Elon Musk's plan to show off his dancing robot has left the Tesla CEO red-faced after his own AI made an embarrassing admission. On X, which Musk owns, the billionaire shared an impressive'real real-time' video of Tesla's humanoid robot, Optimus, demonstrating some flashy dance moves. However, when a curious commenter asked the billionaire's AI, Grok, to rate the robot, the chatbot declared Optimus' usefulness is'limited'. Adding to the humiliation, the AI then ranked Optimus at the bottom of its list of the best humanoid robots. In response to the user's question, Grok wrote: 'Tesla's Optimus robot shows advanced mobility with its dancing, highlighting strong balance and coordination. 'However, its real-world use is limited, mainly in Tesla's factories for simple tasks, with broader deployment planned for 2026.' Putting Optimus at the bottom of its top-five ranking, the AI added that the robot has'impressive mobility but early in practical application.'

When it comes to the evolution of mobile robots, it may be a long time before legged robots can safely interact in the real world, according to a new study. Led by a team of researchers at The Ohio State University, the study published recently in the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022 describes a framework for testing and characterizing the safety of legged robots, machines that, unlike their wheeled counterparts, rely on mechanical limbs for movement. The study found that many current legged robotic models don't always act predictably in response to real-life situations, meaning it's hard to predict whether they'll fail – or succeed – at any given task that requires movement. "Our work reveals that these robotic systems are complex and, more importantly, anti-intuitive," said Bowen Weng, a Ph.D. student in electrical and computer engineering at Ohio State. "It means you can't rely on the robot's ability to know how to react in certain situations, so the completeness of the testing becomes even more important."

Once feared as a job-erasing technology devoid of clinical nuance, autonomous artificial intelligence in medicine is growing up.

This is our third and final post looking at real-world uses of graph visualization. First we explored how link analysis evolved from physical'investigation boards' used by law enforcement, helping analysts to link people, locations and events at scale. Then we reviewed how it's helping to tackle the scale and complexity of cyber security data. This time, we're going to review less obvious use cases where graph visualization helps analysts transform complex data into useable insight. It might not be the most exciting topic, but regulatory compliance is critical for all organizations.