We propose a method for recovering the structure of a sparse undirected graphical model when very few samples are available. The method decides about the presence or absence of bonds between pairs of variable by considering one pair at a time and using a closed form formula, analytically derived by calculating the posterior probability for every possible model explaining a two body system using Jeffreys prior. The approach does not rely on the optimisation of any cost functions and consequently is much faster than existing algorithms. Despite this time and computational advantage, numerical results show that for several sparse topologies the algorithm is comparable to the best existing algorithms, and is more accurate in the presence of hidden variables. We apply this approach to the analysis of US stock market data and to neural data, in order to show its efficiency in recovering robust statistical dependencies in real data with non stationary correlations in time and space.

General Electric builds jet engines and wind turbines and medical gear. But the 124-year-old industrial giant is also transforming itself for the digital age. It's fashioning software that pulls data from all this hardware, hoping to gain an insight into industrial operations that was never possible in the past. The problem is that analyzing all this data is difficult, and the talent needed to make it happen is scarce. So GE is going shopping.

General Electric said on Tuesday it has acquired two tech startups to build its artificial intelligence capability, a move that helps it compete with IBM's Watson product. GE ge said the acquisitions of Bit Stew Systems and Wise.io will expand its Predix platform for industrial internet applications, which connects big machines such as power plants and aircraft engines to databases and analytical software. Terms of the deals weren't disclosed. Berkley, California-based Wise.io has advanced machine learning technology that GE sees "as really well-built for the industrial world," Bill Ruh, chief executive officer of GE Digital, GE's software arm, said in an interview. A branch of artificial intelligence, machine learning allows computers to adapt to new data without new programming.

General Electric (GE), the largest industrial company in the US, says it has developed processes that more than double the speed of innovation and which have the potential to completely restructure its own business. GE will next week launch its first business venture, called Fuse, that will test a hugely ambitious and radical approach to creating new companies through processes and technologies designed to harness the work of global crowds of experts. Dyan Finkhousen is Director of GE's Open Innovation and Advanced Manufacturing group. The GE startup "will usher in what we believe is the future of work," said Dyan Finkhousen, Director of GE's Open Innovation and Advanced Manufacturing group. See also: Emerging technologies to power your systems of insight Obama's report on the future of artificial intelligence: The main takeaways Robot security: Making sure machines don't become the latest big threat She was speaking at Brightidea's Synthesize user conference in San Francisco.

This image, from a 1962 internal General Electric document, conveyed the idea of random access storage using a set of "pigeon holes" in which data could be placed. Fifty-three years ago a small team working to automate the business processes of the General Electric Company built the first database management system. The Integrated Data Store--IDS--was designed by Charles W. Bachman, who won the ACM's 1973 A.M. Turing Award for the accomplishment. Before General Electric, he had spent 10 years working in engineering, finance, production, and data processing for the Dow Chemical Company. He was the first ACM A.M. Turing Award winner without a Ph.D., the first with a background in engineering rather than science, and the first to spend his entire career in industry rather than academia.

Sparse high dimensional graphical model selection is a topic of much interest in modern day statistics. A popular approach is to apply l1-penalties to either (1) parametric likelihoods, or, (2) regularized regression/pseudo-likelihoods, with the latter having the distinct advantage that they do not explicitly assume Gaussianity. As none of the popular methods proposed for solving pseudo-likelihood based objective functions have provable convergence guarantees, it is not clear if corresponding estimators exist or are even computable, or if they actually yield correct partial correlation graphs. This paper proposes a new pseudo-likelihood based graphical model selection method that aims to overcome some of the shortcomings of current methods, but at the same time retain all their respective strengths. In particular, we introduce a novel framework that leads to a convex formulation of the partial covariance regression graph problem, resulting in an objective function comprised of quadratic forms. The objective is then optimized via a coordinate-wise approach. The specific functional form of the objective function facilitates rigorous convergence analysis leading to convergence guarantees; an important property that cannot be established using standard results, when the dimension is larger than the sample size, as is often the case in high dimensional applications. These convergence guarantees ensure that estimators are well-defined under very general conditions, and are always computable. In addition, the approach yields estimators that have good large sample properties and also respect symmetry. Furthermore, application to simulated/real data, timing comparisons and numerical convergence is demonstrated. We also present a novel unifying framework that places all graphical pseudo-likelihood methods as special cases of a more general formulation, leading to important insights.

General Electric's Research and Elscint (Hackensack, NJ), a manufacturer Johnson Controls (Milwaukee, WI) Development Center (Schenectady, of medical imaging systems, has has begun deployment of a knowledge-based NY) has developed an expert system begun offering its customers a service engineering application which is being used to increase the option based on expert systems. The to increase the productivity of the speed of design of new jet engines, MasterMind system delivers troubleshooting engineering design function. The system, called Engineous, on laptop or desktop computers. The General (Menlo Park, CA), is conveyor for further processing. It problems and recommends solutions objects have become rotated.

Further, new application domains such as computer -aided design (CAD), computer- aided manufacturing (CAM), and image understanding based on formal logic require novel concepts in knowledge representation and inference beyond the capabilities of current production rule systems. Fundamental research in artificial intelligence is concentrated at Corporate Research and Development (CR&D), with advanced development and applications pursued in parallel efforts by operating departments. The fundamental research and advanced applications activities are strongly coupled, providing research teams with opportunities for field evaluations of new concepts and systems. This article summarizes current research projects at CR&D and gives an overview of applications within the company.

General Electric is engaged in a broad range of research and development activities in artificial intelligence, with the dual objectives of improving the productivity of its internal operations and of enhancing future products and services in its aerospace, industrial, aircraft engine, commercial, and service sectors. Many of the applications projected for AI within GE will require significant advances in the state of the art in advanced inference, formal logic, and architectures for real-time systems. New software tools for creating expert systems are needed to expedite the construction of knowledge bases. Further, new application domains such as computer -aided design (CAD), computer- aided manufacturing (CAM), and image understanding based on formal logic require novel concepts in knowledge representation and inference beyond the capabilities of current production rule systems. Fundamental research in artificial intelligence is concentrated at Corporate Research and Development (CR&D), with advanced development and applications pursued in parallel efforts by operating departments. The fundamental research and advanced applications activities are strongly coupled, providing research teams with opportunities for field evaluations of new concepts and systems. This article summarizes current research projects at CR&D and gives an overview of applications within the company.