Intelligence plays dice: Stochasticity is essential for machine learning

When solving an equation, using the result to encode a message, transmitting the coded message to another device, decoding the message at the other end, saving the message onto a hard drive, or using it to create a visual rendering; inaccuracies are often the system's enemy and have to be fought against. Furthermore, if and when any of these computational operations is repeated, we expect the results to be unchanged. We view an unrepeatable result as a sign of a "bug" that either has to be fixed, tamed, or at least well understood and tolerated. Reduced precision and reliability is often considered as a price in the tradeoff with computational efficiency. The central thesis of this perspective article is that for machine learning (ML) specifically, and artificial intelligence (AI) more generally, probabilistic operations are fundamentally important building blocks, which the field is growing to rely on. We anticipate that stochasticity will therefore feature more prominently, and as a fundamental principle, in the future of machine intelligence.