Anthropic’s new techniques come at a time of increasing concern about AI transparency and safety. Understanding the model’s internal mechanisms becomes increasingly important.
Category: robotics/AI – Page 38
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.
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 soft robot (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.”
Dirac’s Plate Trick, the Hairy Ball Theorem and more: Research probes physics of irregular objects on inclined planes
Posted in biological, nanotechnology, physics, robotics/AI, transportation | Leave a Comment on Dirac’s Plate Trick, the Hairy Ball Theorem and more: Research probes physics of irregular objects on inclined planes
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.
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 photonic circuits, where losses increase as the number of modes grows.
APT36 spoofed India Post using malware-laced PDFs and Android apps to harvest sensitive data.
Assistive artificial intelligence technologies hold significant promise for transforming health care by aiding physicians in diagnosing, managing, and treating patients. However, the current trend of assistive AI implementation could actually worsen challenges related to error prevention and physician burnout, according to a new brief published in JAMA Health Forum.
The brief, written by researchers from the Johns Hopkins Carey Business School, Johns Hopkins Medicine, and the University of Texas at Austin McCombs School of Business, explains that there is an increasing expectation of physicians to rely on AI to minimize medical errors. However, proper laws and regulations are not yet in place to support physicians as they make AI-guided decisions, despite the fierce adoption of these technologies among health care organizations.
The researchers predict that medical liability will depend on whom society considers at fault when the technology fails or makes a mistake, subjecting physicians to an unrealistic expectation of knowing when to override or trust AI. The authors warn that such an expectation could increase the risk of burnout and even errors among physicians.
AI models from OpenAI, Google, DeepSeek, and others scored pathetically low on the second edition of the ARC-AGI benchmark, suggesting models still have a long way to go before achieving AGI.
Tesla is preparing to launch its robo taxi in June, leveraging its unique autonomy and data advantages to navigate challenges such as new tariffs and production shifts, while positioning itself for significant growth amid declining competitor viability ## Questions to inspire discussion ## Tesla’s Robo Taxi Service.
🚕 Q: When and where is Tesla launching its robo taxi service? A: Tesla’s robo taxi service is set to launch in Austin, Texas in June 2025, with plans for a nationwide rollout in the US later that year.
🏎️ Q: What vehicles will be eligible for Tesla’s robo taxi service? A: The service will be available on all vehicles equipped with Full Self-Driving (FSD) capability, including existing Model 3 and Model Y, not just the upcoming Cybertruck.
💰 Q: How will Tesla’s robo taxi network economics work? A: The economics will be based on cost per mile, factoring in low capital costs of Tesla EVs and low power consumption of their onboard autonomy systems.
📊 Q: What competitive advantage does Tesla have in the robo taxi market? A: Tesla’s existing fleet of billions of miles of deployed vehicles and hundreds of thousands of users provide a massive data advantage for improving and assessing the service. ## Tariffs and Supply Chain.
🏭 Q: What is Tesla’s supply chain strategy? A: Tesla aims to build cars where sold for environmental reasons, which is considered best practice in network design but extremely difficult to implement.
The company has been negotiating with both the Austin city authorities and the city’s autonomous vehicle working group since May 2024 regarding the introduction of the Robotaxi service safely. Set for release in June 2025, this fully self-driving fleet is a backup plan to the journey that Tesla is eager to accomplish of manufacturing electric and self-driving vehicles that can revolutionize city transportation.
During the Q4 2024 earnings conference call on January 29, Elon Musk announced the plan for the Robotaxi rollout in Austin. At the end of the interview, Musk further said, “We feel confident in being able to do an initial launch of unsupervised, no one in the car, full self-driving in Austin in June.” He noted that the process would be progressive to avoid risks that are associated with accidents and legal issues.
The future is autonomous & it starts in Austin, this June.