Prediction's rise as a theory of everything isn't restricted to culture alone; it's also the most widely accepted theory of how minds work as well. The canonical reference on the Predictive Mind is Hohwy's book on the topic, but that's not layperson friendly. Instead, I am posting a link to a Quanta article on predictive processing.

Predictive processing is a framework suggesting that the brain constantly generates predictions about incoming sensory information and updates its internal models based on the differences between expectation and reality. This process helps the brain efficiently interpret the world by minimizing prediction errors, which are discrepancies between what the brain expects and what it actually perceives.

DeepMind's Generative Query Network (GQN) exemplifies this concept in artificial intelligence. GQN can infer three-dimensional scenes from two-dimensional images by predicting what a scene should look like from new perspectives and refining its model based on prediction errors. Neuroscientists find this approach fascinating because it mirrors how the brain might process information, supporting the idea that the brain functions as a sophisticated inference machine.

Predictive coding builds on the "Bayesian brain" hypothesis, which proposes that the brain makes probabilistic inferences to interpret sensory data. Instead of passively receiving information, the brain actively constructs hypotheses to explain experiences, filling in gaps and resolving ambiguities. This explains phenomena like visual illusions and why perception is sometimes described as a form of controlled hallucination.

The theory has broad implications, potentially unifying perception, motor control, attention, decision-making, emotions, and even self-awareness under a single computational principle. Experimental evidence, such as studies on visual processing in mice and face recognition in monkeys, supports predictive coding by showing how neurons encode prediction errors at different brain levels.

However, predictive coding remains controversial. Some experiments have failed to replicate key findings, and debates continue about its scope and mechanisms. Despite this, researchers are optimistic about its potential to deepen understanding of brain function and improve machine learning. By integrating predictive coding principles, artificial intelligence could become more efficient and intelligent, while neuroscience gains new tools to unravel the complexities of cognition and mental disorders.