机器人资讯 魔法院子

Positronic Robotics has introduced a new benchmarking initiative aimed at evaluating how well AI-driven robots perform in real-world industrial tasks, as interest grows in so-called “physical AI” systems. The benchmark, called PhAIL (Physical AI Leaderboard), measures robotic performance using operational metrics such as units per hour and mean time between failures, rather than traditional academic […]

Agibot, a leading robotics company specializing in embodied intelligence, said it has rolled out its 10,000th humanoid robot, becoming one of the first companies in the industry to reach this milestone at scale.  More than a production figure, the achievement marks a significant step forward for the robotics industry, signaling a transition from early-stage validation […]

Money usually goes away sooner than one anticipates. Some minor things, a lost subscription, and all of a sudden, the bank account shows that the balance is lower than intended. Several families go through this situation despite a stable income. Here, a personal budget application will come in handy. It may seem that it is […]

Corvus Robotics and Southern Glazer’s Wine & Spirits have agreed a strategic technology partnership and expanded deployment of the Corvus One autonomous inventory management system across Southern Glazer’s distribution network. Over the past 18 months, Southern Glazer’s has deployed more than 40 Corvus One autonomous drones across nine distribution centers nationwide, with continued expansion planned. […]

AI’s growing energy use sounds alarming, but its global climate impact may be far smaller than expected. Researchers found that while AI consumes huge amounts of electricity, it barely moves the needle on overall emissions. The real impact is more localized, especially around data centers. Meanwhile, AI could become a powerful tool for building greener technologies.

A new study put ChatGPT to the test by asking it to judge whether hundreds of scientific hypotheses were true or false—and the results were far from reassuring. While the AI got it right about 80% of the time on the surface, its performance dropped significantly when accounting for random guessing, revealing only modest reasoning ability. Even more concerning, it frequently contradicted itself when asked the exact same question multiple times, sometimes flipping answers back and forth.

Artificial intelligence is often portrayed as a tool that replaces human work, but new research from Swansea University suggests a far more exciting role: creative collaborator. In a large study with more than 800 participants designing virtual cars, researchers found that AI-generated design galleries sparked deeper engagement, longer exploration, and better results.

Researchers tested whether generative AI could handle complex medical datasets as well as human experts. In some cases, the AI matched or outperformed teams that had spent months building prediction models. By generating usable analytical code from precise prompts, the systems dramatically reduced the time needed to process health data. The findings hint at a future where AI helps scientists move faster from data to discovery.

Neuromorphic computers modeled after the human brain can now solve the complex equations behind physics simulations — something once thought possible only with energy-hungry supercomputers. The breakthrough could lead to powerful, low-energy supercomputers while revealing new secrets about how our brains process information.

Dinosaur footprints have always been mysterious, but a new AI app is cracking their secrets. DinoTracker analyzes photos of fossil tracks and predicts which dinosaur made them, with accuracy rivaling human experts. Along the way, it uncovered footprints that look strikingly bird-like—dating back more than 200 million years. That discovery could push the origin of birds much deeper into prehistory.

A massive new study comparing more than 100,000 people with today’s most advanced AI systems delivers a surprising result: generative AI can now beat the average human on certain creativity tests. Models like GPT-4 showed strong performance on tasks designed to measure original thinking and idea generation, sometimes outperforming typical human responses. But there’s a clear ceiling. The most creative humans — especially the top 10% — still leave AI well behind, particularly on richer creative work like poetry and storytelling.

Scientists have discovered that the human brain understands spoken language in a way that closely resembles how advanced AI language models work. By tracking brain activity as people listened to a long podcast, researchers found that meaning unfolds step by step—much like the layered processing inside systems such as GPT-style models.

A generative AI system can now analyze blood cells with greater accuracy and confidence than human experts, detecting subtle signs of diseases like leukemia. It not only spots rare abnormalities but also recognizes its own uncertainty, making it a powerful support tool for clinicians.

Researchers have created microscopic robots so small they’re barely visible, yet smart enough to sense, decide, and move completely on their own. Powered by light and equipped with tiny computers, the robots swim by manipulating electric fields rather than using moving parts. They can detect temperature changes, follow programmed paths, and even work together in groups. The breakthrough marks the first truly autonomous robots at this microscopic scale.

A philosopher at the University of Cambridge says there’s no reliable way to know whether AI is conscious—and that may remain true for the foreseeable future. According to Dr. Tom McClelland, consciousness alone isn’t the ethical tipping point anyway; sentience, the capacity to feel good or bad, is what truly matters. He argues that claims of conscious AI are often more marketing than science, and that believing in machine minds too easily could cause real harm. The safest stance for now, he says, is honest uncertainty.

AI tools designed to diagnose cancer from tissue samples are quietly learning more than just disease patterns. New research shows these systems can infer patient demographics from pathology slides, leading to biased results for certain groups. The bias stems from how the models are trained and the data they see, not just from missing samples. Researchers also demonstrated a way to significantly reduce these disparities.

New findings challenge the widespread belief that AI is an environmental villain. By analyzing U.S. economic data and AI usage across industries, researchers discovered that AI’s energy consumption—while significant locally—barely registers at national or global scales. Even more surprising, AI could help accelerate green technologies rather than hinder them.

Researchers combined deep learning with high-resolution physics to create the first Milky Way model that tracks over 100 billion stars individually. Their AI learned how gas behaves after supernovae, removing one of the biggest computational bottlenecks in galactic modeling. The result is a simulation hundreds of times faster than current methods.

Researchers have created a prediction method that comes startlingly close to real-world results. It works by aiming for strong alignment with actual values rather than simply reducing mistakes. Tests on medical and health data showed it often outperforms classic approaches. The discovery could reshape how scientists make reliable forecasts.

Researchers at Tsinghua University developed the Optical Feature Extraction Engine (OFE2), an optical engine that processes data at 12.5 GHz using light rather than electricity. Its integrated diffraction and data preparation modules enable unprecedented speed and efficiency for AI tasks. Demonstrations in imaging and trading showed improved accuracy, lower latency, and reduced power demand. This innovation pushes optical computing toward real-world, high-performance AI.