On Thursday, tech giant Google announced that it is opening a new research center in Europe, dedicated to machine learning. The new research center -- which will enable Google to realize its ambitious artificial intelligence (AI) plans by focusing on the development of AI products and research -- will be based in Google Research offices in Zurich, Switzerland. According to the details shared by Emmanuel Mogenet – chief of Google Research for Europe – in a recent blog post, the key focus of the new research group in Europe will be on three AI areas --- machine intelligence, machine perception, and natural language processing and understanding. Mogenet further specified that the main objective of the research group in Europe will be to find new ways in which machine learning infrastructure can be improved, and how the technology can be put it into practical use. In addition, the new AI-focused research group will also work for the advancement of Natural Language Understanding – that is, the capability of machines to understand and process human language – in close collaboration with linguists.

Is your smart phone really smart? Do you ever fear it will get too smart? Will it wake up one morning and decide to start running your life – deleting contacts it doesn't like, booking holidays online that it wants to go on with you or shifting your calendar appointments to suit its tastes? Perhaps, more realistically, you may be inclined to feel that your printer has a mind and mood swings of its own, seemingly out to get you when you are facing the most desperate deadline. But actually, the more we progress in the field of robotics, the more we are forced to recognise and appreciate that the mind is a unique wonder of the living world.

Multi-label classification has received considerable interest in recent years. Multi-label classifiers have to address many problems including: handling large-scale datasets with many instances and a large set of labels, compensating missing label assignments in the training set, considering correlations between labels, as well as exploiting unlabeled data to improve prediction performance. To tackle datasets with a large set of labels, embedding-based methods have been proposed which seek to represent the label assignments in a low-dimensional space. Many state-of-the-art embedding-based methods use a linear dimensionality reduction to represent the label assignments in a low-dimensional space. However, by doing so, these methods actually neglect the tail labels - labels that are infrequently assigned to instances. We propose an embedding-based method that non-linearly embeds the label vectors using an stochastic approach, thereby predicting the tail labels more accurately. Moreover, the proposed method have excellent mechanisms for handling missing labels, dealing with large-scale datasets, as well as exploiting unlabeled data. With the best of our knowledge, our proposed method is the first multi-label classifier that simultaneously addresses all of the mentioned challenges. Experiments on real-world datasets show that our method outperforms stateof-the-art multi-label classifiers by a large margin, in terms of prediction performance, as well as training time.

We propose a method for building an interpretable recommender system for personalizing online content and promotions. Historical data available for the system consists of customer features, provided content (promotions), and user responses. Unlike in a standard multi-class classification setting, misclassification costs depend on both recommended actions and customers. Our method transforms such a data set to a new set which can be used with standard interpretable multi-class classification algorithms. The transformation has the desirable property that minimizing the standard misclassification penalty in this new space is equivalent to minimizing the custom cost function.

It is commonplace to encounter nonstationary data, of which the underlying generating process may change over time or across domains. The nonstationarity presents both challenges and opportunities for causal discovery. In this paper we propose a principled framework to handle nonstationarity, and develop some methods to address three important questions. First, we propose an enhanced constraint-based method to detect variables whose local mechanisms are nonstationary and recover the skeleton of the causal structure over observed variables. Second, we present a way to determine some causal directions by taking advantage of information carried by changing distributions. Third, we develop a method for visualizing the nonstationarity of causal modules. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.

A computer that uses the quirks of quantum physics to work on data should be capable of things far beyond any machine in use today. Governments and large tech companies have spent huge sums trying to prove out that idea. Yet quantum computers have sometimes seemed like one of those technologies that are always 20 years away. Recently some leading research groups have come to think they can see a path to shortening that time considerably. Yesterday Google and researchers from the University of the Basque Country in Bilbao, Spain, published results that could lead to a shortcut to the long-awaited first conclusive demonstration of the power of quantum computing. The new result is one of the first fruits of a plan Google's quantum researchers laid out when I visited their new lab last year.

I keep posting ads for postdocs with other people but this is actually to work with little old me! The Department of Electrical and Computer Engineering (ECE) at Rutgers University is seeking a dynamic and motivated Postdoctoral Fellow to work on developing distributed machine learning algorithms that work on complex neuroimaging data. This work is in collaboration with the Mind Research Network in Albuquerque, New Mexico under NIH Grant 1R01DA040487-01A1. Candidates with a Ph.D. in Electrical Engineering, Computer Science, Statistics or related areas with experience in one of The Fellow will receive valuable experience in translational research as well as career mentoring, opportunities to collaborate with others outside the project within the ECE Department, DIMACS, and other institutions.The initial appointment is for 1 year but can be renewed subject to approval. Salary and compensation will be commensurate with the standard NIH scale for postdocs.

Video Friday is your weekly selection of awesome robotics videos, collected by your multilayer Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. Also I want that thing that will fire birdies at me. The first robot to autonomously and intentionally break Asimov's first law, which states: A robot may not injure a human being or, through inaction, allow a human being to come to harm.

CrowdFlower, a San Francisco-based data enrichment, data mining and crowdsourcing platform for data science teams, last week announced its recent 10M venture funding. The investment round was led by Canvas Ventures, Trinity Ventures and Microsoft. The capital will be used to fuel adoption of CrowdFlower AI, which combines training data, machine learning and human-in-the-loop in a single platform. "We've seen companies like Tesla and Uber build large data science teams and adopt AI and machine learning to solve billion dollar problems like driverless cars," said Lukas Biewald, founder and chief executive officer at CrowdFlower. "But we wanted to bring AI and machine learning within the reach of every business to attack million dollar problems such as classifying customer support tickets or generating customer insights from social data."

The second "Computers Gone Wild: Impact and Implications of Developments in Artificial Intelligence on Society" workshop took place on February 19, 2016 at Harvard Law School. Marin Solja?i?, Max Tegmark, Bruce Schneier, and Jonathan Zittrain convened this informal workshop to discuss recent advancements in artificial intelligence research. Participants represented a wide range of expertise and perspectives and discussed four main topics during the day-long event: the impact of artificial intelligence on labor and economics, algorithmic decision-making, particularly in law, autonomous weapons, and the risks of emergent human-level artificial intelligence. Each session opened with a brief overview of the existing literature related to the topic from a designated participant, followed by remarks from two or three provocateurs. The session leader then moderated a discussion with the larger group.