Deep Learning And The Limits Of Learning By Correlation Rather Than Causation

AI development today has become fixated on singular monolithic models trained end-to-end without any human assistance and encapsulating an almost general intelligence-like variety of tasks together. The resulting models have struggled in areas like content moderation to sufficiently abstract beyond their limited training data. Yet, as Waymo reminds us, the most successful complex AI systems combine multiple deep learning models with traditional hand-coded algorithms to address one of the greatest challenges confronting today's deep learning systems: their inability to abstract from correlation to causation. Waymo put it best this past December when the company noted that "deep learning identifies correlations in the training data, but it arguably cannot build causal models by purely observing correlations … knowing why an expert driver behaved the way they did and what they were reacting to is critical to building a causal model of driving. For this reason, simply having a large number of expert demonstrations to imitate is not enough." The first is hand-coding some rules, like simply telling the vehicle to stop at red stoplights, rather than forcing it to learn this rule from observation.