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Category: robotics/AI – Page 6

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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Featuring the Electro-Mechanical Brake and by-wire technology on the rear brakes, the project will also include ZF’s Integrated Brake Control and traditional front calipers, creating a ‘hybrid’ braking system of by-wire and hydraulics that offers increased flexibility to the manufacturer. The agreement will also provide significant steering technology with ZF’s Electric Recirculating Ball Steering Gear. This cutting-edge braking technology combined with traditional braking systems and innovative steering tools further solidifies ZF’s position as the industry leader in providing complete chassis solutions to its customers while providing a major customer win.

“We are all proud to see ZF’s technology leadership in the Chassis segment providing tangible value for our customers. Our goal when combining our steering, braking, dampers and actuators as well as corresponding software businesses into a single division was to create the world’s most comprehensive Chassis Solutions product and system offering,” said Peter Holdmann, Board of Management member at ZF and head of Division Chassis Solutions. “This combined center of expertise allows us to offer comprehensive solutions that integrate advanced engineering, innovative design, and cutting-edge technology to deliver unparalleled performance and safety.”

The road to the software-defined vehicle With the Electro-Mechanical Brake (EMB) as a key component of the brake-by-wire technology, ZF lays the foundation for the software-defined vehicle that will lead to new functions and features, many that emphasize safety as much as driving comfort. One such feature being explored with by-wire technology is the ability for the vehicle to autonomously brake and steer in a crash situation.

ZF’s Electro-Mechanical Brake provides premium performance for automatic emergency braking, full energy recuperation and redundant fallback options up to full automated driving for passenger car and light truck segments.

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Recent advances in robotics and machine learning have enabled the automation of many real-world tasks, including various manufacturing and industrial processes. Among other applications, robotic and artificial intelligence (AI) systems have been successfully used to automate some steps in manufacturing clothes.

Researchers at Laurentian University in Canada recently set out to explore the possibility of fully automating the knitting of clothes. To do this, they developed a model to convert images into comprehensive instructions that knitting robots could read and follow. Their model, outlined in a paper published in Electronics, was found to successfully realize patterns for the creation of single-yarn and multi-yarn knitted items of clothing.

“Our paper addresses the challenge of automating knitting by converting fabric images into machine-readable instructions,” Xingyu Zheng and Mengcheng Lau, co-authors of the paper, told Tech Xplore.

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Delivery robots made by companies such as Starship Technologies and Kiwibot autonomously make their way along city streets and through neighborhoods.

Under the hood, these robots—like most in use today—use a variety of different sensors and software-based algorithms to navigate in these environments.

Lidar sensors—which send out pulses of light to help calculate the distances of objects—have become a mainstay, enabling these robots to conduct simultaneous localization and mapping, otherwise known as SLAM.

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