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A new Terminator-like skin that self-heals could give rise to killer robots. Scientists at Stanford University have developed synthetic skin made of silicone and polypropylene glycol materials that stretch like human skin without tearing, while magnetic properties allow the skin to self-align. When warmed, both polymers soften and flow, solidifying as they cool. When heated to just 158 degrees Fahrenheit, the self-alignment and healing happen in about 24 hours. The team said the skin could lead to'reconfigurable soft robots that can change shape and sense their deformation on demand,' ultimately transforming warfare.
Copilot in Power Pages: This continues Microsoft's Copilot strategy, embedding a generative AI system to rapidly generate text, design forms, design page layouts, and more when creating pages. Copilot in BI: BI is Microsoft's business insights suite, and the Copilot addition can generate slides, graphics, and data summaries from text-based prompts. Power Virtual Agents and generative AI: It seems the marriage of customer service chatbots and generative AI was inevitable. Microsoft has added generative AI capabilities to its Power Virtual Agent chatbot and extended it with the ability to do database searches and AI Integration. Power Automate gets a Copilot and SDK: Power Automate is a workflow scripting environment (think IFTTT or iOS Shortcuts, but on a network and enterprise level). Microsoft has announced a new Actions SDK that extend out-of-the-box actions. Power Automate also gets a Copilot, which will allow users to create workflows (or pieces of a workflow) using natural language.
Disposable plastics are everywhere: Food containers, coffee cups, plastic bags. Some of these plastics, called compostable plastics, can be engineered to biodegrade under controlled conditions. However, they often look identical to conventional plastics, get recycled incorrectly and, as a result, contaminate plastic waste streams and reduce recycling efficiency. Similarly, recyclable plastics are often mistaken for compostable ones, resulting in polluted compost. Researchers at University College London (UCL) have published a paper in Frontiers in Sustainability in which they used machine learning to automatically sort different types of compostable and biodegradable plastics and differentiate them from conventional plastics.
To magneticians, folks who study the uncanny forces some materials exert thanks to the movements of electrons and sometimes use cryptic hand gestures, the identity of Rare Earth 1 was obvious: neodymium. When added to more familiar elements, like iron and boron, the metal can help create a powerful, always-on magnetic field. But few materials have this quality. And even fewer generate a field that is strong enough to move a 4,500-pound Tesla--and lots of other things, from industrial robots to fighter jets. If Tesla planned to eliminate neodymium and other rare earths from its motors, what sort of magnets would it use instead?
Alibaba Cloud booth is seen during the Apsara Conference 2022 on November 3, 2022 in Hangzhou, Zhejiang Province of China. Alibaba Cloud wants partners to help build generative artificial intelligence (AI) models that are customized for companies across various verticals, including finance and petrochemicals. The Chinese cloud vendor has introduced a partnership program that it hopes will accelerate the development of such applications, powered by its large language model, Tongyi Qianwen. Launched earlier this month, the generative AI model is expected to be integrated with all of Alibaba's own business applications, including e-commerce, search, navigation, entertainment, enterprise communication, and intelligence voice assistance. Also: What is generative AI and why is it so popular?
For the past three years, Terry Aberhart has watched the spindly, fixed-wing drones zip across the big skies above his farm in Canada's Saskatchewan province, testing a technology that could be the future of weeding. Fitted with an artificial intelligence system, the drones are designed by local startup Precision AI to spot, identify and kill the weeds without drenching the entire crop in chemicals. "I'm on the list for one of the first machines when they become available," says Aberhart, a sustainable farming enthusiast. "The current technology is designed for maximum coverage and to hit everything in the field." This could be due to a conflict with your ad-blocking or security software.
Catalyst layers in proton exchange membrane fuel cells consist of platinum-group-metal nanocatalysts supported on carbon aggregates, forming a porous structure through which an ionomer network percolates. The local structural character of these heterogeneous assemblies is directly linked to the mass-transport resistances and subsequent cell performance losses; its three-dimensional visualization is therefore of interest. Herein we implement deep-learning-aided cryogenic transmission electron tomography for image restoration, and we quantitatively investigate the full morphology of various catalyst layers at the local-reaction-site scale. The analysis enables computation of metrics such as the ionomer morphology, coverage and homogeneity, location of platinum on the carbon supports, and platinum accessibility to the ionomer network, with the results directly compared and validated with experimental measurements. We expect that our findings and methodology for evaluating catalyst layer architectures will contribute towards linking the morphology to transport properties and overall fuel cell performance. The catalyst layer in proton-exchange membrane fuel cells involves the complex and crucial interplay between an ionomer network and metallic nanoparticles supported on carbons, but current methods are unable to describe it with high resolution. Now electron tomography at cryogenic temperatures and deep learning algorithms are used to provide quantitative three-dimensional imaging at nanometre resolution of a fuel cell catalyst layer structure.
We introduce a generic scheme for accelerating first-order optimization methods in the sense of Nesterov, which builds upon a new analysis of the accelerated proximal point algorithm. Our approach consists of minimizing a convex objective by approximately solving a sequence of well-chosen auxiliary problems, leading to faster convergence. This strategy applies to a large class of algorithms, including gradient descent, block coordinate descent, SAG, SAGA, SDCA, SVRG, Finito/MISO, and their proximal variants. For all of these methods, we provide acceleration and explicit support for non-strongly convex objectives. In addition to theoretical speed-up, we also show that acceleration is useful in practice, especially for ill-conditioned problems where we measure significant improvements.
The prediction of organic reaction outcomes is a fundamental problem in computational chemistry. Since a reaction may involve hundreds of atoms, fully exploring the space of possible transformations is intractable. The current solution utilizes reaction templates to limit the space, but it suffers from coverage and efficiency issues. In this paper, we propose a template-free approach to efficiently explore the space of product molecules by first pinpointing the reaction center - the set of nodes and edges where graph edits occur. Since only a small number of atoms contribute to reaction center, we can directly enumerate candidate products. The generated candidates are scored by a Weisfeiler-Lehman Difference Network that models high-order interactions between changes occurring at nodes across the molecule. Our framework outperforms the top-performing template-based approach with a 10% margin, while running orders of magnitude faster. Finally, we demonstrate that the model accuracy rivals the performance of domain experts.