A savvy approach to artificial intelligence can radically enhance productivity and slash costs. You're not imagining it: the pace of technological change is indeed quickening and it's placing tremendous competitive pressures on every corner of the global economy Manufacturers are acutely feeling the squeeze. Eighty-five percent of industrial equipment execs surveyed by Accenture say they need to innovate ever faster just to keep up. That puts them in a perilous catch-22: it's prohibitively expensive to upgrade equipment to meet customer demands, yet they risk losing customers altogether if they don't. Enter artificial intelligence, the great equalizer for manufacturers.

Objects made with 3D printing can be lighter, stronger, and more complex than those produced through traditional manufacturing methods. But several technical challenges must be overcome before 3D printing transforms the production of most devices. Commercially available printers generally offer only high-speed, high-precision, or high-quality materials. Rarely do they offer all three, limiting their usefulness as a manufacturing tool. Today, 3D printing is used mainly for prototyping and low-volume production of specialized parts.

The company is accomplishing this by pairing its multimaterial inkjet 3D printer with machine-vision and machine–learning systems.

However, most commercial printers are only able to build objects from a single material at a time and inkjet printers that are capable of multimaterial printing are constrained by the physics of droplet formation. Extrusion-based 3D printing allows a broad palette of materials to be printed, but the process is extremely slow. For example, it would take roughly 10 days to build a 3D object roughly one liter in volume at the resolution of a human hair and print speed of 10 cm/s using a single-nozzle, single-material printhead. To build the same object in less than 1 day, one would need to implement a printhead with 16 nozzles printing simultaneously! Now, a new technique called multimaterial multinozzle 3D (MM3D) printing developed at Harvard's Wyss Institute for Biologically Inspired Engineering and John A. Paulson School of Engineering and Applied Sciences (SEAS) uses high-speed pressure valves to achieve rapid, continuous, and seamless switching between up to eight different printing materials, enabling the creation of complex shapes in a fraction of the time currently required using printheads that range from a single nozzle to large multinozzle arrays.

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.