild of Irish entrepreneurs Paddy Cosgrave, David Kelly and Daire Hickey will bring 50,000 people from 165 countries to the Portuguese capital. They'll network and listen to speakers including the actor Joseph Gordon Levitt and Sean Rad of Tinder. About 15,000 companies will be represented and 7,000 CEOs and business leaders will attend including Cisco Systems' John Chambers and Renault-Nissan's Carlos Ghosn.

For more than a decade, Silicon Valley's technology investors and entrepreneurs obsessed over social media and mobile apps that helped people do things like find new friends, fetch a ride home or crowdsource a review of a product or a movie. Now Silicon Valley has found its next shiny new thing. And it does not have a "Like" button. The new era in Silicon Valley centers on artificial intelligence and robots, a transformation that many believe will have a payoff on the scale of the personal computing industry or the commercial internet, two previous generations that spread computing globally. Computers have begun to speak, listen and see, as well as sprout legs, wings and wheels to move unfettered in the world.

DeepMind, the British artificial intelligence company that's now a part of Google, stunned the world back in March when its AlphaGo AI defeated world-champion Go player Lee Se-dol to achieve one of the most sought-after feats in computer science. The ancient Chinese board game of Go was considered the most difficult "perfect information" game for computers to crack, as the dizzying complexity produced by its simple rules requires a highly advanced degree of intuition to play at a serious level, but AlphaGo managed it with a revolutionary system built on neural networks and machine learning. Now DeepMind is turning its attention to a game that will pose an even bigger challenge: StarCraft II. Blizzard Entertainment's real-time strategy hit is one of the most fiercely competitive games played professionally around the world, and the company is working together with DeepMind to release it as an AI research environment. The announcement is being made at Blizzard's annual conference, BlizzCon.

The US has confirmed the death of a senior al-Qaeda leader targeted by a US drone strike in north eastern Afghanistan last month. Farouq al-Qahtani, the group's leader in the area, was killed two weeks ago in what the Pentagon described as a precision strike. Saudi-born al-Qahtani was placed on a US list of most wanted terrorists in February. He was said to be one of al-Qaeda's senior plotters against the US. Saudi-born al-Qahtani, a Qatari national, was also accused of involvement in plots targeting Europe.

Position yourself for growth in 2017--join us live at the Entrepreneur 360 Conference in Long Beach, Calif. on Nov. 16. In the past decade, learning has gone from a passive to an active experience. Brick-and-mortar institutions are no longer required to continue education in a formal way. From highly effective homeschooling programs to online colleges, to continued education and online tools to enhance your professional career, the opportunities to expand your intelligence from your desk are abundant. One of the biggest technological advancements that has improved the quality of the learning tools we use is the aggregation and analysis of data.

We propose a novel training algorithm for reinforcement learning which combines the strength of deep Q-learning with a constrained optimization approach to tighten optimality and encourage faster reward propagation. Our novel technique makes deep reinforcement learning more practical by drastically reducing the training time. We evaluate the performance of our approach on the 49 games of the challenging Arcade Learning Environment, and report significant improvements in both training time and accuracy.

Nonconvex optimization problems such as the ones in training deep neural networks suffer from a phenomenon called saddle point proliferation. This means that there are a vast number of high error saddle points present in the loss function. Second order methods have been tremendously successful and widely adopted in the convex optimization community, while their usefulness in deep learning remains limited. This is due to two problems: computational complexity and the methods being driven towards the high error saddle points. We introduce a novel algorithm specially designed to solve these two issues, providing a crucial first step to take the widely known advantages of Newton's method to the nonconvex optimization community, especially in high dimensional settings.

Boosting is one of the most significant developments in machine learning. This paper studies the rate of convergence of $L_2$Boosting, which is tailored for regression, in a high-dimensional setting. Moreover, we introduce so-called \textquotedblleft post-Boosting\textquotedblright. This is a post-selection estimator which applies ordinary least squares to the variables selected in the first stage by $L_2$Boosting. Another variant is \textquotedblleft Orthogonal Boosting\textquotedblright\ where after each step an orthogonal projection is conducted. We show that both post-$L_2$Boosting and the orthogonal boosting achieve the same rate of convergence as LASSO in a sparse, high-dimensional setting. We show that the rate of convergence of the classical $L_2$Boosting depends on the design matrix described by a sparse eigenvalue constant. To show the latter results, we derive new approximation results for the pure greedy algorithm, based on analyzing the revisiting behavior of $L_2$Boosting. We also introduce feasible rules for early stopping, which can be easily implemented and used in applied work. Our results also allow a direct comparison between LASSO and boosting which has been missing from the literature. Finally, we present simulation studies and applications to illustrate the relevance of our theoretical results and to provide insights into the practical aspects of boosting. In these simulation studies, post-$L_2$Boosting clearly outperforms LASSO.

Back in 2014, a laser gun (the Laser Weapon System, or LaWS), went into service on the warship USS Ponce. Created as a defense system against drones, more laser weapons are making their way to the battlefield. But there's a problem: Their flying targets have begun to defend themselves. It was back in 1973 that an Air Force experimental laser first shot down a drone. Ever since, drone targets have been used to prove that laser weapons can effectively take down airborne objects.