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Category: robotics/AI – Page 4

When it comes to creating images of the earth from above, satellites, drones, planes and spacecraft are what tend to come to mind. But a startup called Near Space Labs is taking a very different approach to taking high-resolution photos from up high.

Near Space Labs is building aircraft that are raised by helium balloons and then rely on air currents to stay up, move around to take pictures from the stratosphere, and eventually glide back down to earth. On the back of significant traction with customers using its images, the startup has now raised $20 million to expand its business.

Bold Capital Partners (a VC firm founded by Peter Diamandis of XPRIZE and Singularity University fame), is leading the Series B round. Strategic backer USAA (the U.S. Automobile Association) is also investing alongside Climate Capital, Gaingels, River Park Ventures, and previous backers Crosslink Capital, Third Sphere, Draper Associates, and others that are not being named. Near Space Labs has now raised over $40 million, including a $13 million Series A in 2021.

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Increasingly stricter regulations on emissions from lean-burn engines, such as the Euro 7 standard, are approaching. This requires the development of catalytic materials that can reduce the toxic nitrogen oxides efficiently at low temperatures. Researchers at the Department of Physics at Chalmers University of Technology, together with industrial partner Umicore, now present a study showing how machine learning could help engines run cleaner.

Catalytic converters reduce the amount of toxic pollutants emitted into the air from a vehicle’s exhaust system. Stricter regulations on emissions standards within the coming years, such as the European Union’s proposed Euro 7, aim at further reducing air pollution from vehicles. Therefore, improved catalysts are needed to limit the emissions of harmful pollutants.

The main technology of selective catalytic reduction of uses ammonia as a reducing agent. Thus, the catalytic material should promote the formation of a nitrogen–nitrogen bond between nitrogen oxides and ammonia in an oxygen-rich environment and prevent unwanted reactions, which include the oxidation of ammonia to even more nitrogen oxides or nitrous oxide.

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Scientists are racing against time to try and create revolutionary, sustainable energy sources (such as solid-state batteries) to combat climate change. However, this race is more like a marathon, as conventional approaches are trial-and-error in nature, typically focusing on testing individual materials and set pathways one by one.

To get us to the finish line faster, researchers at Tohoku University developed a data-driven AI framework that points out potential solid-state electrolyte (SSE) candidates that could be “the one” to create the ideal sustainable energy solution.

This model does not only select optimal candidates, but can also predict how the reaction will occur and why this candidate is a good choice—providing interesting insights into potential mechanisms and giving researchers a huge head start without even stepping foot into the lab.

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Researchers at Korea’s Daegu Gyeongbuk Institute of Science and Technology (DGIST) have developed a porous laser-induced graphene (LIG) sensor array that functions as a “next-generation AI electronic nose” capable of distinguishing scents like the human olfactory system does and analyzing them using artificial intelligence.

This technology converts scent molecules into electrical signals and trains AI models on their unique patterns. It holds great promise for applications in personalized health care, the cosmetics industry, and environmental monitoring.

While conventional electronic noses (e-noses) have already been developed and used in areas such as food safety and gas detection in industrial settings, they struggle to distinguish subtle differences between similar smells or analyze complex scent compositions. For instance, distinguishing among floral perfumes with similar notes or detecting the faint odor of fruit approaching spoilage remains challenging for current systems. This gap has driven demand for next-generation e-nose technologies with greater precision, sensitivity, and adaptability.

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Four children have gained life-changing improvements in sight following treatment with a pioneering new genetic medicine through Moorfields Eye Hospital and UCL Institute of Ophthalmology.

The work was funded by the NIHR Research Professorship, Meira GTx and Moorfields Eye Charity.

The 4 children were born with a severe impairment to their sight due to a rare genetic deficiency that affects the ‘AIPL1’ gene. The defect causes the retinal cells to malfunction and die. Children affected are only able to distinguish between light and darkness. They are legally certified as blind from birth.

The new treatment is designed to enable the retinal cells to work better and to survive longer. The procedure, developed by UCL scientists, consists of injecting healthy copies of the gene into the retina through keyhole surgery. These copies are contained inside a harmless virus, so they can penetrate the retinal cells and replace the defective gene.

The condition is very rare, and the first children identified were from overseas. To mitigate any potential safety issues, the first 4 children received this novel therapy in only one eye.

The eye gene therapy was delivered via keyhole surgery at Great Ormond Street Hospital. The children were assessed in the NIHR Moorfields Clinical Research Facility, and the NIHR Moorfields Biomedical Research Centre provided infrastructure support for the research.

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A machine-learning algorithm rapidly generates designs that can be simpler than those developed by humans.

Researchers in optics and photonics rely on devices that interact with light in order to transport it, amplify it, or change its frequency, and designing these devices can be painstaking work requiring human ingenuity. Now a research team has demonstrated that the discovery of the core design concepts can be automated using machine learning, which can rapidly provide efficient designs for a wide range of uses [1]. The team hopes the approach will streamline research and development for scientists and engineers who work with optical, mechanical, or electrical waves, or with combinations of these wave types.

When a researcher needs a transducer, an amplifier, or a similar element in their experimental setup, they draw on design concepts tested and proven in earlier experiments. “There are literally hundreds of articles that describe ideas for the design of devices,” says Florian Marquardt of the University of Erlangen-Nuremberg in Germany. Researchers often adapt an existing design to their specific needs. But there is no standard procedure to find the best design, and researchers could miss out on simpler designs that would be easier to implement.

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Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel artificial intelligence (AI) model inspired by neural oscillations in the brain, with the goal of significantly advancing how machine learning algorithms handle long sequences of data.

AI often struggles with analyzing complex information that unfolds over long periods of time, such as climate trends, biological signals, or financial data. One new type of AI model called “state-space models” has been designed specifically to understand these sequential patterns more effectively. However, existing state-space models often face challenges—they can become unstable or require a significant amount of computational resources when processing long data sequences.

To address these issues, CSAIL researchers T. Konstantin Rusch and Daniela Rus have developed what they call “linear oscillatory state-space models” (LinOSS), which leverage principles of forced harmonic oscillators—a concept deeply rooted in physics and observed in .

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