NuTonomy will test out its autonomous vehicle in the streets of Boston by the end of this year. The Cambridge, Massachusetts-based startup began testing its self-driving cars in Singapore last August with the hopes of rolling out its commercial fleet in the U.S. ahead of other large automakers, such as BMW and Ford (F). In an exclusive interview with FOX Business Network's Countdown to the Closing Bell, NuTonomy CEO Karl Iagnemma said its testing of self-driving cars is going great. "The most difficult thing is that even if you can program a car to follow the rules of the road, sometimes, human drivers have other ideas and so these cars have to be intelligent enough to adapt to the way that you and I drive," Iagnemma said. NuTonomy is trying to get a leg up on the competition as companies such as Google, parent company Alphabet Inc. (GOOGL), and Uber look to launch their own autonomous vehicles.

The World Chess Championship is nearing its close, with the final tie-breaking match between Norway's Magnus Carlsen and Russia's Sergey Karjakin set for Monday. The match, a series of 12 games that began on November 11th, has attracted celebrities, tech leaders, and high-profile media coverage. In part, that's thanks to its New York location, where chess has enjoyed a decade-long surge in popularity. The continuing popularity of chess might have been hard to predict in 1997, after IBM's Deep Blue defeated human World Champion Gary Kasparov (also in New York). Before the match, commentators thought a loss by Kasparov would diminish chess as a pursuit.

In a major win for artificial intelligence, Google DeepMind's AlphaGo has beat European Go champion Fan Hui in the complex 2,500-year-old Chinese game of Go, touted the official Google blog. A victory in a Go game against a human champion has long been coveted among AI researchers, because the possible moves that a player can take can reach into the quadrillions and beyond. As a result, Go has proven a formidable challenge for artificial intelligence researchers. Microsoft and Facebook, for example, have been working on ways to win in the game over a human champion, but have had no luck to date, according to a BBC news report. Last October, Google DeepMind held a private, closed-door Go match in its London office between its AlphaGo system and Hui.

Many inference problems in structured prediction are naturally solved by augmenting a tractable dependency structure with complex, non-local auxiliary objectives. This includes the mean field family of variational inference algorithms, soft- or hard-constrained inference using Lagrangian relaxation or linear programming, collective graphical models, and forms of semi-supervised learning such as posterior regularization. We present a method to discriminatively learn broad families of inference objectives, capturing powerful non-local statistics of the latent variables, while maintaining tractable and provably fast inference using non-Euclidean projected gradient descent with a distance-generating function given by the Bethe entropy. We demonstrate the performance and flexibility of our method by (1) extracting structured citations from research papers by learning soft global constraints, (2) achieving state-of-the-art results on a widely-used handwriting recognition task using a novel learned non-convex inference procedure, and (3) providing a fast and highly scalable algorithm for the challenging problem of inference in a collective graphical model applied to bird migration.

We present an image-conditional image generation model. The model transfers an input domain to a target domain in semantic level, and generates the target image in pixel level. To generate realistic target images, we employ the real/fake-discriminator as in Generative Adversarial Nets [6], but also introduce a novel domain-discriminator to make the generated image relevant to the input image. We verify our model through a challenging task of generating a piece of clothing from an input image of a dressed person. We present a high quality clothing dataset containing the two domains, and succeed in demonstrating decent results. Keywords: Domain transfer, Generative Adversarial Nets.

In the fall of 2010, I traveled to New Zealand, and one of the places I visited was the small south island city of Christchurch. I was charmed by the tree-lined Avon River, the English-style cathedral in the main square, and the mountains looming in the distance. Inside the cathedral was a stack of poems with a moving message of peace. I saved one to tack on my cork board at home, where it remains to this day. Three months later I turned on the news to see the Christchurch cathedral splintered and broken, its spire crumbled to the ground.

Some of the top minds in the field have some advice for the next presidential administration on how to proceed with robotics. Last week, a group of 150 experts from Google, Microsoft, Lockheed Martin, UPenn, Yale, Georgia Tech, and Carnegie Mellon (to name a few) published the latest edition of the Roadmap for Robotics, a report that outlines the industry's future. The 100-page paper is meant to serve as a guide to what sorts of developments the tech industry should strive for and what types of technologies Congress should invest in. The experts offer specific suggestions as to how the U.S. should allocate funds toward advancing robotics and lays out suggestions for regulation. For one, it suggests robotics companies be required to offer more transparency regarding the tasks that their machines can and cannot perform.

Onalytica analyzed tweets over the last 6 months mentioning the keywords M2M OR "Machine to Machine." They then identified the top 100 most influential brands and individuals leading the discussion on Twitter. Machine to Machine (M2M) refers to direct communication between devices using any communications channel, including wired and wireless. The M2M market is undergoing a fast transformation as enterprises are increasingly realizing the value of connecting geographically dispersed people, devices, sensors and machines to corporate networks. It is for precisely this reason that the Global M2M market is expected to grow to 27 billion devices, generating $1.6 trillion in revenue in 2024.

Chris Nicholson co-founded Skymind and Deeplearning4j, the most popular deep-learning framework for Java. Quitting Twitter is easy -- I've done it a hundred times. Someone called it "a clown car that drove into a gold mine," and like all clown cars, Twitter makes the passengers get out once in awhile. If I go back, it's because I'm addicted. For an information junkie, that little bubble is hard to resist.

Stock market major BSE on Monday said it has launched an artificial intelligence mechanism to track "news related to listed companies" on digital media. According to BSE, the artificial intelligence mechanism based on a systemic solution has been envisaged to deepen its regulatory oversight on newer channels of communication. "The primary objective of verification is that the mechanism will detect and mitigate potential risks of market manipulation, rumour, and reduce information asymmetry arising from it on digital media platforms, including social media," the BSE said in a statement. "It provides accurate information involving listed companies and BSE through the exchange website for the benefit of investors." According to the regulated stock exchange, the solution employs an advanced level combination of statistical modeling and big data analytics.