For nearly a quarter of a century, this organization has championed a specific way of thinking about tomorrow. If it's true, as the writer William Gibson once had it, that the future is already here, just unevenly distributed, then our task has been to locate the places where various futures break through to our present and identify which one we hope for. Our founders--Louis Rossetto, Jane Metcalfe, and Kevin Kelly--all supported a strain of optimistic libertarianism native to Silicon Valley. The future they endorsed was the one they saw manifested in the early Internet: one where self-organizing networks would replace old hierarchies. To them, the US government was one of those kludgy, inefficient legacy systems that mainly just get in the way.

Denmark has a problem: it may soon be unable to afford offering such a good deal to its people. Free health care for all, a 757 monthly stipend for college students and robust safety nets for the less fortunate all cost money – Denmark devotes slightly more than 30 percent of its gross domestic product to social spending, one of the highest levels in the rich world. According to the Organisation for Economic Cooperation and Development, such a "generous welfare system requires robust public finances." And while Denmark's "seem sustainable" for now, the assumptions that underpin that view carry a high level of uncertainty, the OECD warned in a recent survey. True, an unemployment rate below 4 percent is one of Europe's lowest, and industry frequently complains about shortages in skilled labor.

SINGAPORE – Sherine Toh says her best days at work are when none of the 600 or so staff members at Singapore's Tung Lok Restaurants quits, though such days are rare. The Chinese restaurant group is one of the thousands of businesses struggling with a labor crunch caused by curbs on foreign workers that threaten the city-state's already feeble growth rates. "It has gotten much tougher compared to the old days, five years back," said Toh, who has at least 20 vacancies to fill at any one time as head of human resources. The group closed some outlets because of the shortage. The city's restaurants, hotels and retailers have become the biggest casualties of the labor crunch since Singapore accelerated restrictions on foreign workers in 2011 as political disquiet about immigration grew.

At Audi's most flexible plant, in Neckarsulm, Germany, the driver-side airbags are named after girls, while the steering wheels have boys' names. Other components are designated according to seasons or places: anything to make it easier for shop floor workers to remember when sorting and installing them. While a robot could read any of their 12-digit product identification codes in a microsecond, auto executives say the machines could not deal with the enormous complexity facing their human colleagues, who assemble six model lines and 21 vehicle derivatives at the plant. "Today it would be impossible to add all the sensors necessary to replace the human sense of touch," Audi production boss Hubert Waltl said. "People can reach into a box and without looking immediately recognize a larger part from a smaller one."

"…[W]e are suspicious of rapid cognition. We live in a world that assumes that the quality of a decision is directly related to the time and effort that went into making it." According to folk wisdom, the more time, information, and effort you put into a decision, the better it gets. In other words, "More is better." If you are a doctor making a diagnosis, more medical tests are always better.

Detroit got its first toehold in Startup Nation on Tuesday, after Ford Motor Co. said it was buying SAIPS, an Israeli company that develops technologies that are key to self-driving vehicles. The acquisition was one of four deals the U.S. carmaker announced with the aim of having a high-volume, fully autonomous vehicle in commercial operation in 2021 in a ride-hailing or ride-sharing service. Ford said the investment/collaboration with the four startups is part of its strategy of enhancing its autonomous vehicle development, which includes more than doubling its staff at its Palo Alto, California, research and development center. Ford said SAIPS, a computer vision and machine-learning startup, would strengthen its expertise in artificial intelligence and enhanced computer vision. The company provided no financial details about its acquisition, but industry sources estimated it at tens of millions of dollars.

So you know the Bayes rule. How does it relate to machine learning? It can be quite difficult to grasp how the puzzle pieces fit together - we know it took us a while. This article is an introduction we wish we had back then. While we have some grasp on the matter, we're not experts, so the following might contain inaccuracies or even outright errors. Feel free to point them out, either in the comments or privately.

Oxbotica, a company spun off from the Oxford University Mobile Robotics Group in the United Kingdom, has developed software that allows driverless vehicles to get around--even when there's no GPS to be found. The software relies on a unit within the vehicle that has two stereo cameras that look a bit like Wall-E eyes and two laser scanners like stubby antennae. These gather data about the vehicle's environment; the software then creates 3D point clouds to map everything around the vehicle. The software, called Selenium, was designed to work with any vehicle's sensors and safety systems. It can use the 3D maps it creates to perform tasks autonomously, like navigating and braking.

Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling language and a structured query language. The practical value is illustrated in two ways. First, CGPMs are used in an analysis that identifies satellite data records which probably violate Kepler's Third Law, by composing causal probabilistic programs with non-parametric Bayes in under 50 lines of probabilistic code. Second, for several representative data analysis tasks, we report on lines of code and accuracy measurements of various CGPMs, plus comparisons with standard baseline solutions from Python and MATLAB libraries.

When approximating a black-box function, sampling with active learning focussing on regions with non-linear responses tends to improve accuracy. We present the FLOLA-Voronoi method introduced previously for deterministic responses, and theoretically derive the impact of output uncertainty. The algorithm automatically puts more emphasis on exploration to provide more information to the models.