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.

Distribution and democratization represent two complementary paradigms that are gaining increasing attention in manufacturing. Distributed manufacturing (DM) allows for geographically dispersed production, often at small scales and near the end user. Democratization enables large populations to engage in manufacturing. Massively distributed manufacturing (MDM), which combines these paradigms, is performed on demand by a large network of people located anywhere. Rather than rely on mass production in centralized factories, MDM promises to improve the responsiveness and resilience of manufacturing to urgent production demands (such as emergencies like pandemics); promote mass customization and cost-effective, low-volume production; gainfully employ many informally trained citizens in manufacturing (such as through the gig economy); and reduce the environmental footprint of manufacturing by producing items near their points of use. The Fourth Industrial Revolution will play an important role in enabling MDM by way of cyber-physical operating systems (CPOSs). From the First Industrial Revolution in the 18th century onward, manufacturing has been carried out predominantly through mass production in centralized factories, often far from the end user. Mass production enables large quantities of products to be produced with standardized quality, high productivity, and low cost. However, in the face of urgent demands or disruptions, it lacks flexibility, agility, and resilience and cannot readily provide consumers with personalized products in small quantities (mass customization) ([ 1 ][1]). Moreover, its environmental footprint is large, mainly because it often requires raw materials and finished goods to be transported over long distances. During the past decade, there has been growing interest and activity in distributed and democratized manufacturing as alternative or complementary paradigms to mass production. DM has been emphasized by the United Nations International Development Organization ([ 2 ][2]), the World Economic Forum ([ 3 ][3]), and other major agencies ([ 4 ][4], [ 5 ][5]) as critical to the future of manufacturing. Several companies engaged in DM, such as 3D Hubs, 3Diligent, Fast Radius, and Xometry, have sprouted. Xometry, for example, enables its customers to access the manufacturing capacity of a network of >5000 carefully curated partners—typically small- and medium-sized enterprises—distributed across the world. In terms of democratization ([ 6 ][6]), perhaps the most compelling example is the proliferation of desktop three-dimensional (3D) printers, which currently retail on average for ∼$1000 ([ 7 ][7]), which is within the purchasing power of large portions of the population. In 2019, >700,000 desktop 3D printers were sold globally ([ 7 ][7]). These printers can now be found in homes, offices, schools, maker spaces, public libraries, and other facilities, and people can use them for prototyping and small-scale or micromanufacturing without extensive technical training. ![Figure][8] Networked systems linking producers to consumers Massively distributed manufacturing uses a cyber-physical operating system and artificial intelligence tools to connect and coordinate consumers with producers. Producers in micromanufacturing units can use three-dimensional (3D) printing to fabricate customized products. Smart logistics such as drones and rideshare services enable the physical product delivery. GRAPHIC: C. BICKEL/ SCIENCE However, distributed and democratized manufacturing are still far from the goal of MDM ([ 8 ][9]–[ 10 ][10]), in which products are manufactured by a large, diverse, and geographically dispersed but coordinated network of individuals and organizations with agility and flexibility, but with near–mass-production quality, productivity, and cost effectiveness. For example, a company like Xometry would need to engage millions of users in micromanufacturing across the globe, similar to what companies like Uber and Lyft have achieved with transportation. The latent potential of MDM was evident during the early days of the COVID-19 pandemic, when personal protective equipment (PPE) were in short supply. Mass production was too slow to react to the sudden demands for PPE, including demands for simple but vital plastic products like face shields. Worldwide, thousands, if not millions, of people, many of whom did not have experience in making these products, organized themselves into small networks to produce millions of face shields and other PPE using desktop 3D printers and other small-scale manufacturing equipment ([ 11 ][11]). This effort exposed key challenges of MDM in terms of standardizing production requirements, guaranteeing quality and reliability, and attaining high production efficiencies that can rival those of mass production. This example illustrates the important role of technology in enabling MDM. The First, Second, and Third Industrial Revolutions, driven by mechanization, electrification plus assembly lines, and digital computing, respectively, paved the way for the Fourth Industrial Revolution (or Industry 4.0), undergirded by networked cyber-physical systems and artificial intelligence. For example, Xometry leverages cloud computing and machine learning to power its instant quoting engine that enables customers to receive pricing, expected lead times, and manufacturability feedback within seconds. Similarly, 3Diligent uses cloud computing to enable manufacturers in its network to route jobs across their shop floors and track quality. With advances in Industry 4.0, manufacturing machines (including low-cost 3D printers) are increasingly equipped with sensors and cloud connectivity ([ 12 ][12]). The large amounts of data generated by these sensors