Predictive learning is a machine learning technique where an artificial intelligence model is fed new data to develop an understanding of its environment, capabilities, and limitations. The fields of neuroscience, business, robotics, computer vision, and other fields employ this technique extensively. This concept was developed and expanded by French computer scientist Yann LeCun in 1988 during his career at Bell Labs, where he trained models to detect handwriting so that financial companies could automate check processing.[1]
The mathematical foundation for predictive learning dates back to the 17th century, where the British insurance company Lloyd's used predictive analytics models to make a profit.[2] Starting out as a mathematical concept, this concept expanded the possibilities of artificial intelligence. Predictive learning is an attempt to learn with a minimum of pre-existing mental structure. It was inspired by Piaget's account of children constructing knowledge of the world through interaction. Gary Drescher's book 'Made-up Minds' was crucial to the development of this concept.[3]
The idea that predictions and unconscious inference are used by the brain to construct a model of the world, in which it can identify causes of percepts, goes back even further to Hermann von Helmholtz's iteration of this study. Those ideas were later picked up in the field of predictive coding. Another related predictive learning theory is Jeff Hawkins' memory-prediction framework, which is laid out in his book On Intelligence.