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Category: robotics/AI – Page 32

The provincial government of Andhra Pradesh (AP) in India has entered into a Memorandum of Understanding (MoU) with the Gates Foundation to advance the use of technology in various sectors, including healthcare, agriculture, and education. The agreement was discussed in a meeting between AP Chief Minister N. Chandrababu Naidu and Bill Gates, the Foundation’s chair. Naidu reiterated his administration’s dedication to utilizing innovative technology to propel the state’s development.

The MoU focuses on applying technology in ways that will benefit the public, emphasizing affordable and scalable solutions across essential sectors such as healthcare, medical technology, education, and agriculture. According to Naidu, the collaboration will harness the power of artificial intelligence (AI) to enhance predictive health analytics and automate diagnostic processes. In the agricultural sector, AI-based platforms for expert guidance and satellite technology will be employed to optimize farming practices and resource management through precision agriculture techniques.

“This MoU formalises a strategic collaboration in which the Gates Foundation will provide support to implementation partners, co-identified with the AP government, for targeted interventions within state-driven programmes,” Naidu said.

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Researchers from the National University of Singapore (NUS) have demonstrated that a single, standard silicon transistor, the fundamental building block of microchips used in computers, smartphones and almost every electronic system, can function like a biological neuron and synapse when operated in a specific, unconventional way.

Led by Associate Professor Mario Lanza from the Department of Materials Science and Engineering at the College of Design and Engineering, NUS, the research team’s work presents a highly scalable and energy-efficient solution for hardware-based (ANNs).

This brings —where chips could process information more efficiently, much like the —closer to reality. Their study was published in the journal Nature.

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Anyone who develops an AI solution sometimes goes on a journey into the unknown. At least at the beginning, researchers and designers do not always know whether their algorithms and AI models will work as expected or whether the AI will ultimately make mistakes.

Sometimes, AI applications that work well in theory perform poorly under real-life conditions. In order to gain the trust of users, however, an AI should work reliably and correctly. This applies just as much to popular chatbots as it does to AI tools in research.

Any new AI tool has to be tested thoroughly before it is deployed in the real world. However, testing in the real world can be an expensive, or even risky endeavor. For this reason, researchers often test their algorithms in computer simulations of reality. However, since simulations are approximations of reality, testing AI solutions in this way can lead researchers to overestimate an AI’s performance.

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A research team has successfully developed a technology that utilizes Large Language Models (LLMs) to predict the synthesizability of novel materials and interpret the basis for such predictions. The team was led by Seoul National University’s Professor Yousung Jung and conducted in collaboration with Fordham University in the United States.

The findings of this research are expected to contribute to the novel material design process by filtering out material candidates with low synthesizability in advance or optimizing previously challenging-to-synthesize materials into more feasible forms.

The study, with Postdoctoral Researcher Seongmin Kim as the first author, was published in two chemistry journals: the Journal of the American Chemical Society on July 11, 2024, and Angewandte Chemie International Edition on February 13, 2025.

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Over the past decades, roboticists have introduced a wide range of systems that can move in various complex environments, including different terrains, on the ground, in the air, and even in water. To safely navigate real-world dynamic environments without colliding with humans or nearby objects, most robots rely on sensors and cameras.

Researchers at Tsinghua University have recently developed WHERE-Bot, a new wheel-less, everting (i.e., a flexible robot that moves by turning its body structure inside out) that safely moves in unstructured environments without using sensors to detect obstacles. This robot, introduced in a paper published on the arXiv preprint server and set to be presented at the 8th IEEE International Conference on Soft Robotics (RoboSoft) in April, leverages its unique helical ring-based structure to move in all directions.

“One day, while playing with a Slinky toy during a lab meeting,” Shuguang Li, senior author of the paper, told Tech Xplore. “Suddenly, a new idea struck us: what if we connected the head and tail of the spring toy? By joining its two ends, the spring could be endlessly turned inside-out—a motion we now call ‘everting’—presenting a fascinating color flow. This sparked our curiosity about how such a helical ring—perhaps with some structure modifications—would behave in various environments: on the ground, along a pipe, underwater, on sand, and even in the air.”

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How gravity causes a perfectly spherical ball to roll down an inclined plane is part of the elementary school physics canon. But the world is messier than a textbook.

Scientists in the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have sought to quantitatively describe the much more complex rolling physics of real-world objects. Led by L. Mahadevan, the Lola England de Valpine Professor of Applied Mathematics, Physics, and Organismic and Evolutionary Biology in SEAS and FAS, they combined theory, simulations, and experiments to understand what happens when an imperfect, spherical object is placed on an inclined plane.

Published in Proceedings of the National Academy of Sciences, the research, which was inspired by nothing more than curiosity about the everyday world, could provide fundamental insights into anything that involves irregular objects that roll, from nanoscale cellular transport to robotics.

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Photonic circuits, which manipulate light to perform various computational tasks, have become essential tools for a range of advanced technologies—from quantum simulations to artificial intelligence. These circuits offer a promising way to process information with minimal energy loss, especially in fields like quantum computing where complex systems are simulated to test theories of quantum mechanics.

However, the growth in circuit size and complexity has historically led to a rise in optical losses, making it challenging to scale these systems for large-scale applications, such as multiphoton quantum experiments or all-optical AI systems.

As reported in Advanced Photonics, researchers at the University of Naples Federico II have now developed a new approach to address this problem. Using a liquid-crystal (LC)-based platform, the team designed an optical processor capable of handling hundreds of optical modes in a compact, two-dimensional setup. This breakthrough offers a solution to a key limitation in traditional , where losses increase as the number of modes grows.

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