Teams talk about NLP – natural language processing – in test automation, yet many still ask what it truly means. They hear about tools that turn plain language into test scripts, but they want clear facts. This topic matters now because software teams face tight release cycles and constant change. NLP in test automation means […]
Positronic Robotics launches ‘PhAIL’ benchmark to test real-world performance of physical AI systems
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 […]
Robotics teams rarely struggle because a part cannot be printed at all. More often, they lose time because a part arrives with the wrong material, the wrong orientation, a missed drawing note, or small inconsistencies that only become obvious during assembly or testing. That is why speed alone is not enough. For robotics teams, a […]
Deepfake X-rays created by AI are now convincing enough to fool both doctors and AI models. In tests, radiologists had limited success identifying fake images, especially when they didn’t know they were being shown. This opens the door to risks like fraudulent medical claims and tampered diagnoses. Experts say stronger safeguards and detection tools are critical as the technology advances.
Comau and Reis Robotics partner to deliver advanced automation systems across key industrial sectors
Comau and Reis Robotics have signed a cooperation agreement aimed at jointly developing and delivering advanced automation products and solutions for multiple industrial sectors through a coordinated technical and commercial approach. The agreement signed today by Comau and Reis Robotics, respectively Italian and German companies, testifies to the strength of the European technology framework in […]
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.
As AI systems began acing traditional tests, researchers realized those benchmarks were no longer tough enough. In response, nearly 1,000 experts created Humanity’s Last Exam, a massive 2,500-question challenge covering highly specialized topics across many fields. The exam was engineered so that any question solvable by current AI models was removed. Early results show even the most advanced systems still struggle — revealing a surprisingly large gap between AI performance and true expert-level knowledge.
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.
Scientists warn that rapid advances in AI and neurotechnology are outpacing our understanding of consciousness, creating serious ethical risks. New research argues that developing scientific tests for awareness could transform medicine, animal welfare, law, and AI development. But identifying consciousness in machines, brain organoids, or patients could also force society to rethink responsibility, rights, and moral boundaries. The question of what it means to be conscious has never been more urgent—or more unsettling.
NASA’s Perseverance rover has just made history by driving across Mars using routes planned by artificial intelligence instead of human operators. A vision-capable AI analyzed the same images and terrain data normally used by rover planners, identified hazards like rocks and sand ripples, and charted a safe path across the Martian surface. After extensive testing in a virtual replica of the rover, Perseverance successfully followed the AI-generated routes, traveling hundreds of feet autonomously.
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.
Spanish researchers have created a powerful new open-source tool that helps uncover the hidden genetic networks driving cancer. Called RNACOREX, the software can analyze thousands of molecular interactions at once, revealing how genes communicate inside tumors and how those signals relate to patient survival. Tested across 13 different cancer types using international data, the tool matches the predictive power of advanced AI systems—while offering something rare in modern analytics: clear, interpretable explanations that help scientists understand why tumors behave the way they do.
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.
AI-powered analysis of routine blood tests can reveal hidden patterns that predict recovery and survival after spinal cord injuries. This breakthrough could make life-saving predictions affordable and accessible in hospitals worldwide.
Scientists at Mount Sinai have created an artificial intelligence system that can predict how likely rare genetic mutations are to actually cause disease. By combining machine learning with millions of electronic health records and routine lab tests like cholesterol or kidney function, the system produces "ML penetrance" scores that place genetic risk on a spectrum rather than a simple yes/no. Some variants once thought dangerous showed little real-world impact, while others previously labeled uncertain revealed strong disease links.