The One Hundred Year Study on Artificial Intelligence (AI100) is a longitudinal study that assesses AI's development and societal impact. Reports are released once every five years, with the 2026 edition the third in the series. The title of this year's report is . The panel behind the project is an international committee of experts tasked with examining AI's evolution across technical, ethical, economic, and societal domains. They respond to 12 standing questions about AI's advances, opportunities, dangers, and societal effects, and compare developments since 2021 with the previous panel's assessments.

Claire chatted to Dexter Shepherd from the University of Sussex about using artificial intelligence to help robots understand and adapt to their environment. Dexter Shepherd is a Postdoctoral Researcher at the University of Sussex, exploring machine learning for bio-inspired tactile sensing and robotic locomotion. His work combines embedded AI systems, spatial-temporal models, and reinforcement learning to enable robots to classify textures, orient themselves, and adapt their movements. Beyond his research, Dexter leads and coordinates AI training initiatives in Africa with making electronics and applying AI on embedded devices. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

Jan Petrš is a roboticist, architect, and entrepreneur working at the intersection of robotics and construction. He is currently the co-founder of Déploya Robotics, an EPFL spin-off developing deployable robotic systems for automated façade installation. Previously, he was a Postdoctoral Researcher at EPFL's Laboratory of Intelligent Systems, where he focused on tensegrity robotics. His PhD research at the Czech Technical University focused on developing a robotic system combining reconfigurable and soft robotics for architectural and construction applications. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

A Toyohashi University of Technology team tested whether a robot's approach direction changes how fast it seems to move . Across five experiments using a real mobile robot and virtual reality, 92% of participants showed the same bias: at the same physical speed, a robot approaching from behind seemed faster than one approaching from the front. In the first experiment, participants learned two reference speeds--0.5 and 1.0 metres per second--then judged a robot approaching at speeds between those values. Follow-up experiments suggested the effect wasn't explained by the robot's appearance or motor noise. The researchers link it instead to visual factors involved in turning to look at something coming from behind.

People often take their muscles for granted. They pick up a bag, tighten a grip, climb a hill, hold a cup of coffee and the body quietly adjusts force, speed and position without the person ever thinking about it. Muscle turns chemical energy into movement . It is able to produce force smoothly and reliably, even though it is built from huge numbers of tiny molecular components. When the load changes, muscles don't simply wobble or fail.

Claire chatted to Rika Antonova from the University of Cambridge about using artificial intelligence to develop complimentary robot hardware and algorithms. Rika Antonova is an Associate Professor at the University of Cambridge. Her research interests include co-design, automated algorithm discovery, data-efficient reinforcement learning, exploration, active learning, and robotics. After completing a research Master's degree at the Robotics Institute at Carnegie Mellon University and a PhD at KTH Royal Institute of Technology, Rika undertook a Computing Innovation Fellowship at Stanford University. Before that, she was a senior software engineer at Google.

More than five million industrial robots are now operating in factories worldwide, according to the International Federation of Robotics' newly released World Robotics 2026 report . Factories installed more than 600,000 industrial robots during 2025, an increase of 11% on the previous year. The global operational stock grew by 9%. China continued to pull away from the rest of the world. Its factories installed 354,000 robots, accounting for 59% of the global total.

Swimming can take a lot of muscle. But as MIT engineers have found, even a single layer of muscle cells can power through water if designed right. In a paper appearing today in the journal, the team presents a design for a thin, muscle-powered swimming robot. The "skeleton" of the aquabot is made from a film of gel that is about the length and width of a stick of gum. Each fin is covered with a layer of live muscle cells that is much thinner than a single strand of hair.

Kushant Patel builds software that helps robots and instruments perceive, map, and move through the physical world. After completing a Master's in Mechatronics Engineering at the University of Waterloo, he spent two years at the University of California, Berkeley teaching quadruped robots to map and inspect nuclear material, and a year at the University of California, Davis building closed-loop brain-computer interfaces. At Brookhaven National Laboratory, he is developing control systems for advanced synchrotron beamlines. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines. Robot Talk is a weekly podcast that explores the exciting world of robotics, artificial intelligence and autonomous machines.

At the recent World Humanoid Robot Games 2026 in Beijing, robots sprinted, boxed, played football and tackled tasks inspired by factories, restaurants and emergency situations. The robot Tiangong Ultra even completed the 100 metres in 8.64 seconds, quicker than Usain Bolt's 9.58-second world record. But if we really want to know whether humanoid robots are ready for everyday life, a far less glamorous challenge may tell us more: doing the laundry. Can a robot take a crumpled T-shirt from a laundry basket, work out which way round it is, fold it neatly and recover when the sleeve slips from its hand? No robot system can yet reliably handle the whole laundry process, from carrying a basket to the washing machine and sorting clothes for different programmes to taking them out, hanging them to dry, folding and ironing them.