{
  "id": 7033341,
  "title": "World Models: The AI That Learned to Dream Before It Could Walk",
  "url": "https://urgent.news/2026/09/13/world-models-the-ai-that-learned-to-dream-before-it-could-walk",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-13T00:39:37.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/abdullahbinaqeel/world-models-the-ai-that-learned-to-dream-before-it-could-walk-3kam"
  },
  "original_language": "en",
  "account": "The race to create AI capable of imagining the future has been ongoing for eight years, with two distinct phases: rehearsing inside compressed simulations and generating the simulation itself. Traditionally, machine learning involved real-world experimentation, which was time-consuming and costly, like a robot arm learning to pour coffee or a robot learning to walk. However, a new approach involves AI creating its own internal simulation, or \"world model,\" to practice virtually.\n\nIn 2018, researchers David Ha and Jürgen Schmidhuber developed an AI that learned to drive a car and survive a shooter game almost entirely through simulated practice, averaguing a score of 906 against a passing bar of 900. This breakthrough marked the first official \"solution\" to the CarRacing benchmark and set the stage for further developments.\n\nWorld models, which predict an environment's response to actions, have real-world applications. For instance, Google DeepMind's Dreamer series of agents learned to complete challenging tasks in Minecraft, like collecting a diamond, using simulated practice instead of millions of real attempts. A follow-up project, DayDreamer, applied this concept to a real quadruped robot, enabling it to stand up, walk, and recover from falls in a matter of hours.\n\nThe implications are vast, as companies like NVIDIA, 1X, Agility, and self-driving firms like Waabi and Wayve have adopted similar technologies to teach robots and self-driving systems physical common sense faster and more efficiently. Self-driving cars, for example, now use generative world models to simulate rare, dangerous scenarios, significantly reducing the time and cost associated with traditional training methods.",
  "summary": "Big Ideas in AI — Part 01 Inside the race to build machines that can imagine the future, and what is still stopping them Press enter or click to view image in full size Eight years, one idea. The first half of the timeline is about agents rehearsing inside compressed simulations. The second half is about generating the simulation itself. Picture a robot arm in a lab, trying to learn how to pour a…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}