Expanding AI beyond its digital boundary demands reworking how machines think, fusing the digital intelligence of AI with the mechanical prowess of robotics. This is what I call "physical intelligence", a new form of intelligent machine that can understand dynamic environments, cope with unpredictability, and make decisions in real time.
Unlike the models used by standard AI, physical intelligence is rooted in physics; in understanding the fundamental principles of the real world, such as cause-and-effect.
In one experiment, we trained two drones—one operated by a standard AI model and another by a liquid network—to locate objects in a forest during the summer, using data captured by human pilots.
While both drones performed equally well when tasked to do exactly what they had been trained to do, when they were asked to locate objects in different circumstances—only the liquid network drone successfully completed its task.
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