Is your organization using, or planning to adopt, machine learning? If so, please share your experiences and insights in this survey. And even if you have no plans to use machine learning, please take the survey anyway--we'd love to know why.

CONVERSATIONS on the future have seen a common theme emerge - that it is disrupted and predominantly digital. Technological advancements in artificial intelligence, robotics, sharing platforms and the Internet of Things are fundamentally altering business models and industries. These changes are often not only alien to businesses; they are taking place at unprecedented speed. Many organisations, in particular the larger established ones that are encumbered by complex structures and bureaucratic processes, may find adapting to the pace of change a real struggle. Entrepreneurial businesses are often said to have a huge competitive advantage by virtue of their smaller size and simpler business model, which affords them the needed agility.

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?

This paper investigates message-passing algorithms for solving systems of linear equations in the Laplacian matrices of graphs and to compute electric flows. These two problems are fundamental primitives that arise in several domains such as computer science, electrical engineering, operations research, and machine learning. Despite the extensive literature on approximately solving these problems in quasi-linear time, the algorithms that have been proposed are typically centralized and involve multiple graph theoretic constructions or sampling mechanisms that make them difficult to implement and analyze. On the other hand, message-passing routines are distributed, simple, and easy to implement. In this paper we establish a framework to analyze message-passing algorithms to solve voltage and flow problems. We characterize the error committed by the algorithms in d-regular graphs with equal weights. We show that the convergence of the algorithms is controlled by the total variation distance between the distributions of non-backtracking random walks that start from neighbor nodes. More broadly, our analysis of message-passing introduces new insights to address generic optimization problems with constraints. Laplacian solver, flow problem, message-passing, min-sum, distributed algo-Keywords: rithms.

Abstract--A number of important applied problems in engineering, finance and medicine can be formulated as a problem of anomaly detection based on a one-class classification. A classical approach to this problem is to describe a normal state using a one-class support vector machine. Then to detect anomalies we quantify a distance from a new observation to the constructed description of the normal class. In this paper we present a new approach to one-class classification. We formulate a new problem statement and a corresponding algorithm that allow taking into account privileged information during the training phase. We evaluate performance of the proposed approach using synthetic datasets, as well as the publicly available Microsoft Malware Classification Challenge dataset. Anomaly detection refers to the problem of finding patterns in data that do not conform to an expected behaviour.

According to a survey of 122 founders, executives and investors in health-tech companies released today by Silicon Valley Bank, big data and artificial intelligence will have the greatest impact on the industry in the year ahead. Healthcare delivery and healthcare IT also promise the most growth in 2017. "Big data has been integral to our work at Celmatix. It has empowered physicians to be able to counsel women about their chances of having a baby, based on their relevant personal metrics, and not just their age," said Dr. Piraye Yurttas Beim, CEO at Celmatix. "It's an exciting time to be in a field where the pace of innovation continues to increase as both physicians and patients realize the potential of big data and personalized medicine."

New proposed models are often compared to state-of-the-art using statistical significance testing. Literature is scarce for classifier comparison using metrics other than accuracy. We present a survey of statistical methods that can be used for classifier comparison using precision, accounting for inter-precision correlation arising from use of same dataset. Comparisons are made using per-class precision and methods presented to test global null hypothesis of an overall model comparison. Comparisons are extended to multiple multi-class classifiers and to models using cross validation or its variants. Partial Bayesian update to precision is introduced when population prevalence of a class is known. Applications to compare deep architectures are studied.

SAP acquired procurement software vendor Ariba in 2011, but the company's history dates back two decades. This week, SAP Ariba executives briefed analysts in Boston, giving an overview of recent roadmap milestones as well as a look ahead at what's yet to come. Growth markers: There are now 2.4 million suppliers on Ariba's business network, with more than $1 trillion in commerce transactions each year. In addition, Ariba has a presence in 190 countries. Yet Ariba has set some lofty goals for additional growth.

Views from the Marketplace are paid for by advertisers and select partners of MIT Technology Review. Is your organization using, or planning to adopt, machine learning? If so, please share your experiences and insights in this survey. And even if you have no plans to use machine learning, please take the survey anyway--we'd love to know why.

In machine learning, you may often wish to build predictors that allows to classify things into categories based on some set of associated values. For example, it is possible to provide a diagnosis to a patient based on data from previous patients. Many algorithms have been developed for automated classification, and common ones include random forests, support vector machines, Naïve Bayes classifiers, and many types of neural networks. To get a feel for how classification works, we take a simple example of a classification algorithm – k-Nearest Neighbours (kNN) – and build it from scratch in Python 2. You can use a mostly imperative style of coding, rather than a declarative/functional one with lambda functions and list comprehensions to keep things simple if you are starting with Python. Here, we will provide an introduction to the latter approach.