You might ask what the difference is between most artificial intelligence (AI) companies and SparkCognition. Here it is: while at other firms, humans build models; SparkCognition puts them together with algorithms. Rather than roughing out one model and then doing a bunch of testing, SparkCognition continually tests and fits models to data accumulating in real time, an architecture that allows it to deal with big data. Without foregone conclusions about what might be happening, SparkCognition algorithms keep probing for relationships and possible explanations without any a priori idea of what's going on. This fantastic flexibility, along with the speed of computer technology, allows SparkCognition to come to conclusions fast enough for real-time intervention.

For aerial robots, maintaining a high vantage point for an extended time is crucial in many applications. However, available on-board power and mechanical fatigue constrain their flight time, especially for smaller, battery-powered aircraft. Perching on elevated structures is a biologically inspired approach to overcome these limitations. Previous perching robots have required specific material properties for the landing sites, such as surface asperities for spines, or ferromagnetism. We describe a switchable electroadhesive that enables controlled perching and detachment on nearly any material while requiring approximately three orders of magnitude less power than required to sustain flight. These electroadhesives are designed, characterized, and used to demonstrate a flying robotic insect able to robustly perch on a wide range of materials, including glass, wood, and a natural leaf.

Flapping two tiny wings, the small, thin robot wobbles its way towards the underside of a leaf, bumps into the surface and latches on, perching motionless above the ground. Moments later, its wings begin to flap once more and it jiggles off on its way. The little flying machine, dubbed a "RoboBee", has been designed to perch on a host of different surfaces, opening up new possibilities for the use of drones in providing a bird's-eye view of the world, scientists say. Know as micro aerial vehicles, such robots could be invaluable in reconnaissance of disaster zones or to form impromptu communication networks. But there is a hitch: flying takes energy, so the time these robots can spend in the air is limited by the size of the battery pack they can carry.

Crowdsourcing has been part of the IR toolbox as a cheap and fast mechanism to obtain labels for system development and evaluation. Successful deployment of crowdsourcing at scale involves adjusting many variables, a very important one being the number of workers needed per human intelligence task (HIT). We consider the crowdsourcing task of learning the answer to simple multiple-choice HITs, which are representative of many relevance experiments. In order to provide statistically significant results, one often needs to ask multiple workers to answer the same HIT. A stopping rule is an algorithm that, given a HIT, decides for any given set of worker answers if the system should stop and output an answer or iterate and ask one more worker. Knowing the historic performance of a worker in the form of a quality score can be beneficial in such a scenario. In this paper we investigate how to devise better stopping rules given such quality scores. We also suggest adaptive exploration as a promising approach for scalable and automatic creation of ground truth. We conduct a data analysis on an industrial crowdsourcing platform, and use the observations from this analysis to design new stopping rules that use the workers' quality scores in a non-trivial manner. We then perform a simulation based on a real-world workload, showing that our algorithm performs better than the more naive approaches.

Panoramic Power, a leading provider of device-level energy management solutions, is pleased to announce the release of Device Analyzer, an innovative data-science approach to energy management that generates actionable energy and operational efficiency insights. Using advanced machine learning algorithms, Device Analyzer provides users with greater visibility into the operational state of each of their monitored devices. Device Analyzer learns usage patterns for any device such as lighting, HVAC and production lines and with pre-defined algorithms per device-type, allows users to see the device's operational state in real-time. It understands and monitors device sequencing, detects anomalies and automatically generates operational insights. Device Analyzer shifts the user interaction from a focus only on energy consumption, to an operational one - delivering significant value to improve operations, productivity and facility performance.

We utilize copulas to constitute a unified framework for constructing and optimizing variational proposals in hierarchical Bayesian models. For models with continuous and non-Gaussian hidden variables, we propose a semiparametric and automated variational Gaussian copula approach, in which the parametric Gaussian copula family is able to preserve multivariate posterior dependence, and the nonparametric transformations based on Bernstein polynomials provide ample flexibility in characterizing the univariate marginal posteriors.

Arnold Schwarzenegger's Terminator franchise painted a dystopian picture of the robotic future In the James Cameron blockbuster The Terminator and its follow up sequels, the world was ruled by machines. Flying robots patrolled the skies even as land-based vehicles with minds of their own trundled along on the ground below. But thankfully, instead of trying to wipe out humanity, these drones may possibly soon be an indispensable component of our lives: building skyscrapers using 3D printing technology; transporting cargo across town; crop spraying; or helping find people trapped in buildings. Lockheed Martin's K-Max is a full size, unmanned helicopter, capable of both autonomous and remote-controlled operations. Previously deployed in combat zones, it is now increasingly being used for civilian applications, from fire fighting, to heavy lifting and oil drilling.

Industrial revolutions are momentous events. By most reckonings, there have been only three. The first was triggered in the 1700s by the commercial steam engine and the mechanical loom. The harnessing of electricity and mass production sparked the second, around the start of the 20th century. The computer set the third in motion after World War II (see "The Man Who Made the Computer Age Possible," by Jeffrey E. Garten). It might seem too soon to proclaim that the fourth industrial revolution, spurred by interconnected digital technology, has begun. But Henning Kagermann, the head of the German National Academy of Science and Engineering (Acatech), did exactly that in 2011, when he used the term Industrie 4.0 to describe a proposed government-sponsored industrial initiative. When you look closely at the rapid pace of digitization in industry today, the name doesn't seem hyperbolic at all. It is a signal of sweeping change that is rapidly transforming many companies and may catch others by surprise.

Businesses around the globe are bracing for the next wave of digital transformation. Many have begun to embrace it, but it is still early days. Artificial intelligence (AI) is already changing the way we work and live, but the biggest impacts from it are yet to come. Accenture already has invested in advanced AI across our business, to help our clients improve business outcomes and create new growth opportunities. But our new partnership with IPSoft is meant to truly advance that agenda.

In the James Cameron blockbuster The Terminator and its follow up sequels, the world was ruled by machines. Flying robots patrolled the skies while land-based vehicles with minds of their own trundled along on the ground below. But thankfully, instead of trying to wipe out humanity, these drones could soon be an indispensable component of our lives: building skyscrapers using 3D printing technology; transporting cargo across town; crop spraying; or helping find people trapped in buildings. Lockheed Martin's K-Max is a full size, unmanned helicopter, capable of both autonomous and remote-controlled operations. Previously deployed in combat zones, it is now increasingly being used for civilian applications, from fire fighting, to heavy lifting and oil drilling.