SAN FRANCISCO – November 15, 2016 – Today at Minds Machines, GE (NYSE: GE) announced new products, acquisitions and partner programs to enable further adoption of Predix, the operating system for the Industrial Internet. The platform enhancements, acquisitions and new ISV partner program further complement the Predix technology stack and make it easier for industrial companies to execute a strategic digital transformation to drive internal productivity. In 2016, orders from GE's portfolio of software solutions are on track to climb 25% to more than $7 billion – making GE the fastest growing digital industrial company in the world. Demonstrating the strength of Predix within GE, digital thread productivity will exceed $600 million, accelerating into 2017. "The opportunity for industry is now," said Bill Ruh, Chief Digital Officer of GE and CEO, GE Digital.

In this work, we present an application of Locally Interpretable Machine-Agnostic Explanations to 2-D chemical structures. Using this framework we are able to provide a structural interpretation for an existing black-box model for classifying biologically produced fuel compounds with regard to Research Octane Number. This method of "painting" locally interpretable explanations onto 2-D chemical structures replicates the chemical intuition of synthetic chemists, allowing researchers in the field to directly accept, reject, inform and evaluate decisions underlying inscrutably complex quantitative structure-activity relationship models.

Across the globe, energy systems are changing and creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, National Grid is facing shrinking margins, looming capacity shortages and unpredictable peaks and troughs in energy supply caused by increasing levels of renewable penetration. At the Reinventing Energy Summit, Michael Bironneau, Head of Technology Development at Open Energi, will explore how the same machine learning techniques that have let machines defeat chess and Go masters, can also be leveraged to orchestrate massive amounts of flexible demand-side capacity – from industrial equipment, co-generation and battery storage systems – towards the one goal of creating a smarter grid; one that is cleaner, cheaper, more secure and more efficient. For World Cities Day 2016, I asked Michael a few questions to learn more about utilising data science in energy, creating a smarter grid, political challenges, and more. What are the main transformative technologies that will help create a smarter grid?

Oil and gas exploration can get a boost from the efficient deployment of the artificially intelligent computer system, IBM Watson, a renowned data scientist has said. "Petroleum geology can be accelerated by training IBM Watson on historic data. Nearly every process currently driven by human expertise can be accelerated by its cognitive computing technology," Romeo Kienzler told Gulf Times in an exclusive interview. When asked how the cognitive technology that can think like a human can be used effectively in oil exploration in Qatar and the wider region instead of the conventional methods, IBM Watson's chief data scientist explained the system "can have a look at such vast amounts of structured and unstructured data in seconds which a human brain cannot process in an entire life time." In Qatar to take part in a recent event by Hamad Bin Khalifa University's Qatar Computer Research Institute, Kienzler pointed out that "whenever human expertise is involved in a process, the addition of a cognitive system as an adviser most likely will accelerate the process because information loss is prevented."

Predicting the future is hard. It's nearly impossible to know what technological marvels await in the next few years, let alone the next eight decades. Undaunted, we've put together a list of 10 super-advanced technologies that should be around by the year 2100. Some of these technologies are rather "out there," but I'm reasonably confident in making these predictions. As radical as some of the items described here appear, most--if not all--should be around by the turn of the 22nd century.

Markov Chain Monte Carlo (MCMC) and Belief Propagation (BP) are the most popular algorithms for computational inference in Graphical Models (GM). In principle, MCMC is an exact probabilistic method which, however, often suffers from exponentially slow mixing. In contrast, BP is a deterministic method, which is typically fast, empirically very successful, however in general lacking control of accuracy over loopy graphs. In this paper, we introduce MCMC algorithms correcting the approximation error of BP, i.e., we provide a way to compensate for BP errors via a consecutive BP-aware MCMC. Our framework is based on the Loop Calculus (LC) approach which allows to express the BP error as a sum of weighted generalized loops. Although the full series is computationally intractable, it is known that a truncated series, summing up all 2-regular loops, is computable in polynomial-time for planar pair-wise binary GMs and it also provides a highly accurate approximation empirically. Motivated by this, we first propose a polynomial-time approximation MCMC scheme for the truncated series of general (non-planar) pair-wise binary models. Our main idea here is to use the Worm algorithm, known to provide fast mixing in other (related) problems, and then design an appropriate rejection scheme to sample 2-regular loops. Furthermore, we also design an efficient rejection-free MCMC scheme for approximating the full series. The main novelty underlying our design is in utilizing the concept of cycle basis, which provides an efficient decomposition of the generalized loops. In essence, the proposed MCMC schemes run on transformed GM built upon the non-trivial BP solution, and our experiments show that this synthesis of BP and MCMC outperforms both direct MCMC and bare BP schemes.

We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM) with a subset of variables truncated to be nonnegative. The truncated variables are assumed latent and integrated out to induce a marginal model. We show that the variables in the marginal model are non-Gaussian distributed and their expected relations are nonlinear. We use expectation-maximization to break the inference of the nonlinear model into a sequence of TGGM inference problems, each of which is efficiently solved by using the properties and numerical methods of multivariate Gaussian distributions. We use the TGGM to design models for nonlinear regression and classification, with the performances of these models demonstrated on extensive benchmark datasets and compared to state-of-the-art competing results.

Machine learning has been one of the most exciting development we have had since the internet and its subsequent spread through smart phones. Andrew Ng likens artificial intelligence (AI: term can be used vice versa with machine learning as of this moment that AI system learns from data, but this hasn't always been the case) to electricity; that AI will be pervasive, everywhere and transformative in the way we do things. Why would it be so transformative to the way we do things? Its simply that before advent of AI, everything we built were not even stupid, they had no thoughts and take no actions, its people who gotta make all the decisions for them. My own first practical exposure to building a practical AI system was when I started working as a wind energy analyst.

Supervised and unsupervised learning approaches rapidly map data into a semantic model that can be used in an IT architecture. GE Digital's acquisition of Bit Stew Systems, a small startup with about 100 employees, should come as no surprise to those familiar with the challenges of industrial IoT projects. GE's Predix is a major industrial IoT platform that targets sectors such as manufacturing, aviation and energy, with use cases in predicting maintenance and optimizing performance of massive assets, such as multi-million dollar gas pipelines, jet engines, or gas turbines. Bit Stew, based in Vancouver, Canada, has software that uses machine learning algorithms to filter and integrate data from industrial equipment, databases, and control systems, creating a semantic data model for use throughout an IT architecture – from cloud to edge. "Most of our customers maintain 30 connected systems to our platform, and are managing millions of connected devices," Franco Castaldini, vice president of marketing and product management at Bit Stew, told RTInsights.