The Mill's Blackbird is a fully adjustable car rig used to create photorealistic CG vehicles. VP of Advanced Manufacturing Technologies advises on budgeting, partnerships and common mistakes for SMES. BREAKING: What Does Brexit Mean for UK Manufacturing? Proposes Cloud Robotics for 3D Print Farm Automation Tesla Motor Company wants to purchase SolarCity. Here are some ways that such a merger could affect the solar industry.

It is the future of shipping - and there's not a single sailor on board. Rolls Royce has revealed planed for fleets of'drone ships' to ferry carry around the world - all controlled from a central'holodeck'. It believes an entirely unmanned ship could take to the seas by 2020. Rolls Royce said it has already begun testing the technology needed to make the ships a reality, and expected them to take to the sea by the end of the decade. Cameras would beam 360-degree views from the drone ship back to operators based in a virtual bridge.

It is a sophisticated instrument that intertwines ultra rapid computing with innovative material science and power electronics to optimize and balance electrical energy flow in real time in any electrical environment. It ensures that electricity is distributed with perfect stability and consumed with ideal efficiency. The SineSync is the Battery Management System that leverages this incredible technology to create a true digital microgrid in a box. Perfectly optimized electrical energy flow delivers higher quality and more stable power with the lowest possible power consumption. The entire residential electrical network is protected with renewable lightning protection.

Long ago I lived in Santa Cruz, California. Almost every morning I would throw on a wet suit, grab my surfboard out of the garage, and head to the rocky cliffs just a few blocks from my house. I would descend a well-worn path to the ocean below, paddle out to the break, and spend hours surrounded by kelp beds and barking sea lions, catching waves, feeling exhilarated, and floating on my board, a world away from the troubles on land. I have a family now and have lived for years in the generally wave-less realms of New York City. But a few months ago I suddenly felt that old hunger again. I wanted to race out to the garage and grab a board.

Utilities are significantly increasing data gathering and using external data sets for smarter capacity and investment planning. Everything from weather data to 3D modeling of networks and extraction sites is being gathered, combined with internal and historical data sets and used to inform, predict and plan a variety of business outcomes. While not cheap to obtain, data is being used to make informed decisions on everything from when to shut down power stations to avoid over-capacity in the market, to where to drill and how to manage fluctuating water supplies. For example, moves in several markets to deploy smart meters, capable of feeding back consumption data in near real time, provide a valuable data resource for energy companies. This flow of information--which can update as frequently as every 30 seconds--provides valuable insight into real-world energy consumption and can provide early warning of peaks in demand.

Data analytics firm Orbital Insight is partnering with the World Bank to test technology that could help measure global poverty using satellite imagery and artificial intelligence. The new partnership will test the use of AI to supplement these surveys and increase the accuracy of poverty data. Orbital said its AI software will analyze satellite images to see if characteristics such as building height and rooftop material can effectively indicate wealth. The pilot study will be conducted in Sri Lanka. If successful, the World Bank hopes to scale it worldwide.

The problem of probabilistic forecasting and online simulation of real-time electricity market with stochastic generation and demand is considered. By exploiting the parametric structure of the direct current optimal power flow, a new technique based on online dictionary learning (ODL) is proposed. The ODL approach incorporates real-time measurements and historical traces to produce forecasts of joint and marginal probability distributions of future locational marginal prices, power flows, and dispatch levels, conditional on the system state at the time of forecasting. Compared with standard Monte Carlo simulation techniques, the ODL approach offers several orders of magnitude improvement in computation time, making it feasible for online forecasting of market operations. Numerical simulations on large and moderate size power systems illustrate its performance and complexity features and its potential as a tool for system operators.

PRO: Chris Bishop, director of Microsoft Research in Cambridge, said earlier this year that he believes the hyperbole around the AI risks could jeopardise any future developments that may in fact assist humanity. "Any scenario in which AI is an existential threat to humanity is not just around the corner," he told the Guardian. Referring to the views of high-profile cynics like professor Stephen Hawking, Bishop said: "I think they must be talking decades away for those comments to make any sense. Right now we are in control of that technology and we can make lots of choices about the paths that we follow." Oren Etzioni, chief executive of the Allen Institute for AI and professor of computer science at the University of Washington, meanwhile says the popular dystopian vision of AI is wrong because it "equates intelligence with autonomy".

Whenever people go from one place to another, they don't think much about the roads and sidewalks that pass beneath them. But this infrastructure, known as the public right-of-way, doesn't work by magic. It is managed and regulated by specific laws. People don't own the roads they travel on, but streets and sidewalks provide an easement--a right of use or passage separate from that of ownership. For example: a single-family homeowner usually owns the sidewalk that flanks a property, particularly if that sidewalk falls behind a tree-planting strip that separates it from the street. By local standard or writ, the homeowner grants an easement to the general public to use the sidewalk, utility companies to use the curb where utility lines run, and so forth.

We introduce structured prediction energy networks (SPENs), a flexible framework for structured prediction. A deep architecture is used to define an energy function of candidate labels, and then predictions are produced by using back-propagation to iteratively optimize the energy with respect to the labels. This deep architecture captures dependencies between labels that would lead to intractable graphical models, and performs structure learning by automatically learning discriminative features of the structured output. One natural application of our technique is multi-label classification, which traditionally has required strict prior assumptions about the interactions between labels to ensure tractable learning and prediction. We are able to apply SPENs to multi-label problems with substantially larger label sets than previous applications of structured prediction, while modeling high-order interactions using minimal structural assumptions. Overall, deep learning provides remarkable tools for learning features of the inputs to a prediction problem, and this work extends these techniques to learning features of structured outputs. Our experiments provide impressive performance on a variety of benchmark multi-label classification tasks, demonstrate that our technique can be used to provide interpretable structure learning, and illuminate fundamental trade-offs between feed-forward and iterative structured prediction.