[2018]

His views about AI are largely disproved now. GPT style systems turned out to be perfectly capable of reasoning about causation, as they can reason about any other relation. Despite working entirely in the established machine learning paradigm.

For many years, Pearl was considered the top intellectual critic of machine learning. His point was this: Machine learning is, at its core, just correlational. But true AI would also need to reason about causation. This ability would have to be provided by systems which work entirely differently; by using some form of the theory of causal networks which he co-invented.

Now it turns out that causal reasoning is not a major difficulty for classical machine learning, and that causal graphs are likely as useless for AI as formal logic turned out to be.

Well, at least causal networks are useful for statistics, the type of explicit inference human scientists do.