Many of the robustness properties that are required for real-world applications of AI would be realized by a model that has understood the world. But it is unclear how to measure understanding, let alone how to define it. This talk will propose theoretically-grounded definitions and metrics that test for a model’s implicit understanding, or its world model. We will focus on two kinds of settings: one where implicit world models are tested behaviorally, and another that tests a model’s representation. These exercises demonstrate that models can make highly accurate predictions with incoherent world models, revealing their fragility.

Zoom: https://upenn.zoom.us/j/95189835192

Passcode: 797599