are being used in machine-learning algorithms to provide predictive and corrective actions ([ 13 ][13]). Advanced cloud-based controllers are being developed to improve the quality and productivity of the machines ([ 14 ][14]). These advances in technology and automation can converge into a cloud-based CPOS for MDM. An inspiration for CPOSs is the central coordinator used in distributed computing to automate the allocation and execution of large-scale computing tasks on distributed networks of computers. The central coordinator has enabled Folding at Home ([ 15 ][15]), a distributed computing cluster that leverages the idle capacity of >100,000 personal computers to run simulations that help scientists to understand how proteins fold. Similarly, a CPOS will intelligently, efficiently, and securely coordinate large networks of cloud-connected, autonomous, and geographically-dispersed manufacturing resources. It will optimally allocate manufacturing jobs to the resources connected to it and leverage distributed and democratized delivery systems, such as shared vehicles and drones, for logistics (see the figure). It will apply machine learning to the data gathered from sensors to help assure and improve quality and to optimize operations. Furthermore, CPOSs will leverage the ingenuity of humans through the crowdsourcing of ideas to improve manufacturing operations across networks of manufacturers as well as cybersecurity measures to protect intellectual property and the privacy of participants. CPOSs will thus allow the collaboration of large, autonomous, heterogeneous, and geographically dispersed networks of manufacturers to rapidly respond to production demands and disruptions with agility and flexibility, while ensuring the high quality, productivity, and cost effectiveness of MDM. 1. [↵][16]1. B. J. Pine II , Mass Customization: The New Frontier in Business Competition (Harvard Business School Press, 1993). 2. [↵][17]1. C. López-Gómez, 2. E. O'Sullivan, 3. M. Gregory, 4. A. C. C. Fleury, 5. L. Gomes , Emerging Trends in Global Manufacturing Industries (United Nations Industrial Development Organization, 2013). 3. [↵][18]1. B. Meyerson , Top 10 Emerging Technologies of 2015 (World Economic Forum, 2015). 4. [↵][19]Foresight, The Future of Manufacturing: A New Era of Opportunity and Challenge for the UK Summary Report (The Government Office for Science, London, UK, 2013). 5. [↵][20]European Factories of the Future Research Association, Factories of the Future: Multi-annual Roadmap for the Contractual PPP under Horizon 2020 (European Commission, 2013). 6. [↵][21]MForesight, Democratizing Manufacturing: How to Realize the Promise of the Maker Movement (2017). 7. [↵][22]Wohlers Associates, Wohlers Report 2020: 3D Printing and Additive Manufacturing: Global State of the Industry (Wohlers Associates, 2020); . 8. [↵][23]1. J. S. Srai et al ., Int. J. Prod. Res. 54, 6917 (2016). [OpenUrl][24] 9. 1. H. Stewart, 2. J. Tooze , Making Futures 4, 1 (2015). [OpenUrl][25] 10. [↵][26]1. P. Jiang, 2. J. Leng, 3. K. Ding, 4. P. Gu, 5. Y. Koren , Proc. Inst. Mech. Eng. B 230, 1961 (2016). [OpenUrl][27] 11. [↵][28]1. J. M. Pearce , J. Manuf. Mater. Process. 4, 49 (2020). [OpenUrl][29] 12. [↵][30]1. C. E. Okwudire, 2. S. Huggi, 3. S. Supe, 4. C. Huang, 5. B. Zeng , Inventions 3, 56 (2018). [OpenUrl][31] 13. [↵][32]1. T. Wuest, 2. D. Weimer, 3. C. Irgens, 4. K. D. Thoben , Prod. Manuf. Res. 4, 23 (2016). [OpenUrl][33] 14. [↵][34]1. C. E. Okwudire, 2. X. Lu, 3. G. Kumaravelu, 4. H. Madhyastha , Robot. Comput.-Integr. Manuf. 62, 101880 (2020). [OpenUrl][35] 15. [↵][36]Folding at Home, . Acknowledgments: C.E.O. is a founder of Ulendo, which has licensed research in advanced cloud-based 3D printer control algorithms. 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In the paper, we show how scalable, low-cost trunk-like robotic arms can be constructed using only basic 3D-printing equipment and simple electronics. The design is based on uniform, stackable joint modules with three degrees of freedom each. Moreover, we present an approach for controlling these robots with recurrent spiking neural networks. At first, a spiking forward model learns motor-pose correlations from movement observations. After training, intentions can be projected back through unrolled spike trains of the forward model essentially routing the intention-driven motor gradients towards the respective joints, which unfolds goal-direction navigation. We demonstrate that spiking neural networks can thus effectively control trunk-like robotic arms with up to 75 articulated degrees of freedom with near millimeter accuracy.

Agtech capabilities are bringing traditional farming into the 21st century. These solutions range from sprawling LED-equipped indoor farming facilities to robotically plucking ripe produce off the vine using computer vision and artificial intelligence (AI). On Thursday, John Deere and Intel announced a pilot program that relies on AI and computer vision to detect defects in manufacturing related to the welding process. "Welding is a complicated process. This AI solution has the potential to help us produce our high-quality machines more efficiently than before," said Andy Benko, quality director at John Deere Construction and Forestry Division.

When it comes to embracing new technology and digitising entire sectors of business, look no further than the Industry 4.0 revolution. All over the world, companies from the likes of manufacturing, warehousing and logistics are embracing Industry 4.0's key technologies to open up new values and benefits. However, Industry 4.0 trends shift and evolve as time goes on. As such, we want to take a look at Industry 4.0 technologies and projects in more detail. We've previously discussed the topic of Industry 4.0 before, but here's a quick recap.