physical AI

AI embodied in machines that move and manipulate things in the real world — robotsrobot, n.: A machine that senses its surroundings, decides what to do and physically acts, moving itself or handling objects, with some independence from a human operator. Factory arms, robot vacuums and self-driving cars are all robots. including humanoidshumanoid robot, n.: A robot built roughly in human shape, with a head, torso, two arms and usually two legs, so that it can work in spaces and with tools designed for people., autonomous vehiclesautonomous vehicle, n.: A vehicle, usually a car, truck or shuttle, that can drive itself, sensing the road, deciding what to do and steering, braking and accelerating with no human driving., dronesdrone, n.: An aircraft that carries no one aboard to fly it: a person steers it from the ground, or its own software does. Small camera-carrying quadcopters are the most familiar kind. and automated factories — and that must therefore make sense of physical surroundings before acting on them. As promoted by industry from 2024, the term bundles a technology stack — vision-language-action modelsvision-language-action model, n.: An AI model that looks through a robot's cameras, reads an instruction such as 'put the cup in the sink', and turns both straight into the robot's movements. and other robot foundation modelsrobot foundation model, n.: One large AI model trained on huge amounts of robot and internet data so that it can control many robots on many tasks, and be adapted quickly to new ones.; world foundation modelsworld foundation model, n.: A large AI model trained on vast amounts of video that can imagine how a scene will unfold, used to create realistic practice worlds for robots and self-driving cars. that predict how scenes evolve; physics simulatorsphysics simulator, n.: Software that imitates how real objects move, collide, bend and fall, so robots and their AI can be designed, trained and tested on a computer before touching the real world. and digital twinsdigital twin, n.: A detailed virtual copy of a real machine, factory, robot or city, kept in sync with live sensor data and used to monitor it, test changes safely and predict problems. for generating training data and testing; and onboard inference hardware — with the expectation that methods behind large language modelslarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code. will carry over to embodied tasks. Proponents contrast it with 'digital' or generative AI whose outputs stay on screens. Technically it is close to embodied AIembodied AI, n.: AI that has a body, usually a robot and sometimes a simulated character, and learns or acts by sensing and moving in a world rather than only processing text or images.; the newer term emphasizes deployment at scale and the hard, unsolved problems of dexterity, reliability and safety in unstructured settings (see Moravec's paradoxMoravec's paradox, n.: The observation that what is hard for people, like chess or algebra, is often easy for computers, while what toddlers do effortlessly, such as seeing, walking and picking things up, is very hard for machines.).

Every keynote this year had a physical AI segment, usually with a humanoid walking on stage.