For many companies, digitization and automation are the keys to the further development of additive manufacturing. Thus, more and more manufacturers are relying on cloud-based solutions and integrating various algorithms into their 3D printing solutions in order to exploit the full potential of the technology. As a digital process itself, 3D printing is part of Industry 4.0 and thus an important component of an era in which artificial intelligence, such as machine learning, is increasingly being used to optimize the value chain. Artificial intelligence (AI) is able to process a large amount of complex data in a very short time, which is why it is becoming increasingly important as a decision maker. We explain what machine learning is and why this form of AI is helping to shape the future of additive manufacturing.

The rise of computation-based methods in thermal management has gained immense attention in recent years due to the ability of deep learning to solve complex 'physics' problems, which are otherwise difficult to be approached using conventional techniques. Thermal management is required in electronic systems to keep them from overheating and burning, enhancing their efficiency and lifespan. For a long time, numerical techniques have been employed to aid in the thermal management of electronics. However, they come with some limitations. To increase the effectiveness of traditional numerical approaches and address the drawbacks faced in conventional approaches, researchers have looked at using artificial intelligence at various stages of the thermal management process. The present study discusses in detail, the current uses of deep learning in the domain of 'electronic' thermal management.

My current blog is epistem.ink. This one is here just for archival purposes. It surprises me that when people think of "software that brings about the singularity" they think of text models, or of RL agents. To me, this seems counter-intuitive, and the fact that most people researching ML are interested in subjects like vision and language is flabbergasting. For one, because getting anywhere productive in these fields is really hard, for another, because their usefulness seems relatively minimal.

The ocean is the most defining physical feature of Earth, covering 71% of the surface of this planet. It is home to incredible biodiversity, ranging from microscopic bacteria and viruses to the largest animal on Earth, the blue whale. We still do not know how many species live in the ocean, but the disappearance of an increasing number of them suggests that extinction is taking place, perhaps at a faster rate than on land. The stakes for ocean biodiversity have never been higher than they are this decade, and now more than ever, we need results. A widely promoted target is to bring 30% of marine area under protection by 2030--a major step that will contribute a great deal to marine biodiversity.

The evolution of technology and the rise of the internet have changed everything. We now live differently, see more, eat better, and do things differently. But have you ever imagined how technology, especially AI has impacted the way furniture is designed, manufactured, etc? Today, a variety of technologies can be used to promote and advertise end products in a range of furniture segments. Digital catalogs incorporate evolving technologies like augmented reality, virtuality, 3D modeling and art, mobile technology. This type of manufacturing is called additive manufacturing.

Rapid developments in evolutionary computation, robotics, 3D-printing, and material science are enabling advanced systems of robots that can autonomously reproduce and evolve. The emerging technology of robot evolution challenges existing AI ethics because the inherent adaptivity, stochasticity, and complexity of evolutionary systems severely weaken human control and induce new types of hazards. In this paper we address the question how robot evolution can be responsibly controlled to avoid safety risks. We discuss risks related to robot multiplication, maladaptation, and domination and suggest solutions for meaningful human control. Such concerns may seem far-fetched now, however, we posit that awareness must be created before the technology becomes mature.

Artificial Intelligence (AI) techniques continue to broaden across governmental and public sectors, such as power and energy - which serve as critical infrastructures for most societal operations. However, due to the requirements of reliability, accountability, and explainability, it is risky to directly apply AI-based methods to power systems because society cannot afford cascading failures and large-scale blackouts, which easily cost billions of dollars. To meet society requirements, this paper proposes a methodology to develop, deploy, and evaluate AI systems in the energy sector by: (1) understanding the power system measurements with physics, (2) designing AI algorithms to forecast the need, (3) developing robust and accountable AI methods, and (4) creating reliable measures to evaluate the performance of the AI model. The goal is to provide a high level of confidence to energy utility users. For illustration purposes, the paper uses power system event forecasting (PEF) as an example, which carefully analyzes synchrophasor patterns measured by the Phasor Measurement Units (PMUs). Such a physical understanding leads to a data-driven framework that reduces the dimensionality with physics and forecasts the event with high credibility. Specifically, for dimensionality reduction, machine learning arranges physical information from different dimensions, resulting inefficient information extraction. For event forecasting, the supervised learning model fuses the results of different models to increase the confidence. Finally, comprehensive experiments demonstrate the high accuracy, efficiency, and reliability as compared to other state-of-the-art machine learning methods.

The Special Economic Zone (SEZ) at Duqm is being prepped for investments related to artificial intelligence (AI) as well as 3D-printing projects, a senior official announced on Saturday. Eng Saleh bin Rashid al Hashmi, Director General of Planning and Engineering Affairs and Head of the Modern Building and 3D Printing Techniques Team at SEZAD, said the zone can provide a suitable and attractive environment for experimenting such technologies offered by the corporates specialised in modern building techniques. Successful technologies can then be promoted within and outside the Sultanate, he noted. "The Zone welcomes the initiatives of local and international private sector companies and research institutions to expand their services and activities and develop their business in SEZAD. Accordingly, investors will be stimulated to use modern technologies that serve reducing the building cost and duration of projects as well as providing environment-friendly buildings", Al Hashmi commented.

Volvo is eager to bring self-driving technology to construction crews, but it's taking a decidedly unusual route to get there. The automaker has unveiled an autonomous wheel loader prototype, the LX03, that's based on a Lego model -- 42081 Lego Technic Concept Wheel Loader Zeux, if you're looking for it. The machine can haul 5 tons and can make its own decisions in a wide variety of situations, including team-ups with human workers. The LX03 is also uniquely modular. Volvo can make "just one or two changes" to produce a larger or smaller loader to meet a customer's demands.

The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.