Discrete combinatorial optimization plays a central role in many scientific disciplines, however for hard problems we lack linear time algorithms that would allow us to solve very large instances. Moreover it is still unclear what are the key features that make a discrete combinatorial optimization problem hard to solve. Here we study random K-satisfiability problems with K 3, 4 which are known to be very hard close to the SAT-UNSAT threshold, where problems stop having solutions. We show that the Backtracking Survey Propagation algorithm, in a time practically linear in the problem size, is able to find solutions very close to the threshold, in a region unreachable by any other algorithm. All solutions found have no frozen variables, thus supporting the conjecture that only unfrozen solutions can be found in linear time, and that a problem becomes impossibile to solve in linear time when all solutions contain frozen variables. Optimization problems with discrete variables are widespread among scientific disciplines and often among the hardest to solve.

Dean Pomerleau can still remember his first tussle with the black-box problem. The year was 1991, and he was making a pioneering attempt to do something that has now become commonplace in autonomous-vehicle research: teach a computer how to drive. This meant taking the wheel of a specially equipped Humvee military vehicle and guiding it through city streets, says Pomerleau, who was then a robotics graduate student at Carnegie Mellon University in Pittsburgh, Pennsylvania. With him in the Humvee was a computer that he had programmed to peer through a camera, interpret what was happening out on the road and memorize every move that he made in response. Eventually, Pomerleau hoped, the machine would make enough associations to steer on its own.

Miniature robots that doctors could guide through a patient's body to kill cancer cells are closer to reality thanks to winners of this year's Nobel Prize for Chemistry. Three winners share the 727,000 prize for developing nanoscale machines--1000th the width of a human hair--that pave the way for applications in medicine, computing and engineering. The winners were Jean-Pierre Sauvage of the University of Strasbourg in France, Fraser Stoddart of Northwestern University in Illinois, USA, and Bernard Feringa of the University of Groningen in the Netherlands. Each devised different groups of molecules with moving parts that they could control remotely, despite their tiny size. "It's early days, but once you can control movement, you have many possibilities," said Feringa, interviewed after receiving notification of the prize.

PONG is a gloriously simple video game: you control one paddle, aiming to bounce the ball past your opponent's paddle. Artificial intelligence has learned to play it so well that it can easily beat human players. But try to get the same AI to play Breakout, a very similar paddle-based game, and it is utterly stumped. It can't reuse what it has learned about paddles and balls from Pong, and has to learn to play from scratch. Computers can learn without our guidance, but the knowledge they acquire is meaningless beyond the problem they are set.

The 2016 Nobel Prize in chemistry has been awarded for the design and synthesis of the world's smallest machines. The work has overtones of science fiction, but holds huge promise in fields as diverse as medicine, materials and energy. This is especially true of efforts to develop nano-scale machines (1,000 times smaller than the width of a human hair), which are always destined to remain tiny however big our ambitions for them grow. It's difficult to trace the development of molecular machines to one person or scientific step. But a 1959 lecture by the celebrated physicist Richard Feynman is as good a point as any.

Machine learning, which involves programs that get more accurate with experience, is fundamentally different from any kind of computing that's come before. "There's always been a simple division of labor: machines do number crunching, and humans make decisions," says Pradeep Dubey, an Intel Fellow at the company's Intel Labs division. Machine-learning programs--and in particular the high-profile deep-learning subset that can teach themselves--are different. These programs have the potential to discover new drug compounds or identify consumer trends without human intervention. For Dubey and others at Intel, it was clear that they needed to find a way to make machine-learning programs work well on Intel's architecture.

Source(d), a Madrid, Spain-based startup that uses artifical intelligence to identify the most suitable developers for respective jobs, raised 6m in funding. Backers included Xavier Niel, Otium Venture and Sunstone Capital. The company intends to use the funds for commercial expansion in Europe and the United States and continued improvement of the artificial intelligence algorithms. Founded in March of 2015 by CEO Eiso Kant, Jorge Schnura and Philip von Have, source{d} develops deep learning (artificial intelligence) algorithms to identify, qualify and present developers to companies, by analyzing their code. By basing their approach on technical understanding, the process guarantees that the needs of their clients are met and the developers fit better with their technical challenge.

Nick Bostrom: London's DeepMind is winning the global race to develop human-level artificial intelligence (businessinsider.com) This is the best tl;dr I could make, original reduced by 75%. Nick Bostrom, one of the leading voices on artificial intelligence, has singled out London research lab DeepMind as the company closest to developing a system that can mimic human-level artificial intelligence - a target widely shared by those at the forefront of the AI industry. When asked who was leading the global AI race, Bostrom immediately responded with DeepMind. "Right now, I think here in London we have the DeepMind group who are, I think, the biggest [group] specifically focused on solving general intelligence," Bostrom told Business Insider at a breakfast meeting aboard the Sunbourn Yacht Hotel in East London on Wednesday.

Last week IBM announced they had entered into a definitive agreement to acquire strategic consultancy Promontory Financial Group. Promontory is not exactly a household name, but they are one of the very top tier of global banking consultancies, with a particular focus on regulation and compliance. IBM cite a McKinsey figure of 270 Billion costs related to compliance, which consists of direct compliance spend of 99 Billion, with the remainder accounted for with other forms of compliance leakage (fines, damage to goodwill etc.). For clarity we must state that there is no public data available for this figure, but it seems in-line with other research that is available. In many ways the figures are somewhat irrelevant, if we look at, for example, the destruction to shareholder value that can occur when a financial institution is not in compliance with relevant rules and regulations.

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