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Robotics Research News -- ScienceDaily

AI won’t replace you but someone using AI might

Generative AI is transforming the workplace faster than ever, but new research from the University of Vaasa suggests the biggest threat may not be AI itself — it’s falling behind in learning how to use it. Researcher Zhe Zhu found that employees who see tools like ChatGPT and Gemini as helpful collaborators rather than job-stealing rivals tend to be more engaged, adaptable, and optimistic about their careers.

Robotics Research News -- ScienceDaily

AI scans 400,000 Reddit posts and finds hidden Ozempic side effects

By analyzing over 400,000 Reddit posts, researchers discovered that users of popular GLP-1 weight-loss drugs frequently discussed unexpected symptoms like menstrual irregularities, chills, and hot flashes. The findings suggest AI could turn social media into a powerful early-warning system for spotting side effects that clinical trials may miss.

Robotics Research News -- ScienceDaily

New AI body map reveals obesity’s hidden attack on facial nerves

Scientists have created an AI-powered system that can scan and map an entire mouse body in extraordinary detail — and it just uncovered a surprising new effect of obesity. Beyond disrupting metabolism, obesity appears to damage facial sensory nerves linked to touch and sensation, while also triggering widespread inflammation across the body.

Robotics Research News -- ScienceDaily

Ordinary WiFi can now identify people with near perfect accuracy

Scientists in Germany have demonstrated a startling new form of surveillance: identifying people using nothing more than ordinary WiFi signals. By analyzing how radio waves bounce around a room, researchers can effectively “see” and recognize individuals — even if they are not carrying a device and even if their phone is turned off.

Robotics Research News -- ScienceDaily

AI reveals the invisible magnetic chaos wasting energy inside electric motors

Electric vehicles are pushing scientists to tackle one of the biggest hidden energy drains inside electric motors: magnetic energy loss. Now, researchers in Japan have developed a powerful AI-driven physics model that can peer into the chaotic “maze-like” magnetic patterns inside motor materials and reveal how heat and microscopic magnetic structures trigger wasted energy.

Robotics Research News -- ScienceDaily

NASA’s new AI space chip could let spacecraft think for themselves

NASA is testing a next-generation space computer chip that could give spacecraft the ability to operate far more independently in deep space. The radiation-hardened processor is showing performance levels hundreds of times beyond current spaceflight computers while surviving punishing tests designed to mimic the harsh conditions of space. The technology could enable AI-powered spacecraft, faster scientific discoveries, and smarter missions to the Moon and Mars.

Robotics Research News -- ScienceDaily

Your “um” and pauses could reveal early dementia risk

The little pauses, “ums,” and moments when you struggle to find the right word may reveal far more about your brain than anyone realized. Researchers discovered that everyday speech patterns are closely tied to executive function — the mental system that powers memory, planning, focus, and flexible thinking. By using AI to analyze natural conversations, the team found they could predict cognitive performance with surprising accuracy, potentially opening the door to simple speech-based tools that could detect early signs of dementia long before traditional testing does.

Robotics Research News -- ScienceDaily

JUPITER supercomputer breaks world record with 50-qubit quantum simulation

Scientists in Germany have pulled off a staggering computing feat by fully simulating a 50-qubit quantum computer for the first time ever using Europe’s new exascale supercomputer, JUPITER. The breakthrough shatters the previous 48-qubit record and highlights just how powerful next-generation supercomputers have become.

Robotics Research News -- ScienceDaily

New AI method tackles one of science’s hardest math problems

Penn researchers have developed a smarter AI method for solving notoriously difficult inverse equations, which help scientists uncover hidden causes behind observable effects. By introducing “mollifier layers” that smooth noisy data, they’ve made these calculations more stable and far less computationally demanding. This could transform fields like genetics, where understanding how DNA behaves is key to disease research.

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