This led him to complete a master's degree in University College Dublin, followed by a PhD in machine learning in additive manufacturing (more …

Caterpillar Inc. (often shortened to CAT) is an American Fortune 100 corporation that designs, develops, engineers, manufactures, markets, and sells machinery, engines, financial products, and insurance to customers via a worldwide dealer network. It is the world's largest construction-equipment manufacturer. In 2018, Caterpillar was ranked number 65 on the Fortune 500 list and number 238 on the Global Fortune 500 list. Caterpillar stock is a component of the Dow Jones Industrial Average . CATERPILLAR INC.&tbm isch Caterpillar is the world's leading manufacturer of construction and mining equipment, diesel and natural gas engines, industrial gas turbines and diesel-electric locomotives. We are a leader and proudly have the largest global presence in the industries we serve.

In design, fabrication, and control problems, we are often faced with the task of synthesis, in which we must generate an object or configuration that satisfies a set of constraints while maximizing one or more objective functions. The synthesis problem is typically characterized by a physical process in which many different realizations may achieve the goal. This many-to-one map presents challenges to the supervised learning of feed-forward synthesis, as the set of viable designs may have a complex structure. In addition, the non-differentiable nature of many physical simulations prevents direct optimization. We address both of these problems with a two-stage neural network architecture that we may consider to be an autoencoder. We first learn the decoder: a differentiable surrogate that approximates the many-to-one physical realization process. We then learn the encoder, which maps from goal to design, while using the fixed decoder to evaluate the quality of the realization. We evaluate the approach on two case studies: extruder path planning in additive manufacturing and constrained soft robot inverse kinematics. We compare our approach to direct optimization of design using the learned surrogate, and to supervised learning of the synthesis problem. We find that our approach produces higher quality solutions than supervised learning, while being competitive in quality with direct optimization, at a greatly reduced computational cost.

This week in a paper published in the journal Nature, researchers at Google detailed how they used AI to design the next generation of tensor processing units (TPU), the company's application-specific integrated circuits optimized for AI workloads. While the work wasn't novel -- Google's been refining the technique for the better part of years -- it gave the clearest illustration yet of AI's potential in hardware design. But the Nature paper suggests AI can at the very least augment human designers to accelerate the brainstorming process. Beyond chips, companies like U.S.- and Belgium-based Oqton are applying AI to design domains including additive manufacturing. Oqton's platform automates CNC, metal, and polymer 3D printing and hybrid additive and subtractive workflows, like creating castable jewelry wax.

And let's face it, we subtly see it in our everyday lives when a form gets filled out, or a choice of books and movies is set up for us, by learning our past preferences. We further see it with new applications like voice activation, preset GPS directions, and many other applications. Artificial intelligence has gained recognition as a valuable tool to turbocharge so many applications in business, industry, and other corners of commerce. Remarkable outcomes using artificial intelligence are instilling positive and monumental changes in engineering design, and improved living for many throughout the world. In a parallel rhythm, 3D printing has emerged and continues to advance.

Multi working-machines pathfinding solution enables more mobile machines simultaneously to work inside of a working site so that the productivity can be expected to increase evolutionary. To date, the potential cooperation conflicts among construction machinery limit the amount of construction machinery investment in a concrete working site. To solve the cooperation problem, civil engineers optimize the working site from a logistic perspective while computer scientists improve pathfinding algorithms' performance on the given benchmark maps. In the practical implementation of a construction site, it is sensible to solve the problem with a hybrid solution; therefore, in our study, we proposed an algorithm based on a cutting-edge multi-pathfinding algorithm to enable the massive number of machines cooperation and offer the advice to modify the unreasonable part of the working site in the meantime. Using the logistic information from BIM, such as unloading and loading point, we added a pathfinding solution for multi machines to improve the whole construction fleet's productivity. In the previous study, the experiments were limited to no more than ten participants, and the computational time to gather the solution was not given; thus, we publish our pseudo-code, our tested map, and benchmark our results. Our algorithm's most extensive feature is that it can quickly replan the path to overcome the emergency on a construction site.

A multivariate density forecast model based on deep learning is designed in this paper to forecast the joint cumulative distribution functions (JCDFs) of multiple security margins in power systems. Differing from existing multivariate density forecast models, the proposed method requires no a priori hypotheses on the distribution of forecasting targets. In addition, based on the universal approximation capability of neural networks, the value domain of the proposed approach has been proven to include all continuous JCDFs. The forecasted JCDF is further employed to calculate the deterministic security assessment index evaluating the security level of future power system operations. Numerical tests verify the superiority of the proposed method over current multivariate density forecast models. The deterministic security assessment index is demonstrated to be more informative for operators than security margins as well.

During lockdowns and social-distancing restrictions, the Charlotte, N.C.-based company said it saw an uptick in customers using its AI-based virtual assistant, Erica, online money-transfer service Zelle and mobile check deposit, among other digital services. The bank's technology and business executives spoke about the company's digital growth at a virtual event on Monday, and said they're exploring more ways to innovate and keep pace with the demand for its technology. The Morning Download delivers daily insights and news on business technology from the CIO Journal team. "Digital demand is here to stay. That's not going away…now the question is how can we serve (customers) in more ways," said Aditya Bhasin, chief information officer for consumer, small business and wealth management at the bank.

A Dutch couple have become Europe's first tenants of a fully 3D printed house in a development that its backers believe will open up a world of choice in the shape and style of the homes of the future. Elize Lutz, 70, and Harrie Dekkers, 67, retired shopkeepers from Amsterdam, received their digital key – an app allowing them to open the front door of their two-bedroom bungalow at the press of a button – on Thursday. "It is beautiful," said Lutz. "It has the feel of a bunker – it feels safe," added Dekkers. Inspired by the shape of a boulder, the dimensions of which would be difficult and expensive to construct using traditional methods, the property is the first of five homes planned by the construction firm Saint-Gobain Weber Beamix for a plot of land by the Beatrix canal in the Eindhoven suburb of Bosrijk. In the last two years properties partly constructed by 3D printing have been built in France and the US, and nascent projects are proliferating around the world.

Russia is claiming the first standard production artificial intelligence powered combine harvesters will come to market this month. Autonomous driving technology specialist, Cognitive Pilot, and Bryanskselmash, agricultural equipment manufacturer, have agreed fit automated drive technology to series produced harvesters rolling off the production line from the end of April 2021. The partners plan to expand joint marketing and other activities that will increase the attractiveness of the solution and expand its geographical reach. In another venture, Cognitive Pilot and Rosagroleasing, Russia's largest state-owned agricultural leasing company, have announced first contracts for AI-based agricultural equipment. This will make equipment available to domestic agricultural enterprises, seeking to improve efficiency, including both medium-size and small-size enterprises.