By combining human-centric machine learning and intelligent context generation, contextere is developing an intelligent personal agent capable of delivering actionable insights at the point of service. Its industrial software weaves together the power of AI and IoT data to give blue collar workers the right information, at the right time, on the right device. Here, Gabe Batstone, contextere CEO shares a vision of the future that empowers workers through automation. In recent years, industrial enterprises have seen a rise in emerging technologies and digital tools that offer considerable improvements in the workplace. It's clear that modernizing in this context is an uphill battle for the blue-collar workforce.

Once a computer scientist's pipe dream, artificial intelligence is now part of our daily lives in the form of voice recognition systems, product recommendation platforms and navigation tools. All of these rely on computer algorithms that process information and solve problems in a way similar to – and sometimes superior to – the human mind. Yet artificial intelligence is doing more than just recommending new restaurants and the best routes to them. It is also changing the way scientists across diverse disciplines are studying the world. Aided by the close proximity of medical researchers, computer scientists, psychologists and more, Stanford researchers are deploying artificial intelligence to map poverty in Africa, find safer alternatives to conventional rechargeable batteries and perhaps even understand our own minds.

Researchers have created the first flying wireless robotic insect. The news: Behold RoboFly, a laser-powered robot built by University of Washington researchers that weighs in at slightly more than a toothpick. Too small for propellers, this teensy-weensy bot takes off by rapidly flapping its wings. The challenge: Insect-bots require a relatively large amount of power to move their wings fast enough to take off. Batteries are too large and heavy to fly, so previous robots of this size had to be plugged in.

Structural causal models are a popular tool to describe causal relations in systems in many fields such as economy, the social sciences, and biology. In this work, we show that these models are not flexible enough in general to give a complete causal representation of equilibrium states in dynamical systems that do not have a unique stable equilibrium independent of initial conditions. We prove that our proposed generalized structural causal models do capture the essential causal semantics that characterize these systems. We illustrate the power and flexibility of this extension on a dynamical system corresponding to a basic enzymatic reaction. We motivate our approach further by showing that it also efficiently describes the effects of interventions on functional laws such as the ideal gas law.

Kernel transfer operators, which can be regarded as approximations of transfer operators such as the Perron-Frobenius or Koopman operator in reproducing kernel Hilbert spaces, are defined in terms of covariance and cross-covariance operators and have been shown to be closely related to the conditional mean embedding framework developed by the machine learning community. The goal of this paper is to show how the dominant eigenfunctions of these operators in combination with gradient-based optimization techniques can be used to detect long-lived coherent patterns in high-dimensional time-series data. The results will be illustrated using video data and a fluid flow example.

The analysis of complex systems such as inventory control, financial markets, waste-to-energy plants and computer networks is difficult because of the inherent uncertainties in these systems. Risk-aware optimization offers a possible remedy by giving stronger reliability guarantees than the risk-neutral case. Furthermore, it allows expression of the risk attitude of the decision maker. Risk awareness is especially important in sequential decision making because of the dynamic nature of the uncertainty. Markov decision processes (MDPs) introduced by Bellman in [10] provide a mathematical framework for modeling sequential decision making in situations where outcomes are partly random and partly under the control the decision maker. However, in many cases the exact model of the underlying Markov decision process is not known and one can only observe the trajectory of states, actions, and rewards/costs.

Machine Learning helps make complex systems more efficient. By applying advanced Machine Learning techniques such as Cognitive Fingerprinting, wind project operators can utilize these tools to learn from collected data, detect regular patterns, and optimize their own operations. In his session at 18th Cloud Expo, Stuart Gillen, Director of Business Development at SparkCognition, discussed how research has demonstrated the value of Machine Learning in delivering next generation analytics to improve safety, performance, and reliability in today's modern wind turbines. Speaker Bio Stuart Gillen is the Director of Business Development at SparkCognition. In this role, he is responsible for driving business engagements, partner development, marketing activities, and go-to market strategy.

For robots of all sizes, power is a fundamental problem. Any robot that moves is constrained in one way or another by power supply, whether it's relying on carrying around heavy batteries, combustion engines, fuel cells, or anything else. It's particularly tricky to manage power as your robot gets smaller, since it's much more straightforward to scale these things up rather than down--and for really tiny robots (with masses in the hundreds of milligrams range), especially those that demand a lot of power, there really isn't a good solution. In practice, this means that on the scale of small insects robots often depend on tethers for power, which isn't ideal for making them practical in the long term. At the IEEE International Conference on Robotics and Automation in Brisbane, Australia, next week, roboticists from the University of Washington, in Seattle, will present RoboFly, a laser-powered insect-sized flapping wing robot that performs the first (very brief) untethered flight of a robot at such a small scale.

In 1989, two MIT artificial intelligence researchers made a terrifying prediction. "Within a few years," wrote Rodney Brooks and Anita Flynn, "it will be possible at modest cost to invade a planet with millions of tiny robots." Their paper "Fast, Cheap and out of Control: A Robot Invasion of the Solar System,", argued that small, autonomous "gnat robots" would soon become cheap enough to solve problems en masse. Nearly three decades later, those millions of tiny robots have yet to take over, at least not exactly like Brooks and Flynn envisioned. While they were right in some ways--the world has more than 700 million active iPhones--the vision of the fast, autonomous, tiny, buzzing bot is still a dream.

Analysis of reactive-diffusion simulations requires a large number of independent model runs. For each high-fidelity simulation, inputs are varied and the predicted mixing behavior is represented by changes in species concentration. It is then required to discern how the model inputs impact the mixing process. This task is challenging and typically involves interpretation of large model outputs. However, the task can be automated and substantially simplified by applying Machine Learning (ML) methods. In this paper, we present an application of an unsupervised ML method (called NTFk) using Non-negative Tensor Factorization (NTF) coupled with a custom clustering procedure based on k-means to reveal hidden features in product concentration. An attractive aspect of the proposed ML method is that it ensures the extracted features are non-negative, which are important to obtain a meaningful deconstruction of the mixing processes. The ML method is applied to a large set of high-resolution FEM simulations representing reaction-diffusion processes in perturbed vortex-based velocity fields. The applied FEM ensures that species concentration are always non-negative. The simulated reaction is a fast irreversible bimolecular reaction. The reactive-diffusion model input parameters that control mixing include properties of velocity field, anisotropic dispersion, and molecular diffusion. We demonstrate the applicability of the ML method to produce a meaningful deconstruction of model outputs to discriminate between different physical processes impacting the reactants, their mixing, and the spatial distribution of the product. The presented ML analysis allowed us to identify additive features that characterize mixing behavior.