There's no mistaking the fact that Google is one of many companies that consider machine learning to be the future of technology. Now, the firm has announced the formation of a Zurich-based research group dedicated to the field. The Swiss city is already home to Google's largest engineering office outside of the US. The engine that powers Knowledge Graph and the conversation engine powering the Google Assistant in Allo messenger were developed by the teams located here. According to a blog post by Emmanuel Mogenet, head of Google Research for Europe, the new group will be focusing on the key areas of machine learning, natural language processing and understanding, and machine perception.

Industrial companies are confronted with several new trends that will fundamentally change production and logistics processes. For example, the term "Industry 4.0," which was coined in Germany, stands for the digital networking of people, objects, and systems to create integrated production processes. In international jargon, it is referred to as the Industrial Internet of Things (IIoT). All technologies, systems, and components that are involved in the industrial value creation process are connected to each other as well as to company networks and the internet. Smart factory is another trend that forms a part of the IIoT development.

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.

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.

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.

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.

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.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

In this short tutorial I want to provide a short overview of some of my favorite Python tools for common procedures as entry points for general pattern classification and machine learning tasks, and various other data analyses. In this section want to recommend a way for installing the required Python-packages packages if you have not done so, yet. Otherwise you can skip this part. Although they can be installed step-by-step "manually", but I highly recommend you to take a look at the Anaconda Python distribution for scientific computing. Anaconda is distributed by Continuum Analytics, but it is completely free and includes more than 195 packages for science and data analysis as of today.