A team of researchers from the University of North Carolina at Chapel Hill have unveiled a 3D printer that uses light to create an entire object at once. It's called the Replicator, named after the machines in the Star Trek universe that can synthesize food, water, air and various objects seemingly out of nothing. Before you get too excited, the researchers didn't quite create an exact replica of that fictional machine, but it still offers a new and promising 3D printing technique. According to the team's paper published in Science, the Replicator works like a reverse CT scan. When a patient undergoes the procedure, an X-ray tube rotates around their body to take multiple photos that a computer can use to create 3D images.

Experts warn ML should be used "for its correct purpose" – not for prying long-term strategies from sparse information Data is the latest of many hopes for banks and other investors looking for improved returns in a lacklustre environment. Several banks have begun to point their research teams at big data – using internal data, purchased databases or new research to collect huge quantities of data points, which can then be analysed using the new technology of machine learning (ML). UBS seems to be in the lead at present, but Morgan Stanley, BNP Paribas and many others are following. And this combination is being applied elsewhere as well; last week Risk looked at HSBC's client intelligence unit, which is aimed at using internal client data to generate new sales leads for existing customers. Standard Chartered's data analytics group earned a 2019 Risk Award for quant of the year for its head Alexei Kondratyev, based on the group's machine learning work.

John Deere has been to CES before. The company known for its dark green tractors with the yellow deer on them has rubbed shoulders with the smart TVs, smart light bulbs, smart cars, smart switches and smart toothbrushes for years. But 2019 was a bit different. "We do consider ourselves a technology company. We wanted to engage the tech company and tell the story of agriculture," said Julian Sanchez, John Deere's director of technology innovation.

Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or commercial buildings. The methods for optimizing academic-buildings are distinct from the optimal methods for home appliances. In my study, we address a novel methodology to control the operation of heating, ventilation, and air conditioning system (HVAC). With the development of Artificial Intelligence and computer technologies, reinforcement learning (RL) can be implemented in multiple realistic scenarios and help people to solve thousands of real-world problems. Reinforcement Learning, which is considered as the art of future AI, builds the bridge between agents and environments through Markov Decision Chain or Neural Network and has seldom been used in power system. The art of RL is that once the simulator for a specific environment is built, the algorithm can keep learning from the environment. Therefore, RL is capable of dealing with constantly changing simulator inputs such as power demand, the condition of power system and outdoor temperature, etc. Compared with the existing distribution power system planning mechanisms and the related game theoretical methodologies, our proposed algorithm can plan and optimize the hourly energy usage, and have the ability to corporate with even shorter time window if needed.

Many believe we are living within the fourth industrial revolution, as the impact of smart technologies overtakes every area of our lives, including the engineering industry. It is marked by emerging technology breakthroughs in robotics, artificial intelligence, the Internet of Things, additive manufacturing/3D printing and fully autonomous vehicles. This revolution is expected to impact all disciplines, industries, and economies as connected machines begin to interact and, ultimately, even make decisions autonomously. The growth of additive manufacturing, a process that builds parts layer-by-layer from sliced CAD models to form solid objects, has the potential to become a new key technology. Electronics, engineering and medicinal electrical devices specialist Siemens has been investing in this innovative technology right from its inception, and is now driving the industrialisation and commercialisation of these processes.

Predictive maintenance based on machine learning has reached the point where it can benefit virtually every manufacturer, big or small, an expert will tell engineers at the upcoming Pacific Design & Manufacturing Show. Kayed Almasarweh, IBM's Watson and cognitive solutions lead, contends that machine learning and artificial intelligence can minimize unplanned downtime, eliminate maintenance guesswork, optimize supply chain management, and reduce warranty costs in products, if used correctly. "This is not only for big manufacturing operations; it's for everybody," Almasarweh told Design News. "Once you get it implemented with the right data, you can get a return on investment almost immediately." Almasarweh will provide a high-level view of predictive maintenance based on machine learning in a session titled, "Applying IoT and Machine Learning for Predictive Maintenance," at the Anaheim Convention Center on February 6th.

Whill released its first personal electric vehicle in 2016 (in Japan, it came west the following year). Since then it has released new models with different audiences in mind. Here at CES 2019, it's showing its Autonomous Drive System (ADS), which as the name suggests, will shuttle you to where you want to go on its own. Thanks to the addition of front- and rear-mounted cameras, the Autonomous Drive System can navigate public (indoor or outdoor) spaces on its own. A spokesperson told me that the ADS was partly its answer to scooters and bikes offered by the likes of Lime and Bird.

Sam Rothwell, HSO team lead, explores the manufacturing trends that we may see appearing in 2019. The comments below explore why there will be a further rise in the use of technologies such as AI, 3D printing and IoT as they become more affordable and are increasingly seen as vital tools to manufacturers. Rothwell says: "In the manufacturing sector, we have been seeing the rise of a number of key technologies recently, specifically artificial intelligence (AI), Internet of Things (IoT) and additive manufacturing (3D printing). "We expect, in 2019, to see this trend continue, especially in AI and IoT adoption, as manufacturers are increasing investment in these now affordable technologies which are underpinning more accurate decision making and unlocking new business models, such as shifting towards service-based product offerings. "The main operational areas we see potential in, are primarily around the predictive maintenance area, lowering costs by collecting data on real-world performance versus expected, then allowing adjustments to be made to future design. "This improvement in predictive maintenance has also allowed manufacturers to think differently about how they deliver value to their customer base, shifting from specific and individual product sales, towards ongoing subscription or per-usage billing.

This image shows the original paintings (across the top), as illuminated by different light sources (from left, 6410K, 4291K & 3410K) and below are the same paintings reproduced by the AI in RePaint. This image shows that RePaint works effectively in dramatically different lighting conditions.MIT CSAIL A team from the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) has designed a system called RePaint that uses artificial intelligence (AI) and 3D printing to reproduce paintings. The system is a workflow for spectral reproduction of paintings can capture the spectral color, regardless of light, and reproduce it. This technology could be used by museums to reproduce artwork that has been stolen or is on loan to another museum. Despite the fact that the reproductions made by the researchers were only the size of a business card, the team noted that RePaint was four times more accurate than state-of-the-art physical models at creating the exact color shades for different artworks.

If you've ever wanted to get your hands on the Mona Lisa, you may just be in luck. For researchers have found a way for art fanatics to create their own version of a priceless masterpiece, through a combination of AI and 3D printing. The replicas have been made by researchers from the Massachusetts Institute of Technology (MIT) using a piece of software called'RePaint'. The replicas have been made by researchers from the Massachusetts Institute of Technology (MIT) using a piece of software called'RePaint' (pictured) Despite the progress so far, the team says they have a few improvements to make before they can whip up a dazzling dupe of'Starry Night.' 'If you just reproduce the colour of a painting as it looks in the gallery, it might look different in your home,' says Changil Kim, one of the researchers from MIT that published a paper on the system, which will be presented in December. 'Our system works under any lighting condition, which shows a far greater colour reproduction capability than almost any other previous work.'