Vienna, November 19th of 2019, Semantic Web Company (Austria) and PANTOPIX (Germany) have announced a comprehensive cooperation to provide the machinery industry with expertise in metadata management and structured information. Semantic Web Company (SWC), based in Vienna, is the leading provider of graph-based metadata management. The Germany Company PANTOPIX is a high-end specialist for improving information processes, developing data models as well as providing intelligent information for technical documentation. The key pillar of the partnership is to develop taxonomies, ontologies and large-scale Enterprise Knowledge Graphs to make target-oriented technical content available to internal and external customers. Knowledge Graphs enable companies to process large amounts of data from various silos and adding value to it so that it can be used in meaningful and more intelligent ways. It provides a structure and common interface for all data and enables the creation of smart multilateral relations throughout databases.

The way products are designed and engineered is changing thanks to new technologies. These technologies, from digital twins to 3D printing, not only support humans in their design and engineering work, but they can also efficiently uncover new ways of solving problems that humans hadn't thought of before. The human professionals in design and engineering roles in organizations will see changes to their job duties, will be challenged to acquire new skills and flexibility, and learn new ways of collaborating with machines. They also need to learn how to work with new design, engineering, and product development tools enabled by these new technologies. Organizations and professionals in engineering and design roles can't ignore the changes if they want to remain competitive.

Robots will soon be everywhere – especially if ordinary objects can be turned into them. A computer program can now use 3D-printing to turn household objects into hand-activated robots. It can be used to turn on the water taps on a bathroom sink with the wave of a hand, or to give a window the ability to shut itself when the weather gets cold. Xiang'Anthony' Chen at the University of California in Los Angeles and colleagues developed the tool, known as Robiot, to automate simple physical tasks.

Machining businesses mired in time-consuming traditional quoting might be surprised to learn that many of their counterparts are winning work with little more than a mouse click. Precisely how many is anyone's guess. Although these are top performers, other CNC machine shops constitute the majority of the 2,500-plus manufacturers bidding on this online platform (others include injection molders, additive manufacturing services and parts finishers). While many of the manufacturers are small, many of the parts purchasers are large. Some, like Bosch and BMW, have become investors, pouring more than $50 million into the company since May.

For a factory where robots toil around the clock to build a rocket with almost no human labour, the sound of grunts echoing across the parking lot make for a jarring contrast. "That's Keanu Reeves' stunt gym," says Tim Ellis, the chief executive and cofounder of Relativity Space, a startup that wants to combine 3D printing and artificial intelligence to do for the rocket what Henry Ford did for the automobile. As we walk among the robots occupying Relativity's factory, he points out the just-completed upper stage of the company's rocket, which will soon be shipped to Mississippi for its first tests. Across the way, he says, gesturing to the outside world, is a recording studio run by Snoop Dogg. Neither of those A-listers have paid a visit to Relativity's rocket factory, but the presence of these unlikely neighbours seems to underscore the company's main talking point: It can make rockets anywhere.

On a meetup that I attended a couple of months ago in Sydney, I was introduced to an online machine learning course by fast.ai. I never paid any attention to it then. This week, while working on a Kaggle competition, and looking for ways to improve my score, I came across this course again. I decided to give it a try. Here is what I learned from the first lecture, which is a 1 hour 17 minutes video on INTRODUCTION TO RANDOM FOREST.

Universities are embracing artificial intelligence solutions to assist in IT projects and academics. At George Washington University, after piloting its 24/7 chatbot service MARTHA, 89 percent of users advocated the tool be a permanent tool. "We've created a service broker that can handle decisions on where to go to look for information," Jonathan Fozard, assistant vice president for the CIO's office at George Washington University told EdTech. "As we educated it and users tested it, the Watson component was learning alongside of us. If someone types in a question about 3D printing, we know that's most likely a student who has access to 3D printers in the engineering classroom or a medical enterprise."

Our Random Forest model predicts a 73% probability of the Oakland Raiders keeping it within the 4.5 points being offered at Westgate.

For a factory where robots toil around the clock to build a rocket with almost no human labor, the sound of grunts echoing across the parking lot make for a jarring contrast. "That's Keanu Reeves' stunt gym," says Tim Ellis, the chief executive and cofounder of Relativity Space, a startup that wants to combine 3D printing and artificial intelligence to do for the rocket what Henry Ford did for the automobile. As we walk among the robots occupying Relativity's factory, he points out the just-completed upper stage of the company's rocket, which will soon be shipped to Mississippi for its first tests. Across the way, he says, gesturing to the outside world, is a recording studio run by Snoop Dogg. Neither of those A-listers have paid a visit to Relativity's rocket factory, but the presence of these unlikely neighbors seems to underscore the company's main talking point: It can make rockets anywhere. In an ideal cosmos, though, its neighbors will be even more alien than Snoop Dogg.

Farming and advanced AI may seem antithetical, but they're not. The venerable farm equipment company has not only long embraced advanced technologies, the company for years has evangelized adoption of high performance clusters and simulation software for product design. And Deere freely states it's an extremely complex undertaking. In a recent article in IEEE, Deere's Julian Sanchez, who heads the Moline, IL, company's intelligent vehicles strategy, said that while the company is working on autonomous driving, "it's not just about driving tractors around." The more difficult problem, he said, is crop classification.