If intelligence is a computation, then the temporal stream of sensations is its input, and the temporal stream of actions is its output. These two intermingled time series make up experience. They are the basis on which all intelligent decisions are made and the basis on which those decisions are judged. A focus on experience has implications for many aspects of AI; in this talk we consider its implications for knowledge representation. I propose that it is possible and desirable for an AI agent's knowledge of the world to be expressed entirely as predictions about its low-level experience. Even abstract concepts, such as the concept of a chair, can be expressed as predictions, e.g., about what will happen if we try to sit. The predictive approach is appealing because it connects knowledge directly to data, allowing knowledge to be autonomously verified and tuned, perhaps even learned. However, there is a tremendous gap between human-level knowledge (e.g., about space, objects, people, or water) and low-level experience. The purpose of this talk is to present some recent work suggesting how this gap might someday be bridged. I describe a series of small experiments in which extensions of reinforcement learning methods are used to learn predictive representations of abstract commonsense knowledge in micro-worlds. These are first steps on a long journey toward understanding how a mind might make sense of the blooming, buzzing confusion of its sensori-motor experience.