Artificial Skin Feels Pain While DeepMind AI Predicts Soccer Movements
Researchers at the University of Glasgow create artificial skin that learns to react to simulated pain, while DeepMind introduces an AI system that predicts soccer player movements. These breakthroughs highlight major advancements in robotic sensory processing and sports analytics.
Engineers at the University of Glasgow develop a new type of artificial skin that learns to experience and react to simulated pain. The technology relies on synaptic transistors made from zinc-oxide nanowires printed onto flexible plastic, which mimic the human brain's neural pathways. This built-in artificial synapse speeds up processing by reducing input to a spike in voltage, allowing the skin to respond differently depending on the level of pressure applied.
The Glasgow team sees this innovation serving a crucial role in robotics, where it protects machines from potential damage. For example, the artificial skin prevents a robotic arm from picking up objects that are at dangerously high temperatures. Unlike previous attempts at artificial skin, this design processes touch data directly at the point of contact rather than sending it back to a central computer.
In a separate development, DeepMind creates a machine learning system called Graph Imputer that predicts where soccer players will run on the field. The AI analyzes camera recordings of only a subset of players to accurately anticipate the movements of athletes who are completely off-screen. While not perfect, this predictive model helps teams analyze pitch control and calculate the probability that a specific player could reach the ball from any given location.