By now, the fact that transatlantic democratic capitalism, once the engine of postwar prosperity, has run into trouble can hardly be denied by anyone with the courage to browse a daily newspaper. Hunger, homelessness, toxic chemicals in the water supply, the lack of affordable housing: all these issues are back on the agenda, even in the most prosperous of countries. This appalling decline in living standards was some time in the making – 40 years of neoliberal policies are finally taking their toll – so it shouldn't come as a shock. However, coupled with the spillover effects of wars in the Middle East – first the refugees, now the increasingly regular terrorist attacks in the heart of Europe – our economic and political malaise looks much more ominous. It's hardly surprising that the insurgent populist forces, on both left and right, have such an easy time bashing the elites.

Robotics company Boston Dynamics released a new video showcasing its upgraded Atlas robot, and the footage features a slew of impressive (and somewhat unsettling) new capabilities. The humanoid Atlas robot, which has been overhauled with a sleeker design, can be seen at the beginning of the video walking around untethered before it opens the front door to Boston Dynamics' office and steps outside. The bot is then seen walking on uneven and snowy terrain, maneuvering around trees and correcting its balance several times. The new-and-improved robot is "designed to operate outdoors and inside buildings," Boston Dynamics wrote in a description of the video posted on YouTube. "It is specialized for mobile manipulation. It is electrically powered and hydraulically actuated. It uses sensors in its body and legs to balance and LIDAR and stereo sensors in its head to avoid obstacles, assess the terrain, help with navigation and manipulate objects."

We may have reached an inflection point. The problem space of chess is so astonishingly large that incremental increases in hardware speed and algorithms are unlikely to result in meaningful gains from here on out. Turns out I was kinda … totally completely wrong. The number of possible moves, or "problem space", of Chess is indeed astonishingly large, estimated to be 1050: Deep Blue was interesting because it forecast a particular kind of future, a future where specialized hardware enabled brute force attack of the enormous chess problem space, as its purpose built chess hardware outperformed general purpose CPUs of the day by many orders of magnitude. In the heady days of 1997, Deep Blue could evaluate 200 million chess positions per second.

A novel written by artificial intelligence was a finalist in Japan's Hoshi Shinichi Literary Award. The award is named after Hoshi Shinichi, a Japanese science fiction author whose books include The Whimsical Robot and Greetings from Outer Space. The unique contest accepts submissions from humans and machines, and judges for the prize, now in its third year, weren't told which novels were written by humans and which were penned by human-AI teams. This year was the first time the committee received submissions written by AI programs. The AI's novel is called The Day A Computer Writes A Novel, or Konpyuta ga shosetsu wo kaku hi in Japanese.

Google's artificial intelligence (AI) ethics board, established when Google acquired London AI startup DeepMind in 2014, remains one of the biggest mysteries in tech, with both Google and DeepMind refusing to reveal who sits on it. Google set up the board at DeepMind's request after the cofounders of the 400 million research-intensive AI lab said they would only agree to the acquisition if Google promised to look into the ethics of the technology it was buying into. Business Insider asked Google once again who is on its AI ethics board and what they do but it declined to comment. A number of AI experts told Business Insider that it's important to have an open debate about the ethics of AI given the potential impact it's going to have on all of our lives. Artificial intelligence is the field of building computer systems that understand and learn from observations without the need to be explicitly programmed, as defined by Nathan Benaich, an AI investor at venture capital firm Playfair Capital.

First went checkers, then fell chess. Now, a computer program has defeated the world's top player in the ancient east Asian board game of Go -- a major milestone for artificial intelligence that brings to a close the era of board games as benchmarks in computing. At the Four Seasons Hotel in Seoul, Google DeepMind's AlphaGo capped a 3-0 week on Saturday against Lee Sedol, a giant of the game. Lee and AlphaGo were to play again Sunday and Tuesday, but with AlphaGo having already clinched victory in the five-game match, the results are in and history has been made. It was a feat that experts had thought was still years away.

