OpenAI may get most of the attention, but these two-dozen companies finding their way in AI by making vibe-coding software, building robots, and developing drones.
Category: robotics/AI – Page 93
Deep-learning algorithms enhance mutation detection in cancer and RNA sequencing
Researchers from the Faculty of Engineering at The University of Hong Kong (HKU) have developed two innovative deep-learning algorithms, ClairS-TO and Clair3-RNA, that significantly advance genetic mutation detection in cancer diagnostics and RNA-based genomic studies.
The pioneering research team, led by Professor Ruibang Luo from the School of Computing and Data Science, Faculty of Engineering, has unveiled two groundbreaking deep-learning algorithms—ClairS-TO and Clair3-RNA—set to revolutionize genetic analysis in both clinical and research settings.
Leveraging long-read sequencing technologies, these tools significantly improve the accuracy of detecting genetic mutations in complex samples, opening new horizons for precision medicine and genomic discovery. Both research articles have been published in Nature Communications.
Radiowaves enable energy-efficient AI on edge devices without heavy hardware
As drones survey forests, robots navigate warehouses and sensors monitor city streets, more of the world’s decision-making is occurring autonomously on the edge—on the small devices that gather information at the ends of much larger networks.
But making that shift to edge computing is harder than it seems. Although artificial intelligence (AI) models continue to grow larger and smarter, the hardware inside these devices remains tiny.
Engineers typically have two options, neither are ideal. Storing an entire AI model on the device requires significant memory, data movement and computing power that drains batteries. Offloading the model to the cloud avoids those hardware constraints, but the back-and-forth introduces lag, burns energy and presents security risks.
Are Space Elevators Still a Thing for the Future?
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Written, researched and presented by Paul Shillito.
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Powering AI from space, at scale, with a passive tether design
Penn Engineers have developed a novel design for solar-powered data centers that will orbit Earth and could realistically scale to meet the growing demand for AI computing while reducing the environmental impact of data centers.
Reminiscent of a leafy plant, with multiple, hardware-containing stems connected to branching, leaf-like solar panels, the design leverages decades of research on “tethers,” rope-like cables that naturally orient themselves under the competing forces of gravity and centrifugal motion. This architecture could scale to the thousands of computing nodes needed to replicate the power of terrestrial data centers, at least for AI inference, the process of querying tools like ChatGPT after their training concludes.
Unlike prior designs, which typically require constant adjustments to keep solar panels pointed toward the sun, the new system is largely passive, its orientation maintained by natural forces acting on objects in orbit. By relying on these stabilizing effects, the design reduces weight, power consumption, and overall complexity, making large-scale deployment more feasible.
Foundation AI models trained on physics, not words, are driving scientific discovery
While popular AI models such as ChatGPT are trained on language or photographs, new models created by researchers from the Polymathic AI collaboration are trained using real scientific datasets. The models are already using knowledge from one field to address seemingly completely different problems in another.
While most AI models—including ChatGPT—are trained on text and images, a multidisciplinary team, including researchers from the University of Cambridge, has something different in mind: AI trained on physics.
Synthetic ‘muscle’ with microfluidic blood vessels shows promise for soft robotics
Researchers are continuing to make progress on developing a new synthetic material that behaves like biological muscle, an advancement that could provide a path to soft robotics, prosthetic devices and advanced human-machine interfaces. Their research, recently published in Advanced Functional Materials, demonstrates a hydrogel-based actuator system that combines movement, control and fuel delivery in a single integrated platform.
Biological muscle is one of nature’s marvels, said Stephen Morin, associate professor of chemistry at the University of Nebraska–Lincoln. It can generate impressive force, move quickly and adapt to many different tasks. It is also remarkable in its flexibility in terms of energy use and can draw on sugars, fats and other chemical stores, converting them into usable energy exactly when and where they are needed to make muscles move.
A synthetic version of muscle is one of the Holy Grails of material science.