In just half a day, a new type of robot built an igloo-shaped building half the diameter of the U.S. Capitol dome--all by itself. In the future, such autonomous machines could assemble entire towns, create wacky Dr. Seuss–like structures, and even prepare the moon for its first human colony. "It's an impressive project," says Matthias Kohler, an architect who studies autonomous construction at ETH Zurich in Switzerland, but was not involved in the work. People have experimented with many approaches to autonomous construction, and the scientists--a team from the Massachusetts Institute of Technology's (MIT's) materials science and design focused Mediated Matter lab in Cambridge--weighed them all before designing their robot. Should their robot manufacture prefabricated parts in a distant factory?

How can we create robots that can carry out important tasks in dangerous environments? Machine learning is supporting advances in the field of robotics. To find out more, we talked to Dr Rustam Stolkin, Royal Society Industry Fellow for Nuclear Robotics, Professor of Robotics at the University of Birmingham, and Director at A.R.M Robotics Ltd, about his work combining machine learning and robotics to create practical solutions to nuclear problems. There are many definitions of engineering, but the one I like is "the creation of artefacts for the benefit of mankind". Engineering is a way of using science to be creative and to create novel technologies which can bring major societal and economic benefit.

Integrated Computational Materials Engineering (ICME) aims to accelerate optimal design of complex material systems by integrating material science and design automation. For tractable ICME, it is required that (1) a structural feature space be identified to allow reconstruction of new designs, and (2) the reconstruction process be property-preserving. The majority of existing structural presentation schemes rely on the designer's understanding of specific material systems to identify geometric and statistical features, which could be biased and insufficient for reconstructing physically meaningful microstructures of complex material systems. In this paper, we develop a feature learning mechanism based on convolutional deep belief network to automate a two-way conversion between microstructures and their lower-dimensional feature representations, and to achieves a 1000-fold dimension reduction from the microstructure space. The proposed model is applied to a wide spectrum of heterogeneous material systems with distinct microstructural features including Ti-6Al-4V alloy, Pb63-Sn37 alloy, Fontainebleau sandstone, and Spherical colloids, to produce material reconstructions that are close to the original samples with respect to 2-point correlation functions and mean critical fracture strength. This capability is not achieved by existing synthesis methods that rely on the Markovian assumption of material microstructures.

Almost a year ago QuantStart discussed deep learning and introduced the Theano library via a logistic regression example. Given the recent results of the QuantStart 2017 Content Survey it was decided that an up to date beginner-friendly article was needed to introduce deep learning from first principles. These days it is almost impossible to work in any technology-heavy field without hearing about the latest advances in the field of deep learning. Quantitative finance is no different. Many of the recent discussions in the latest quant finance conferences such as Quantopian's QuantCon and Newsweek's AI & Data Science - Capital Markets are largely focusing around the promise of deep learning as the next frontier in quantitative trading.

Construction seems like an industry that, were I still living in Silicon Valley, I would be tempted to call "ripe for disruption." Researchers at the MIT Media Lab agree, pointing out in a paper just published in Science Robotics that construction "relies on traditional fabrication technologies that are dangerous, slow, and energy-intensive." Hey, sounds like a job for some robots, right? The Media Lab's paper introduces the Digital Construction Platform (DCP), which is "an automated construction system capable of customized on-site fabrication of architectural-scale structures." In other words, it's a robot arm that uses additive construction techniques to build large structures safely, quickly, and even (in some cases) renewably.

