Our brains constantly make predictions about the world around us. We assess what we’re uncertain about, and what we need to resolve. Once our brains model the world, they learn over time and continuously improve. When you walk toward a crosswalk, your brain predicts whether cars will stop. If you’re unsure, you look both ways, wait, or step back. Your brain updates its understanding every moment until you feel safe to walk.
That cycle—sense, predict, update, act—is active inference in motion, a continuous predict-and-act loop that is deeply coded into humans, allowing us to adapt and improve. Our brains are highly efficient, operating on only 20 watts, just enough to power a light bulb.
Most AI systems today fail to retain feedback, adjust to context, or improve over time. What's missing, as a recent MIT study pointed out, are systems that adapt, remember, and evolve.
Active inference can fill this role. It’s how all natural systems—humans, animals, plants—address the volatile, uncertain, complex, and ambiguous world around them. But these approaches were long thought impossible to scale in computing.
This is what we have tackled with Genius™: Agents that adapt, learn, and get smarter in real time. Visit our models to find out more.
This prediction-action update cycle is how people as well as Genius-powered agents learn and adapt in real time.