The competition will take a while. It doesn't start until 2017, and won't pick a winner until early 2020. DARPA will even have to create a giant wireless testbed to see how the competitors fare in relatively realistic conditions. It could be worthwhile, though, as the winner will scoop up a 2 million prize. The institution notes that there could be clear advantages to AI-based radios in the military, which could keep communications up and running on the battlefield.

Teams representing the University of California, Berkeley and Arizona State compete in the Grand Final of last year's Heroes of the Dorm tournament at the Shrine Expo Hall in Los Angeles. By the end of this week, four teams will advance to determine a champion after an intense multi-week competition. Their names will look familiar -- UConn, Miami and Oregon -- and monetary stakes are high, to the tune of 500,000. They're battling with video game controllers in the growing arena of competitive video gaming, whose increasing popularity has attracted the attention of big names in tech and media, from Electronic Arts to ESPN to Yahoo, as they see a growing audience eager to learn about the competitive scene and engage more in games at the center of its rise. 'We have the early markers of what will ultimately make eSports mainstream," says Joost van Dreunen, CEO of SuperData Research, which gathers data on the global games market. But it could require a generational shift before competitive video gaming -- known to many as "eSports" -- formally becomes mainstream entertainment. On April 3, the "Heroic Four" will be determined in Heroes of the Dorm, a competitive video game tournament hosted by Blizzard Entertainment, based on its action game Heroes of the Storm. For the second year, teams representing colleges from across the U.S., including the University of Connecticut and Arizona State University, are playing for glory and more than 500,000 in scholarships and prizes, including a free ride through school for the winning team. Fans watch the action online on ESPN, Twitch and YouTube, and they can even join tournament pools, where the winner with the most accurate bracket snags 10,000. It's the latest example of competitive video gaming's increased following, as younger fans gravitate towardeSports. The market is valued at 747 million, according to SuperData, and is expected to more than double to 1.9 billion in three years. The rising audience -- SuperData estimates it at 134 million as of last year -- is pushing video game publishers and cable networks to create competitive video game experiences and explore broadcasting options. The eSports market is young. Whalen Rozelle, Director of eSports at Riot Games -- makers of the hit competitive game League of Legends -- says it's still in "our pre-teen phase," with plenty of room to grow. "The industry still hasn't really figured out'is every game an eSport?

The nonnegative matrix factorization (NMF) is widely used in signal and image processing, including bio-informatics, blind source separation and hyperspectral image analysis in remote sensing. A great challenge arises when dealing with a nonlinear formulation of the NMF. Within the framework of kernel machines, the models suggested in the literature do not allow the representation of the factorization matrices, which is a fallout of the curse of the pre-image. In this paper, we propose a novel kernel-based model for the NMF that does not suffer from the pre-image problem, by investigating the estimation of the factorization matrices directly in the input space. For different kernel functions, we describe two schemes for iterative algorithms: an additive update rule based on a gradient descent scheme and a multiplicative update rule in the same spirit as in the Lee and Seung algorithm. Within the proposed framework, we develop several extensions to incorporate constraints, including sparseness, smoothness, and spatial regularization with a total-variation-like penalty. The effectiveness of the proposed method is demonstrated with the problem of unmixing hyperspectral images, using well-known real images and results with state-of-the-art techniques.

As I walked around my office on a recent morning, a female voice on my iPhone narrated the objects I passed. "Brick," "wall," "telephone," she said matter-of-factly. The voice paused when I came upon a bike hung on a wall-mounted rack, then intoned, "bicycle." The voice is part of a free image-recognition app called Aipoly that's trying to make it easier for those with vision impairments to recognize their surroundings. To use it, you point the phone's rear camera at whatever you want it to identify, and Aipoly will speak what it sees (or, at least, what it thinks it sees) and show the object's name on the phone's display.