Amazon is extending its big data cloud platform across Europe, and the online retailer has machine learning research groups in Bangalore, Seattle, Palo Alto and Berlin, The Wall Street Journal reported. CEO Jeff Bezos is obsessed with predictive analytics and artificial intelligence -- anything that can help Amazon to better forecast customer wants and needs before the trends ever become obvious to the casual observer. The Amazon Machine Learning platform also is available for customer use via Amazon Web Services. Facebook's Artificial Intelligence Research project (FAIR) focuses on "giving people better ways to communicate." Some of Facebook's top AI researchers are Microsoft veterans, and those experts have a habit of sharing Facebook's knowledge.

Toon said that Graphcore currently stands at 40 employees and that the $30 million raised in the recently announced Series A (see Graphcore gets big backing for machine learning) would be used to complete the first design and for some limited expansion. "We could have taken more but this is sufficient to get product out," said Toon. "We will keep the engineering based here in Bristol but there is scope for some customer support and business development roles in Silicon Valley, Seattle and China," he added. Toon acknowledged that there is one other major technology company, besides Samsung and Robert Bosch, that contributed to the Series A funding . He said that company has chosen not to go public on the investment. With regard to the Intelligent Processor Unit (IPU) Knowles commented: "We will release our technology in the second half of 2017. It is a brand new, from-scratch design."

The World Intellectual Property Organization has developed a ground-breaking new "artificial intelligence"-based translation tool for patent documents, handing innovators around the world the highest-quality service yet available for accessing information on new technologies. WIPO Translate now incorporates cutting-edge neural machine translation technology to render highly technical patent documents into a second language in a style and syntax that more closely mirrors common usage, out-performing other translation tools built on previous technologies. WIPO has initially "trained" the new technology to translate Chinese, Japanese and Korean patent documents into English. Patent applications in those languages accounted for some 55% of worldwide filings in 20141. Users can already try out the Chinese-English translation facility on the public beta test platform.

Dr. Xiwei Liu is an associate professor in the State Key Laboratory of Management and Control for Complex Systems Automation Institute, Chinese Academy of Sciences. In 2006, he received Ph.D. degree in human factor engineering from the System Control and Management Laboratory, Nara Institute of Science and Technology, Japan. Then he worked there as a post doctor and assistant professor. From 2007 to 2009, he worked as a system engineer in Japan supplying research and development, consultant services of management information system for Toyota Motor Corporation, Japan Display Inc. (Hitachi Displays, Ltd.), Bank of Tokyo-Mitsubishi UFJ, etc. Since 2009, he has joined the State Key Laboratory of Management and Control for Complex Systems.

She paints and writes poetry. She's also an artificial intelligence from the movie Her, which imagines how a juiced-up Siri will change our lives. Now, tech companies large and small are racing to make this a reality. You've heard the jargon: AI, machine learning, deep learning, neural networks, natural language processing. What is artificial intelligence, or AI? AI, simply put, is an attempt to make computers as smart, or even smarter than human beings.

Tensor decomposition is an important technique for capturing the high-order interactions among multiway data. Multi-linear tensor composition methods, such as the Tucker decomposition and the CANDECOMP/PARAFAC (CP), assume that the complex interactions among objects are multi-linear, and are thus insufficient to represent nonlinear relationships in data. Another assumption of these methods is that a predefined rank should be known. However, the rank of tensors is hard to estimate, especially for cases with missing values. To address these issues, we design a Bayesian generative model for tensor decomposition. Different from the traditional Bayesian methods, the high-order interactions of tensor entries are modeled with variational auto-encoder. The proposed model takes advantages of Neural Networks and nonparametric Bayesian models, by replacing the multi-linear product in traditional Bayesian tensor decomposition with a complex nonlinear function (via Neural Networks) whose parameters can be learned from data. Experimental results on synthetic data and real-world chemometrics tensor data have demonstrated that our new model can achieve significantly higher prediction performance than the state-of-the-art tensor decomposition approaches.

Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.

Mullaney is the author of two forthcoming books on the Chinese typewriter and computer, and we discussed what he's learned while researching them. His argument is pretty fascinating to unpack because, at its heart, it is about more than China. It is about our relationship to computers, not just as physical objects but as conduits to intangible software. Typing English on a QWERTY computer keyboard, he says, "is about the most basic rudimentary way you can use a keyboard." You press the "a" key and "a" appears on your screen.

Even if self-driving cars aren't part of our daily lives yet, vehicles are becoming internet-connected at a rapid pace. Gartner predicts that one fifth of all autos on the road, and great majority of new vehicles being produced worldwide will have wireless network connectivity by 2020. Yet, few organizations have access to use the data generated by these vehicles today. That's where Otonomo, a startup based in Herzliya, Israel comes in. The company's systems gather up driver and vehicle data from disparate automakers and original equipment manufacturers.

An artificial intelligence (AI) judge has accurately predicted most verdicts of the European Court of Human Rights, and might soon be making important decisions about cases. Scientists built an artificial intelligence computer that was able to look at legal evidence as well as considering ethical questions to decide how a case should be decided. And it predicted those with 79 per cent accuracy, according to its creators. The algorithm looked at data sets made up 584 cases relating to torture and degrading treatment, fair trials and privacy. The computer was able to look through that information and make its own decision – which lined up with those made by Europe's most senior judges in almost every case.