Imagine a near-future robot with a silicon brain and a humanlike body, equipped with all kinds of sensors and effectors. This robot is controlled by an artificial neural network designed according to the principles of predictive processing and active inference. The signals flowing through its circuits implement a generative model of its environment, and of its own body. It is constantly using this model to make Bayesian best guesses about the causes of its sensory inputs. These synthetic controlled (and controlling) hallucinations are geared, by design, towards keeping the robot in an optimal
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