Probabilistic algorithms and state machines work by massively parallel stochastic walks, rather than carefully coordinated sequences of deterministic steps. We expect them to eventually produce desired outputs in reasonable proportions, rather than perform any given step precisely. This may help us model biological, neural, psychological and social systems, which robustly exhibit reasonable behavior under a wide range of conditions but rarely - if ever - can be made to repeat themselves perfectly.

Our machines will begin to sanity check implausible inputs and sample plausible alternatives rather than blindly follow our instructions. Our interactions will someday be taken as noisy evidence, interpreted with respect to probabilistic programs that model our intent, rather than taken as definite inputs to some deterministic function. This epistemological flexibility, arising from the wiggle room afforded by probability, could potentially allow us to one day build a probabilistic computer that is not well described by the phrase “garbage in, garbage out”.

Probabilistic computation may also provide clues for understanding neural computation and cognitive architecture. We can let go of our focus on calculating probabilities and optimal actions, instead favoring systems that sample good guesses. For example, neural systems may appear noisy because they are trying to solve problems of inference and decision making under uncertainty by sampling. The variability might not be Gaus sian error around some linearized set-point, but rather the natural dynamics of a distributed circuit that is robustly hallucinating world states in accordance with a generative probabilistic model and the evidence of the senses.