Researchers from the University of Houston have devised a new machine learning algorithm that is efficient enough to run on a personal computer and predict the properties of more than 100,000 compounds in search of those most likely to be efficient phosphors for LED lighting. Jakoah Brgoch, assistant professor of chemistry, and members of his lab describe the work a paper published Oct. 22 in Nature Communications. The researchers used machine learning to quickly scan huge numbers of compounds for key attributes, including Debye temperature and chemical compatibility. Brgoch previously demonstrated that Debye temperature is correlated with efficiency. LED, or light-emitting diode, based bulbs work by using small amounts of rare earth elements, usually europium or cerium, substituted within a ceramic or oxide host--the interaction between the two materials determines the performance.

The UCR Time Series Archive - introduced in 2002, has become an important resource in the time series data mining community, with at least one thousand published papers making use of at least one dataset from the archive. The original incarnation of the archive had sixteen datasets but since that time, it has gone through periodic expansions. The last expansion took place in the summer of 2015 when the archive grew from 45 datasets to 85 datasets. This paper introduces and will focus on the new data expansion from 85 to 128 datasets. Beyond expanding this valuable resource, this paper offers pragmatic advice to anyone who may wish to evaluate a new algorithm on the archive. Finally, this paper makes a novel and yet actionable claim: of the hundreds of papers that show an improvement over the standard baseline (1-Nearest Neighbor classification), a large fraction may be misattributing the reasons for their improvement. Moreover, they may have been able to achieve the same improvement with a much simpler modification, requiring just a single line of code.

Technology giant Huawei aims to help train one million artificial intelligence (AI) talents in the next three years to boost the fast-expanding sector. Huawei will provide free online training, organise boot camps and collaborate with industry players. It will also set up a one billion yuan (S$199 million) fund for universities and research institutes to support AI talent development. Mr Zheng Yelai, Huawei's vice-president and president of its cloud business unit, announced this yesterday, the last day of the Huawei Connect Conference in Shanghai. The move is in line with China's push to become a global AI powerhouse in the next decade.

Computers used to take up entire rooms. Today, a two-pound laptop can slide effortlessly into a backpack. But that wouldn't have been possible without the creation of new, smaller processors -- which are only possible with the innovation of new materials. But how do materials scientists actually invent new materials? Through experimentation, explains Sanket Deshmukh, an assistant professor in the chemical engineering department whose team's recently published computational research might vastly improve the efficiency and costs savings of the material design process.

Well-known Belfast startup Axial3D produces 3D prints of your body parts. This isn't to satisfy the narcissistic social media types – it has important surgical implications. This previous TechWatch article describes the company's process. Now, Axial3D is developing new AI techniques to make instantaneous the transition from 2D images to 3D prints. How are they doing that?

My sandwich looked like something out of a restaurant commercial: glossy yellow cheese oozing between two golden-brown slices of toast. The first bite should have been rich, gooey and decadent. I stalked back to the kitchen, pulled open the fridge door, and snatched the culprit. Printed on the back of the plastic package--Mexican Style Blend Finely Shredded Cheese, left by a visiting friend--was an ingredient list longer than just "cheese." Food additives are a fact of modern life--they improve shelf-life, flavor, texture, consistency and color.

Kubernetes is becoming synonymous with cloud-native computing. As an open-source platform, it enables development, deployment, orchestration and management of containerized microservices across multicloud ecosystems. Kubernetes is the key to cloud-native microservices that are platform agnostic, dynamically managed, loosely coupled, distributed, isolated, efficient, and scalable. The maturation of Kubernetes continues to deepen as it leverages containers, orchestrations, service meshes, immutable infrastructure, and declarative APIs. One clear indicator of Kubernetes' maturation is the rich ecosystem of other open-source projects that have grown up around it.

Dutch scientists have developed robot bees which could help pollinate plants without the use of insects. Researchers at Delft University of Technology in the Netherlands believe they may have solved the problem of climate change or pesticides killing off the creatures. The DelFly Nimble's wings beat at 17 times per second to power the robot at speeds over 15 miles per hour (25kph). However, they share an uncanny resemblance to robot bees that are hacked and turned into killing machines in the popular science fiction series Black Mirror. It uses off-the-shelf components, making it cheap to build, and scientists say it could be used in a host of real-world applications.

Robotic arms have come a long way since they were first introduced in 1951. Today, rows of industrial robotic arms help automate tasks and boost productivity in many industries including automotive, electronic, chemical production, food processing and so on. In this report, we first examine the different types of industrial arms, assessing the merits of DELTA, SCARA, articulated and Cartesian types. We then demonstrate how the market for industrial robotic arms has evolved in the past twenty years, tracing the historical market development in annual unit numbers and value (robotic arm and total system value). Here, we look at market segmentation by application and territory.

Then there are those companies that view AI as a transformational platform that has the potential to create a new species of business. These future-minded companies are centering on the customer and using AI to connect people and processes and predict business outcomes, capitalizing on new revenue opportunities by applying new forms of intelligence. With research showing that half of the S&P 500 will be replaced over the next 10 years, organizations that view AI as merely a trend or a quick antidote to a business problem risk falling to the back of the pack or vanishing altogether. Meanwhile, businesses that are successfully building for the future realize that the actions they take today will permit their enterprises to sense and predict with accuracy so they can make better, faster decisions that transform the customer experience. In the current environment, not all businesses are created equal, says Sanjay Srivastava, chief digital officer at Genpact: "Firms that embed AI as their central nervous system will unlock a significantly more dynamic and powerful tomorrow and will act more like living organisms--enabling them to operate instinctively. They'll augment intelligence, anticipate disruption, outmaneuver threats, and connect more closely with customers. As this new breed of business evolves, it will have the ability to reshape industries and amplify human potential in ways that we've not yet even imagined."