Toyota Material Handling Europe has introduced a new automated guided vehicle (AGV) system designed to handle a range of warehouse transport tasks and pallet types. Called Swarm Automation Transport, the system combines the company’s SAI125CB automated counterbalance stacker with its T-ONE control software platform, enabling coordination across automated and mixed fleets. The company says the […]
Vecna Robotics, a provider of flexible material handling automation, has announced CaseFlow Voice, which the company says is the only case picking automation solution with fully integrated voice technology. CaseFlow Voice embeds Lucas Systems’ “Jennifer” voice capabilities directly into case picking workflows, enabling hands-free operations that improve worker productivity and accuracy while helping warehouses achieve […]
Scientists in Japan say they have developed a new approach – dubbed ‘HEAPGrasp’ – that improves robots’ grasping success rate for transparent and shiny objects, beyond reducing handling time, using only RGB camera The fields of manufacturing, logistics, and even restaurants are increasingly moving toward automation, with robots being employed for a wide range of […]
Robots make work faster, cleaner, and more consistent. They also change how risk shows up on the floor. The danger is not only the moving arm, but also the in-between moments, when a cell is paused, a jam is cleared, or a quick adjustment turns into hands inside the fence. Operators sit closest to these […]
Festo has introduced the HPSX Universal Adaptive Gripper, a pneumatic soft gripper engineered to improve speed, hygiene and flexibility in demanding food, pharmaceutical and cosmetics applications. Combining robust design, food-safe materials and adaptive silicone fingers, the HPSX directly addresses long-standing automation challenges where rapid, precise and gentle product handling is essential. Peter Potters, product manager […]
Neura Mobile Robots has presented an application for mobile manipulation in intralogistics for the first time at LogiMAT. Under its ek Robotics brand, Neura demonstrated how mobile transport robotics and cognitive robotics can be combined into a seamless system, enabling a new level of automation. At the heart of the application is the combination of […]
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 […]
Researchers at Kobe University have developed an AI system that can detect acromegaly, a rare hormone disorder, by analyzing photos of the back of the hand and a clenched fist. The disease often develops slowly and can take years to diagnose, even though untreated cases may shorten life expectancy.
As millions turn to ChatGPT and other AI chatbots for therapy-style advice, new research from Brown University raises a serious red flag: even when instructed to act like trained therapists, these systems routinely break core ethical standards of mental health care. In side-by-side evaluations with peer counselors and licensed psychologists, researchers uncovered 15 distinct ethical risks — from mishandling crisis situations and reinforcing harmful beliefs to showing biased responses and offering “deceptive empathy” that mimics care without real understanding.
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.
AI may learn better when it’s allowed to talk to itself. Researchers showed that internal “mumbling,” combined with short-term memory, helps AI adapt to new tasks, switch goals, and handle complex challenges more easily. This approach boosts learning efficiency while using far less training data. It could pave the way for more flexible, human-like AI systems.