'World Models,' an Old Idea in AI, Mount a Comeback

Summary

The latest ambition of artificial intelligence research — particularly within the labs seeking “artificial general intelligence,” or AGI — is something called a world model: a representation of the environment that an AI carries around inside itself like a computational snow globe. The AI system can use this simplified representation to evaluate predictions and decisions before applying them to its real-world tasks. The deep learning luminaries Yann LeCun (of Meta), Demis Hassabis (of Google DeepMind) and Yoshua Bengio (of Mila, the Quebec Artificial Intelligence Institute) all believe world models are essential for building AI systems that are truly smart, scientific and safe. The fields of psychology, robotics and machine learning have each been using some version of the concept for decades. You likely have a world model running inside your skull right now — it’s how you know not to step in front of a moving train without needing to run the experiment first. So does this mean that AI researchers have finally found a core concept whose meaning everyone can agree upon? As a famous physicist once wrote: Surely you’re joking. A world model may sound straightforward — but as usual, no one can agree on the details. What gets represented in the model, and to what level of fidelity? Is it innate or learned, or some combination of both? And how do you detect that it’s even there at all? It helps to know where the whole idea started. In 1943, a dozen years before the term “artificial intelligence” was coined, a 29-year-old Scottish psychologist named Kenneth Craik published an influential monograph in which he mused that “if the organism carries a ‘small-scale model’ of external reality … within its head, it is able to try out various alternatives, conclude which is the best of them … and in every way to react in a much fuller, safer, and more competent manner.” Craik’s notion of a mental model or simulation presaged the “cognitive revolution” that transformed psychology in...