Crowdsourcing enables researchers to conduct social experiments on a heterogenous set of participants and at a lower economic cost than conventional laboratory studies. For example, researchers can harness internet users to conduct user studies on their personal computers. Among various approaches to conduct subjective tests, pairwise comparisons are expected to yield more reliable results. However, in crowdsourced studies, the individuals performing the ratings are diverse compared to more controlled settings, which is difficult to control for using traditional experimental designs; researchers have recently proposed several randomized methods to conduct user studies [1, 2, 3], which accommodate incomplete and imbalanced data. HodgeRank, as an application of combinatorial Hodge theory to the preference or rank aggregation problem from pairwise comparison data, possibly being incomplete and imbalanced, was first introduced by [4], and inspired a series of studies in statistical ranking [5, 6, 7, 8]. Hodge theory has also found applications in game theory [9] and computer vision [10, 11], in addition to traditional applications in fluid mechanics [12] etc. HodgeRank formulates the ranking problem in terms of the discrete Hodge decomposition of the pairwise data and shows that it can be decomposed into three orthogonal components: a gradient flow representing a global rating (optimal in the L

We analyse the matrix factorization problem. Given a noisy measurement of a product of two matrices, the problem is to estimate back the original matrices. It arises in many applications such as dictionary learning, blind matrix calibration, sparse principal component analysis, blind source separation, low rank matrix completion, robust principal component analysis or factor analysis. It is also important in machine learning: unsupervised representation learning can often be studied through matrix factorization. We use the tools of statistical mechanics - the cavity and replica methods - to analyze the achievability and computational tractability of the inference problems in the setting of Bayes-optimal inference, which amounts to assuming that the two matrices have random independent elements generated from some known distribution, and this information is available to the inference algorithm. In this setting, we compute the minimal mean-squared-error achievable in principle in any computational time, and the error that can be achieved by an efficient approximate message passing algorithm. The computation is based on the asymptotic state-evolution analysis of the algorithm. The performance that our analysis predicts, both in terms of the achieved mean-squared-error, and in terms of sample complexity, is extremely promising and motivating for a further development of the algorithm.

Iris Capital is a pan-European venture capital fund manager specializing in digital economy. In such a world, many information platforms are available, but there is no software tool that applies the latest in machine and deep learning. Come to us to present us the next generation software tool or platform that automatically detects the right innovative teams/companies depending on who's looking for it and how innovation is defined. Our pitching competition is aimed at international early-stage start-ups between 1 and 5 years of existence. The 5 to 10 best start-ups will be evaluated on stage by a jury made of Iris Capital investors, large corporate innovation VP, leading start-up CEOs and media agencies.

What an utterly interesting time to be alive. We all remember how Deep Blue defeated Kasparov. Chess became a'solved problem' pretty soon after that historic event. And in the past week, we have witnessed an even more amazing feat: AlphaGo beating world-class Go player Lee Sedol. The game that knows more positions than there are atoms in the universe was no match for Google's DeepMind team. It's fascinating to see how a game with relatively simple rules can lead to such complex and strategic gameplay.

The Go contest between Lee Sedol and AlphaGo, Google DeepMind's Go-playing computer program, was broadcast across South Korea. Scrambling to respond to the success of Google DeepMind's world-beating Go program AlphaGo, South Korea announced on 17 March that it would invest 863 million (1 trillion won) in artificial-intelligence (AI) research over the next five years. It is not immediately clear whether the cash represents new funding, or had been previously allocated to AI efforts. But it does include the founding of a high-profile, public–private research centre with participation from several Korean conglomerates, including Samsung, LG Electronics and Hyundai Motor, as well as the technology firm Naver, based near Seoul. "Thanks to the'AlphaGo shock', we have learned the importance of AI before it is too late" The timing of the announcement indicates the impact in South Korea of AlphaGo, which two days earlier wrapped up a 4–1 victory over grandmaster Lee Sedol in an exhibition match in Seoul.

Artificial intelligence researchers at Google DeepMind are celebrating after reaching a major breakthrough that's been pursued for more than 20 years: The team taught a computer program the ancient game of Go, which has long been considered the most challenging game for an an artificial intelligence to learn. Not only can the team's program play Go, it's actually very good at it. The computer program AlphaGo was developed by Google DeepMind specifically with the task of beating professional human players in the ancient game. The group challenged the three-time European Go Champion Fan Hui to a series of matches, and for the first time ever, the software was able to beat a professional player in all five of the games played on a full-sized board. The team announced the breakthrough in a Nature article published today.

When it comes to communication, humans are hugely sensitive to each other's emotional states. Indeed, most people expect their emotional state to be taken into account by their correspondents. And when this happens, communication tends to be more effective. So if computers are ever to interact effectively with humans, they will need some way of repeating this trick and assessing the emotional state of their interlocutors. Understanding whether an individual has a positive or negative state of mind could make a huge difference to the quality of response that a computer might give.

The overeager adoption of big data is likely to result in catastrophes of analysis comparable to a national epidemic of collapsing bridges. Hardware designers creating chips based on the human brain are engaged in a faith-based undertaking likely to prove a fool's errand. Despite recent claims to the contrary, we are no further along with computer vision than we were with physics when Isaac Newton sat under his apple tree. Those may sound like the Luddite ravings of a crackpot who breached security at an IEEE conference. In fact, the opinions belong to IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley. Jordan is one of the world's most respected authorities on machine learning and an astute observer of the field. His CV would require its own massive database, and his standing in the field is such that he was chosen to write the introduction to the 2013 National Research Council report "Frontiers in Massive Data Analysis." San Francisco writer Lee Gomes interviewed him for IEEE Spectrum on 3 October 2014. IEEE Spectrum: I infer from your writing that you believe there's a lot of misinformation out there about deep learning, big data, computer vision, and the like. Michael Jordan: Well, on all academic topics there is a lot of misinformation. The media is trying to do its best to find topics that people are going to read about. Sometimes those go beyond where the achievements actually are.

Every age has its iconic images. One of the more terrifying ones of the 21st century is the image of a man in desert or army fatigues making a "V for victory" sign with raised arm while standing over the decapitated body of a Western victim. In most of these images, the perpetrator's face and head are covered with a scarf or hood to hide his identity. That has forced military and law enforcement agencies to identify these individuals in other ways, such as with voice identification. This is not always easy or straightforward, so there is significant interest in finding new ways.

South Korean Go master Lee Se-dol is now down 0-2 to Google DeepMind's AlphaGo which is on the verge of a milestone achievement in artificial intelligence. Master Se-dol has expressed surprise and amazement at the sophistication and skill of his virtual opponent. It has taken a long time to get here. Games have long been an attractive development tool for artificial intelligence researchers. In 1994, a computer program excelled at checkers and in 1997 it was chess.