We may be decades from unlocking the true power of quantum computing, but D-Wave is promising to offer a taste of the future with its significantly upgraded quantum processor. When it is released early next year, the Canadian firm's new quantum chip will be able to handle some 2,000 quantum bits (qubits), roughly double the usable number found in the processor in the existing D-Wave 2X system, and be capable of solving certain problems 1,000x faster than its predecessor. D-Wave machines are multi-million dollar computers that crunch data using "quantum transistors", tiny loops of niobium cooled to close to absolute zero by liquid helium. Only a handful of such systems are in use, run by Google and the Universities Space Research Association, Lockheed Martin and Los Alamos National Laboratory. However, D-Wave also offers access to its quantum computers via a cloud service.

IBM's Project DataWorks in action: A NYC-based startup that develops clean energy projects in American inner cities used Project DataWorks to build a cognitive application that performs a comprehensive energy audit of individual properties to simulate energy savings and ultimate determine the correct mix of high-efficiency technology to reduce each customer's energy consumption. IBM's Project DataWorks uses Watson Analytics to analyze and create complex visualizations IBM's Project DataWorks uses Watson Analytics and natural language processing to analyze and create complex visualizations with one line of code – like this one, which illustrates correlations between product purchases by customers of a sporting goods store. IBM's Project DataWorks helps users access and gain insights from the 90% of unstructured data that goes untapped by organizations (according to IDC). The Console pictured here provides a snapshot that categorizes and previews an organization's data assets for easy access while also providing a full audit trail that allows users to understand who else on their team is interacting with the data and how.

According to Tractica's research, one of the industries best positioned to leverage artificial intelligence (AI) – at least in the developed world – is agriculture. In our Artificial Intelligence for Enterprise Applications report, we forecast that spending on AI software in the agriculture industry will grow from 16.2 million to 373.7 million by 2024. Recently we sat down with Jeffrey Orrey, CEO of GeoVisual Analytics. GeoVisual is a Boulder-based startup focused on using remote sensing and big data analytics to improve and predict crop yields, better manage croplands, and improve harvests. The company's analysis is based on the properties of electromagnetic waves in the near infrared (NIR) spectrum, which are invisible to the human eye.

One of our neighbours is a company called Rainbird which is building a commercial AI platform. We paid them a visit to learn more about AI and its intersection with design. Artificial Intelligence is a very wide, multi-disciplinary field. This means that there's no single model for how software and hardware come together to create practical applications. Knowledge modelling: This is a way to gather and organise human input to capture a particular area of knowledge.

It is projected that urban India will contribute about 75% of the national GDP in the next 15 years. With the rapid rate of urbanization and depleting resources, it is essential for India to rethink its strategy to see how it can best optimize the capabilities of its physical infrastructure. By leveraging global best practices in smart city development and ICT (Information and Communication Technology), India is in a unique position to leapfrog a few stages of growth to become a global superpower. In 2015, the Indian government announced an ambitious plan to develop 100 smart cities, with a commitment of spending 7.27 billion over the next five years. The infrastructure overhaul would include creating intelligent transport systems, smart grids, smart waste management, and smart water grids/solutions.

The literature on judgment aggregation is moving from studying impossibility results regarding aggregation rules towards studying specific judgment aggregation rules. Here we give a structured list of most rules that have been proposed and studied recently in the literature, together with various properties of such rules. We first focus on the majority-preservation property, which generalizes Condorcet-consistency, and identify which of the rules satisfy it. We study the inclusion relationships that hold between the rules. Finally, we consider two forms of unanimity, monotonicity, homogeneity, and reinforcement, and we identify which of the rules satisfy these properties.

Many machine learning tasks can be formulated as Regularized Empirical Risk Minimization (R-ERM), and solved by optimization algorithms such as gradient descent (GD), stochastic gradient descent (SGD), and stochastic variance reduction (SVRG). Conventional analysis on these optimization algorithms focuses on their convergence rates during the training process, however, people in the machine learning community may care more about the generalization performance of the learned model on unseen test data. In this paper, we investigate on this issue, by using stability as a tool. In particular, we decompose the generalization error for R-ERM, and derive its upper bound for both convex and non-convex cases. In convex cases, we prove that the generalization error can be bounded by the convergence rate of the optimization algorithm and the stability of the R-ERM process, both in expectation (in the order of $\mathcal{O}((1/n)+\mathbb{E}\rho(T))$, where $\rho(T)$ is the convergence error and $T$ is the number of iterations) and in high probability (in the order of $\mathcal{O}\left(\frac{\log{1/\delta}}{\sqrt{n}}+\rho(T)\right)$ with probability $1-\delta$). For non-convex cases, we can also obtain a similar expected generalization error bound. Our theorems indicate that 1) along with the training process, the generalization error will decrease for all the optimization algorithms under our investigation; 2) Comparatively speaking, SVRG has better generalization ability than GD and SGD. We have conducted experiments on both convex and non-convex problems, and the experimental results verify our theoretical findings.

All those cars on California's famously gridlocked highways could be doing more than using energy. They could be producing it. The California Energy Commission is investing 2 million to study whether piezoelectric crystals can be used to produce electricity from the mechanical energy created by vehicles driving on roads. The commission is choosing a company or university to take on small-scale field tests. It will study how the small crystals, which generate energy when compressed, could produce electricity for the grid if installed under asphalt.

U.S. stocks are slumping in Monday morning trading as healthcare companies and banks take the biggest losses. Energy companies are inching higher as oil prices rise. Major indexes in Europe and Asia are also starting the week on a steep skid. The Dow Jones industrial average was down 108 points, or 0.6%, to 18,153 as of 10:15 a.m. The Standard & Poor's 500 index fell 10 points, or 0.4%, to 2,155.

By building on a recently introduced genetic-inspired attribute-based conceptual framework for safety risk analysis, we propose a novel methodology to compute construction univariate and bivariate construction safety risk at a situational level. Our fully data-driven approach provides construction practitioners and academicians with an easy and automated way of extracting valuable empirical insights from databases of unstructured textual injury reports. By applying our methodology on an attribute and outcome dataset directly obtained from 814 injury reports, we show that the frequency-magnitude distribution of construction safety risk is very similar to that of natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state-of-the-art techniques in hydroclimatology and insurance, we introduce univariate and bivariate nonparametric stochastic safety risk generators, based on Kernel Density Estimators and Copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithfully to the original data, allowing safetyrelated decision-making under uncertainty to be grounded on extensive empirical evidence. Just like the accurate modeling and simulation of natural phenomena such as wind or streamflow is indispensable to successful structure dimensioning or water reservoir management, we posit that improving construction safety calls for the accurate modeling, simulation, and assessment of safety risk. The underlying assumption is that like natural phenomena, construction safety may benefit from being studied in an empirical and quantitative way rather than qualitatively which is the current industry standard. Finally, a side but interesting finding is that attributes related to high energy levels and to human error emerge as strong risk shapers on the dataset we used to illustrate our methodology.