In Episode 2 of The World Model Podcast, we dive deep into one of the most fascinating breakthroughs in modern AI: world models—specifically DeepMind’s Dreamer series—and how machines are now “practicing in their sleep.”We break down how Dreamer AIs learn superhuman skills using a fraction of the data once required, thanks to three core components: a compact representation model, an internal imagination engine, and a reward predictor. Together, these let the AI close its eyes and simulate thousands of possible futures, learning entirely inside its own mind.You’ll hear how this approach mirrors human imagination, why “useful hallucinations” can be more powerful than perfect predictions, and how researchers are now applying these methods to robotics—where robots can practice walking, grasping, and problem-solving in internal simulations before ever touching the real world.We also explore the big implications: faster learning, lower costs, and AIs that don’t just react—they imagine. But with that power comes new risks, including flawed world models and the blurred line between simulated and real experience.If you want to understand the shift from brute-force learning to AI that dreams its way to mastery, this episode is your roadmap.Stay until the end for a preview of next week’s topic: how world models could be the missing key to fully autonomous vehicles.