Fujitsu today announces the addition of nVidia Volta Graphical Processing Units (GPUs) to accelerate advances in artificial intelligence and support deep learning processing on its latest Primergy x86 servers. Available to customers in Europe, the Middle East, India and Africa beginning December 2017, select Primegy models are certified for the new-generation of nVidia Tesla V100 GPU accelerators. AI and deep learning computing involves large amounts of raw data and highly demanding compute environments. Fujitsu is rising to this challenge by introducing native deep learning processing capabilities to select Fujitsu Primergy CX and RX server models. To achieve the highest possible levels of system performance, Fujitsu is introducing native support for NVIDIA GPUs via direct connection to the mainboard.

Alibaba Group announced today that it's launching a new research organization aimed at tackling emerging technologies like machine learning, network security, the internet of things, and quantum computing. It's called the Academy for Discovery, Adventure, Momentum and Outlook (referred to as the DAMO Academy for short), and Alibaba plans to recruit 100 researchers to staff labs around the globe. The company plans to open two labs in China, both in the tech giant's home city of Hangzhou as well as Beijing. In addition, the Chinese ecommerce and technology titan will open labs in San Mateo, California; Seattle, Washington; Moscow, Russia; Tel Aviv, Israel; and Singapore. Over the next three years, the company plans to spend $15 billion on research and development, a significant increase over its current rate of spending.

AI has been a hot topic among loyalty marketers in recent years, and even more this year. Can AI transform and elevate a loyalty marketer's approach to customer experience? A new study from Emarsys, conducted by Forrester Consulting, examines Artificial Intelligence (AI) marketing readiness in the retail and e-commerce industries. The study, "Building Trust and Confidence: AI Marketing Readiness in Retail and e-Commerce," sought to understand if there is still a gap between the readiness of AI marketing solutions to execute on real-time B2C marketing campaigns, and the readiness of marketing tech users and business decision makers to adopt AI marketing technology. The study surveyed companies in the U.S., U.K, Germany, France, and Australia that each had revenue of at least $50 million upward to $5 billion.

Last week, i took the train in direction of Toulouse(France). It was the first time for 2 years that i have not been in this town since i finished my studies here and found a job in another town. But, i was there this time as speaker at DevFestToulouse 2017 edition . My Keynote was about « Physics laws & Big Data » . I tried to explain to attendees that as Physics is the science of nature ( word etymology), the principles discovered by physicians & scientific methods that they used could be potentially be useful for Big Data projects.

Robots are taking our jobs. Almost everything you can learn in our current education system will be automated soon. Supply chains are in upheaval as production moves closer to the consumer and products are made by individual robots rather than rows of underpaid workers. Artificial intelligence and machine learning are threatening thousands of white-collar jobs. As manufacturing shifts away from the traditional Asian hubs, shipping lines now suffer from massive overcapacity that will probably stay for a long time. We are aware of all of these trends.

We analyze the local convergence of proximal splitting algorithms to solve optimization problems that are convex besides a rank constraint. For this, we show conditions under which the proximal operator of a function involving the rank constraint is locally identical to the proximal operator of its convex envelope, hence implying local convergence. The conditions imply that the non-convex algorithms locally converge to a solution whenever a convex relaxation involving the convex envelope can be expected to solve the non-convex problem.

I present a web service for querying an embedding of entities in the Wikidata knowledge graph. The embedding is trained on the Wikidata dump using Gensim's Word2Vec implementation and a simple graph walk. A REST API is implemented.

Graph theory provides a language for studying the structure of relations, and it is often used to study interactions over time too. However, it poorly captures the both temporal and structural nature of interactions, that calls for a dedicated formalism. In this paper, we generalize graph concepts in order to cope with both aspects in a consistent way. We start with elementary concepts like density, clusters, or paths, and derive from them more advanced concepts like cliques, degrees, clustering coefficients, or connected components. We obtain a language to directly deal with interactions over time, similar to the language provided by graphs to deal with relations. This formalism is self-consistent: usual relations between different concepts are preserved. It is also consistent with graph theory: graph concepts are special cases of the ones we introduce. This makes it easy to generalize higher-level objects such as quotient graphs, line graphs, k-cores, and centralities. This paper also considers discrete versus continuous time assumptions, instantaneous links, and extensions to more complex cases.

Topological Data Analysis (tda) is a recent and fast growing eld providing a set of new topological and geometric tools to infer relevant features for possibly complex data. This paper is a brief introduction, through a few selected topics, to basic fundamental and practical aspects of tda for non experts. 1 Introduction and motivation Topological Data Analysis (tda) is a recent eld that emerged from various works in applied (algebraic) topology and computational geometry during the rst decade of the century. Although one can trace back geometric approaches for data analysis quite far in the past, tda really started as a eld with the pioneering works of Edelsbrunner et al. (2002) and Zomorodian and Carlsson (2005) in persistent homology and was popularized in a landmark paper in 2009 Carlsson (2009). tda is mainly motivated by the idea that topology and geometry provide a powerful approach to infer robust qualitative, and sometimes quantitative, information about the structure of data-see, e.g. Chazal (2017). tda aims at providing well-founded mathematical, statistical and algorithmic methods to infer, analyze and exploit the complex topological and geometric structures underlying data that are often represented as point clouds in Euclidean or more general metric spaces. During the last few years, a considerable eort has been made to provide robust and ecient data structures and algorithms for tda that are now implemented and available and easy to use through standard libraries such as the Gudhi library (C++ and Python) Maria et al. (2014) and its R software interface Fasy et al. (2014a). Although it is still rapidly evolving, tda now provides a set of mature and ecient tools that can be used in combination or complementary to other data sciences tools. The tdapipeline. tda has recently known developments in various directions and application elds. There now exist a large variety of methods inspired by topological and geometric approaches. Providing a complete overview of all these existing approaches is beyond the scope of this introductory survey. However, most of them rely on the following basic and standard pipeline that will serve as the backbone of this paper: 1. The input is assumed to be a nite set of points coming with a notion of distance-or similarity between them. This distance can be induced by the metric in the ambient space (e.g. the Euclidean metric when the data are embedded in R d) or come as an intrinsic metric dened by a pairwise distance matrix. The denition of the metric on the data is usually given as an input or guided by the application. It is however important to notice that the choice of the metric may be critical to reveal interesting topological and geometric features of the data.

For many algorithms, parameter tuning remains a challenging and critical task, which becomes tedious and infeasible in a multi-parameter setting. Multi-penalty regularization, successfully used for solving undetermined sparse regression of problems of unmixing type where signal and noise are additively mixed, is one of such examples. In this paper, we propose a novel algorithmic framework for an adaptive parameter choice in multi-penalty regularization with a focus on the correct support recovery. Building upon the theory of regularization paths and algorithms for single-penalty functionals, we extend these ideas to a multi-penalty framework by providing an efficient procedure for the construction of regions containing structurally similar solutions, i.e., solutions with the same sparsity and sign pattern, over the whole range of parameters. Combining this with a model selection criterion, we can choose regularization parameters in a data-adaptive manner. Another advantage of our algorithm is that it provides an overview on the solution stability over the whole range of parameters. This can be further exploited to obtain additional insights into the problem of interest. We provide a numerical analysis of our method and compare it to the state-of-the-art single-penalty algorithms for compressed sensing problems in order to demonstrate the robustness and power of the proposed algorithm.