Chemical multisensor devices need calibration algorithms to estimate gas concentrations. Their possible adoption as indicative air quality measurements devices poses new challenges due to the need to operate in continuous monitoring modes in uncontrolled environments. Several issues, including slow dynamics, continue to affect their real world performances. At the same time, the need for estimating pollutant concentrations on board the devices, espe- cially for wearables and IoT deployments, is becoming highly desirable. In this framework, several calibration approaches have been proposed and tested on a variety of proprietary devices and datasets; still, no thorough comparison is available to researchers. This work attempts a benchmarking of the most promising calibration algorithms according to recent literature with a focus on machine learning approaches. We test the techniques against absolute and dynamic performances, generalization capabilities and computational/storage needs using three different datasets sharing continuous monitoring operation methodology. Our results can guide researchers and engineers in the choice of optimal strategy. They show that non-linear multivariate techniques yield reproducible results, outperforming lin- ear approaches. Specifically, the Support Vector Regression method consistently shows good performances in all the considered scenarios. We highlight the enhanced suitability of shallow neural networks in a trade-off between performance and computational/storage needs. We confirm, on a much wider basis, the advantages of dynamic approaches with respect to static ones that only rely on instantaneous sensor array response. The latter have been shown to be best choice whenever prompt and precise response is needed.

With hype around artificial intelligence (AI) at an all-time high, Gartner says many CIOs are understandably cautious about promoting its potential value to their business. But a new report from Gartner calls on CIOs to start investing now on AI. Digital business pressure combined with the rapid pace of innovation make it a great time for CIOs to aggressively learn how AI might influence the business strategy over the next two to four years, according to the authors of the report, Gartner analysts Janelle B. Hill, Bern Elliot and Jamie Popkin. They say CIOs can get involved at the front end of business strategy development, and educating their CEO and the board about recent developments in AI. In these discussions, CIOs can promote AI's massive potential to disrupt markets and remake existing business models, not just as an output that further automates existing capabilities.

This paper presents a constructive algorithm that achieves successful one-shot learning of hidden spike-patterns in a competitive detection task. It has previously been shown (Masquelier et al., 2008) that spike-timing-dependent plasticity (STDP) and lateral inhibition can result in neurons competitively tuned to repeating spike-patterns concealed in high rates of overall presynaptic activity. One-shot construction of neurons with synapse weights calculated as estimates of converged STDP outcomes results in immediate selective detection of hidden spike-patterns. The capability of continual learning is demonstrated through the successful one-shot detection of new sets of spike-patterns introduced after long intervals in the simulation time. Simulation expansion (Lightheart et al., 2013) has been proposed as an approach to the development of constructive algorithms that are compatible with simulations of biological neural networks. A simulation of a biological neural network may have orders of magnitude fewer neurons and connections than the related biological neural systems; therefore, simulated neural networks can be assumed to be a subset of a larger neural system. The constructive algorithm is developed using simulation expansion concepts to perform an operation equivalent to the exchange of neurons between the simulation and the larger hypothetical neural system. The dynamic selection of neurons to simulate within a larger neural system (hypothetical or stored in memory) may be a starting point for a wide range of developments and applications in machine learning and the simulation of biology.

Regression analysis is a statistical tool to determine relationships between different types of variables. Variables that remain unaffected by changes made in other variables are known as independent variables, also known as a predictor or explanatory variables while those that are affected are known as dependent variables also known as the response variable. Linear regression is a statistical procedure which is used to predict the value of a response variable, on the basis of one or more predictor variables. Some common examples of linear regression are calculating GDP, CAPM, oil and gas prices, medical diagnosis, capital asset pricing etc. R Simple linear regression enables us to find a relationship between a continuous dependent variable Y and a continuous independent variable X. It is assumed that values of X are controlled and not subject to measurement error and corresponding values of Y are observed.

Apple will unveil the eagerly anticipated iPhone 8 at a special event on 12th September in the Steve Jobs Theater inside Apple's new'Spaceship' HQ, it has been claimed. The event is also expected to see the launch of an iPhone 7s and 7s Plus, a new version of the apple watch and a new 4K Apple TV. It is believed the new iPhone 8 will go on sale on September 22nd. The Steve Jobs Theater is situated on top of a hill -- one of the highest points within Apple Park -- overlooking meadows and the main building. The latest claims from the Wall Street Journal are that'The company is expected to unveil three iPhones, according to other people familiar with its plans.'

Smart Wind and Solar Power Big data and artificial intelligence are producing ultra-accurate forecasts that will make it feasible to integrate much more renewable energy into the power grid. Researchers around the world are collecting wind speed and output data from wind turbines. Artificial-intelligence-based software are then fed the output, along with data from weather satellites, weather stations, and other wind farms. The result: wind power forecasts of unprecedented accuracy are making it possible to use far more renewable energy, at lower cost, than utilities ever thought possible. While solar power generation lags wind power production, researchers are furiously working around the world to better harness the sun's abundant power.

Hopkinsville, Kentucky, is normally a mid-size town, home to 32,000 people and a big bowling ball manufacturer. But on August 21, its human density more than tripled, as around 100,000 people swarmed toward the total solar eclipse. Hundreds of miles above the crowd, high-resolution satellites stared down, snapping images of the sprawl. These satellites belong to a company called DigitalGlobe, and their cameras are sharp enough to capture a book on a coffee table. And a lot can happen between brunch and dinner.

LaserSnake2 is a highly flexible robot that's ideal for working in confined and hazardous spaces like aircraft assembly, nuclear power stations or the inspection of sewage systems.

IMAGE: Scientists will use ORNL's computing resources such as the Titan supercomputer to develop deep learning solutions for data analysis. A team of researchers from Oak Ridge National Lab oratory has been awarded nearly $2 million over three years from the Department of Energy to explore the potential of machine learning in revolutionizing scientific data analysis. The Advances in Machine Learning to Improve Scientific Discovery at Exascale and Beyond (ASCEND) project aims to use deep learning to assist researchers in making sense of massive datasets produced at the world's most sophisticated scientific facilities. Deep learning is an area of machine learning that uses artificial neural networks to enable self-learning devices and platforms. The team, led by ORNL's Thomas Potok, includes Robert Patton, Chris Symons, Steven Young and Catherine Schuman.

Living in New England, it seems like I can never catch a break from high utility bills. If paying your utilities hurts your wallet every month, there are a lot of different ways you can slash those bills down to a more manageable number. For one, smart home technology can help you be more efficient with both heating and cooling, as well as with water and electricity use. Here are 10 smart products that can help reduce your utility bills and put money back in your pocket. Are you forever leaving the living room light on?