Use human or generated contributions in the receiving operation

A person can change task conditions, demonstrate a behavior, choose among candidates or propose a useful alteration. Chapter 8 explains interactive development, branching and preparation of material people can meaningfully judge. Identify what their response supplies and perform the resulting change. A selected image may identify an interesting branch; it does not yet supply the working controller behind that image. Prepare informative comparisons and preserve access to the chosen material, then try the resulting candidate. C.40:4.9/.10 connects comparison and human contribution to this actual continuation.

Generated material can enter as the candidate, a changing operation, training experience or an environment. In Chapter 13, these placements lead to different constructions. For example, a language model can propose code, the code is executed under a specified test, and that result selects or informs the next proposal. The execution and test provide a way to reject fluent but ineffective output. A generated training set instead has to train a learner whose further performance judges the generator; visual plausibility is not that performance. Retain the generator or prompt, generated material, actual receiving operation and its returned result at their respective roles.

A world model adds a further distinction. In Ha and Schmidhuber’s recurrent world-model construction, observations train a compact predictive representation, a controller can develop using the learned dynamics, and its behavior returns to the actual task environment for examination. Search can exploit errors in a learned model, so simulated success leaves that receiving test consequential. The Dreamer 3 construction instead combines continued real interaction, learning a world model and learning behavior through imagined trajectories. The learning operations and information flow differ; calling both a world model does not make their controllers or guarantees interchangeable. C.40:4.8 supplies the general prediction–policy–application return, while the selected source supplies the concrete learner and its update.