Statisticians like to insist that correlation should not be confused with causation. Most of us intuitively understand this actually not a very subtle difference. We know that correlation is in many ways weaker than causal relationship. A causal relationship invokes some mechanics, some process by which one process influences another. A mere correlation simply means that two processes just happened to exhibit some relationship, perhaps by chance, perhaps influenced by yet another unobserved process, perhaps by an entire chain of unobserved and seemingly unrelated processes. 

When we rely on correlation, we can have models that are very often correct in their predictions, but they might be correct for all the wrong reasons. This distinction between weak, statistical relationship and a lot stronger, mechanistic, direct, dynamical, causal relationship is really at the core of what in my mind is the fatal weakness in contemporary approach in AI. 

The argument

Let me role play, what I think is a distilled version of a dialog between an AI enthusiast and a skeptic like myself: 

AI enthusiast: Look at all these wonderful things we can do now using deep learning. We can recognize images, generate images, generate reasonable answers to questions, this is … Read more...