ABB and IBM on Tuesday announced a strategic collaboration that brings together ABB's digital offering -- ABB Ability -- with IBM Watson Internet of Things cognitive capabilities to unlock new value for customers in utilities, industry, transport and infrastructure. Customers will benefit from ABB's deep domain knowledge and extensive portfolio of digital solutions combined with IBM's expertise in artificial intelligence and machine learning as well as different industry verticals. The first two joint industry solutions powered by ABB Ability and Watson will bring real-time cognitive insights to the factory floor and smart grids. "This powerful combination marks truly the next level of industrial technology, moving beyond current connected systems that simply gather data, to industrial operations and machines that use data to sense, analyze, optimize and take actions that drive greater uptime, speed and yield for industrial customers," said ABB CEO Ulrich Spiesshofer. "With an installed base of 70 million connected devices, 70,000 digital control systems and 6,000 enterprise software solutions, ABB is a trusted leader in the industrial space, and has a four decade long history of creating digital solutions for customers. IBM is a leader in artificial intelligence and cognitive computing. Together, IBM and ABB will create powerful solutions for customers to benefit from the Fourth Industrial Revolution."

GE for GE: Deploy the digital industrial blueprint to improve its own manufacturing operations through Brilliant Factories, internal and external supply chains with the asset management system called the Digital thread, and engineering design with machine-learning modeling software called the Digital twin. Examples include boosting the global productivity of oil company BP by up to 4% by connecting oil and gas wells to the cloud. GE for World: Enable industrial companies by connecting them to the Industrial Internet, which can save hundreds of millions of dollars in manufacturing downtime and outages or by optimizing entire supply chains. GE for GE: Deploy the digital industrial blueprint to improve its own manufacturing operations through Brilliant Factories, internal and external supply chains with the asset management system called the Digital thread, and engineering design with machine-learning modeling software called the Digital twin. Examples include boosting the global productivity of oil company BP by up to 4% by connecting oil and gas wells to the cloud.

James Slifierz is making the rounds of New York City investors as he prepares to move his startup -- Skywatch -- into the Communitech Data Hub in Waterloo. The facility at Erb and Albert streets, slated to open in mid-May, is Communitech's latest tech accelerator. It is for startups that are working with big data and its many applications in artificial intelligence, machine learning and the Internet of Things. "I think it is an exciting opportunity," Slifierz says. He believes the Data Hub will be ideal for Skywatch, which is building an automated program that provides easy access to the enormous amounts of data collected by satellites. The technology is in private testing right now.

Even a simple chemical reaction can be surprisingly complicated. That's especially true for reactions involving catalysts, which speed up the chemistry that makes fuel, fertilizer and other industrial goods. In theory, a catalytic reaction may follow thousands of possible paths, and it can take years to identify which one it actually takes so scientists can tweak it and make it more efficient. Now researchers at the Department of Energy's SLAC National Accelerator Laboratory and Stanford University have taken a big step toward cutting through this thicket of possibilities. They used machine learning – a form of artificial intelligence – to prune away the least likely reaction paths, so they can concentrate their analysis on the few that remain and save a lot of time and effort.

Hannover, Germany, - 25 Apr 2017: ABB and IBM (NYSE: IBM) today announced a strategic collaboration that brings together ABB's industry leading digital offering, ABB Ability TM, with IBM Watson Internet of Things cognitive capabilities to unlock new value for customers in utilities, industry, transport and infrastructure. At Hannover Messe, IBM and ABB announced a new partnership in industrial artificial intelligence that will combine the power of IBM Watson with ABB Ability, the comprehensive digital offering of ABB, to unlock new value for clients in utilities, industry, transport and infrastructure. Pictured, Harriet Green, General Manager Watson IoT, Customer Engagement and Education, IBM; and Guido Jouret, Chief Digital Officer, ABB, discuss the future of cognitive and industrial machines. "This powerful combination marks truly the next level of industrial technology, moving beyond current connected systems that simply gather data, to industrial operations and machines that use data to sense, analyze, optimize and take actions that drive greater uptime, speed and yield for industrial customers," said ABB CEO Ulrich Spiesshofer. "With an installed base of 70 million connected devices, 70,000 digital control systems and 6,000 enterprise software solutions, ABB is a trusted leader in the industrial space, and has a four decade long history of creating digital solutions for customers. IBM is a leader in artificial intelligence and cognitive computing. Together, IBM and ABB will create powerful solutions for customers to benefit from the Fourth Industrial Revolution."