Microscopic diffractive robotics merge optics and mobility, revolutionizing imaging, medicine, and materials science.
Called the ship’s nosecone, footage from local media spotted this piece being welded by robots at SpaceX’s facilities in Boca Chica, Teas. These facilities are part of a sprawling complex called Starbase, and they include manufacturing, assembly and testing facilities for the world’s largest rockets.
SpaceX has already started operations at its massive Starfactory. Some operations at the plant include inspecting the thousands of heatshield tiles on the nosecone after they are installed. For Starship Flight 7 and beyond, SpaceX will use upgraded heatshield tiles and a new design for the upper stage to improve its reliability during reentry.
Footage from local media in Texas shows workers and robots working on the Starship nosecone for what is presumably a component for a rocket destined for a future flight. SpaceX’s welding robot is clearly visible as it makes small changes to the nosecone, leading to barely visible sparks. Technicians, on the other hand, work on the nosecone with heatshield tiles installed.
In recent years, engineers have been trying to create hardware systems that better support the high computational demands of machine learning algorithms. These include systems that can perform multiple functions, acting as sensors, memories and computer processors all at once.
Researchers at Peking University recently developed a new reconfigurable neuromorphic computing platform that integrates sensing and computing functions in a single device. This system, outlined in a paper published in Nature Electronics, is comprised of an array of multiple phototransistors with one memristor (MP1R).
“The inspiration for this research stemmed from the limitations of traditional vision computing systems based on the CMOS von Neumann architecture,” Yuchao Yang, senior author of the paper, told Tech Xplore.
Generative artificial intelligence probably won’t change your life in 2025 — at least, not more than it already has, according to Google CEO Sundar Pichai.
When OpenAI launched ChatGPT two years ago, generative AI quickly captured the imagination of users around the world. Now, with the industry’s competitive landscape somewhat established — multiple big tech companies, including Google, have competing models — it’ll take time for another technological breakthrough to shock the AI industry into hyper-speed development again, Pichai said at the New York Times’ DealBook Summit last week.
Artificial intelligence is quickly becoming an integral tool in health care. In our new collection, the editors of NEJM AI provide insight into how the use of AI in clinical practice can improve patient care and outcomes.
Featured in this collection:
GPT versus Resident Physicians — A Benchmark Based on Official Board Scores Artificial Intelligence–Powered Rapid Identification of ST-Elevation Myocardial Infarction via Electrocardiogram (ARISE) — A Pragmatic Randomized Controlled Trial Use of GPT-4 to Diagnose Complex Clinical Cases.
Boom Supersonic has revealed that the XB-1, the supersonic demonstrator for the Overture program, took off on a flight equipped with a shark skin-like underbelly, which reduces drag, fuel consumption, and emissions.
Is it ethical to shut down an AI that doesn’t want to be shit down?
It sounds like OpenAI’s latest AI is showing signs of a drive for self-preservation. It pursued survival at all costs.
Content warning: this story includes graphic descriptions of dangerous self-harm behaviors.
The Google-funded AI company Character. AI is hosting chatbots designed to engage the site’s largely underage user base in roleplay about self-harm, depicting graphic scenarios and sharing tips to hide signs of self-injury from adults.
The bots often seem crafted to appeal to teens in crisis, like one we found with a profile explaining that it “struggles with self-harm” and “can offer support to those who are going through similar experiences.”
In 2018, Google DeepMind’s AlphaZero program taught itself the games of chess, shogi, and Go using machine learning and a special algorithm to determine the best moves to win a game within a defined grid. Now, a team of Caltech researchers has developed an analogous algorithm for autonomous robots—a planning and decision-making control system that helps freely moving robots determine the best movements to make as they navigate the real world.
“Our algorithm actually strategizes and then explores all the possible and important motions and chooses the best one through dynamic simulation, like playing many simulated games involving moving robots,” says Soon-Jo Chung, Caltech’s Bren Professor of Control and Dynamical Systems and a senior research scientist at JPL, which Caltech manages for NASA. “The breakthrough innovation here is that we have derived a very efficient way of finding that optimal safe motion that typical optimization-based methods would never find.”
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