We learn the structure of a Markov Network between two groups of random variables from joint observations. Since modelling and learning the full MN structure may be hard, learning the links between two groups directly may be a preferable option. We introduce a novel concept called the \emph{partitioned ratio} whose factorization directly associates with the Markovian properties of random variables across two groups. A simple one-shot convex optimization procedure is proposed for learning the \emph{sparse} factorizations of the partitioned ratio and it is theoretically guaranteed to recover the correct inter-group structure under mild conditions. The performance of the proposed method is experimentally compared with the state of the art MN structure learning methods using ROC curves. Real applications on analyzing bipartisanship in US congress and pairwise DNA/time-series alignments are also reported.

Any-angle pathfinding is a fundamental problem in robotics and computer games. The goal is to find a shortest path between a pair of points on a grid map such that the path is not artificially constrained to the points of the grid. Prior research has focused on approximate online solutions. A number of exact methods exist but they all require super-linear space and pre-processing time. In this study, we describe Anya: a new and optimal any-angle pathfinding algorithm. Where other works find approximate any-angle paths by searching over individual points from the grid, Anya finds optimal paths by searching over sets of states represented as intervals. Each interval is identified on-the-fly. From each interval Anya selects a single representative point that it uses to compute an admissible cost estimate for the entire set. Anya always returns an optimal path if one exists. Moreover it does so without any offline pre-processing or the introduction of additional memory overheads. In a range of empirical comparisons we show that Anya is competitive with several recent (sub-optimal) online and pre-processing based techniques and is up to an order of magnitude faster than the most common benchmark algorithm, a grid-based implementation of A*.

The goal of subspace learning is to find a $k$-dimensional subspace of $\mathbb{R}^d$, such that the expected squared distance between instance vectors and the subspace is as small as possible. In this paper we study subspace learning in a partial information setting, in which the learner can only observe $r \le d$ attributes from each instance vector. We propose several efficient algorithms for this task, and analyze their sample complexity

We propose a novel Riemannian manifold preconditioning approach for the tensor completion problem with rank constraint. A novel Riemannian metric or inner product is proposed that exploits the least-squares structure of the cost function and takes into account the structured symmetry that exists in Tucker decomposition. The specific metric allows to use the versatile framework of Riemannian optimization on quotient manifolds to develop preconditioned nonlinear conjugate gradient and stochastic gradient descent algorithms for batch and online setups, respectively. Concrete matrix representations of various optimization-related ingredients are listed. Numerical comparisons suggest that our proposed algorithms robustly outperform state-of-the-art algorithms across different synthetic and real-world datasets.

In his book The Singularity Is Near, American computer scientist Ray Kurzweil had predicted a decade ago that by 2045 non-biological intelligence will have exceeded biological intelligence on Earth due to exponential changes in infotech, biotech and nanotech. Basically, man and machine will become one. But Murray Shanahan, a London-based cognitive robotics professor, disagrees with Kurzweil's theory in his more recent book, Technological Singularity. "Kurzweil was very precise (about time)," Shanahan tells China Daily in an interview in Beijing. "Technological singularity has a very dramatic impact on humanity."

Although Foxconn confirmed to the BBC that it was working to automate much of its manufacturing operations, the company denied that the new robotic assembly line would mean fewer jobs for humans. Instead, the company says it is simply using the machines to "replace repetitive tasks previously done by employees" while allowing those employees to focus on more valuable parts of the manufacturing process like R&D and quality control. "We will continue to harness automation and manpower in our manufacturing operations," Foxconn told the BBC, "and we expect to maintain our significant workforce in China." Meanwhile, the South China Morning Post also reports that 35 Taiwanese companies including Foxconn have spent a total of 4 billion yuan (or about 609 million USD) on artificial intelligence last year. Many of those companies employ tens of thousands in Kunshan, where two-thirds of the 2.5 million people are migrant workers. According to a government survey, 600 companies in Kunshan plan to follow Foxconn's lead.

Apple and Samsung supplier Foxconn has reportedly replaced 60,000 factory workers with robots. One factory has "reduced employee strength from 110,000 to 50,000 thanks to the introduction of robots", a government official told the South China Morning Post. Xu Yulian, head of publicity for the Kunshan region, added: "More companies are likely to follow suit." In a statement to the BBC, Foxconn Technology Group confirmed that it was automating "many of the manufacturing tasks associated with our operations" but denied that it meant long-term job losses. "We are applying robotics engineering and other innovative manufacturing technologies to replace repetitive tasks previously done by employees, and through training, also enable our employees to focus on higher value-added elements in the manufacturing process, such as research and development, process control and quality control.

Affectiva, a startup developing "emotion recognition technology" that can read people's moods from their facial expressions captured in digital videos, raised 14 million in a Series D round of funding led by Fenox Venture Capital. According to co-founder Rana el Kaliouby, the Waltham, Mass.-based company wants its technology to become the de facto means of adding emotional intelligence and empathy to any interactive product, and the best way for organizations to attain unvarnished insights about customers, patients or constituents. She explained that Affectiva uses computer vision and deep learning technology to analyze facial expressions or non-verbal cues in visual content online, but not the language or conversations in a video. The company's technology ingests digital images--including video in chat applications, live-streamed or recorded videos, or even GIFs--through simple web cams typically. Its system first categorizes then maps the facial expressions to a number of emotional states, like happy, sad, nervous, interested or surprised.

They say that kids aren't born sexist or racist--hate is taught. Artificial intelligence is the same way, and humans are fabulous teachers. ProPublica reported, for example, that an algorithm used to to predict the likelihood of convicts committing future crime tends to tag black folks as higher risk than whites. Despite the oft-repeated claim that such data-driven approaches are more objective than past methods of determining the risk of recidivism or anything else, it's clear that our very human biases have rubbed off on our machines. Consider the case of Microsoft's simple Tay bot, which sucked up all the slurs and racist opinions that Twitter users threw at it and ended up spouting Nazi drivel.

A little-known extremist cleric was chosen Wednesday to be the new leader of the Afghan Taliban, just days after a U.S. drone strike killed his predecessor. But within hours of the Taliban's announcement that the group's council of leaders had unanimously selected Mullah Haibatullah Akhundzada, opposition to him emerged -- a sign that rifts within the insurgency could widen and possibly drive the Taliban further from peace talks with the government of Afghanistan. The Taliban called on all Muslims to support Akhundzada as a matter of religious obligation and declared three days of official mourning for Mullah Mohammed Akhtar Mansour, who was slain Saturday by a U.S. drone in Pakistan. The announcement came as a suicide bomber struck a minibus carrying court employees in Kabul, killing at least 11 people, an official said. The Taliban promptly claimed responsibility for the attack.