This article doesn’t have any substance. It’s full of anecdata like shifting by 3 pixels to mess up a video game AI or some vague nonsense about “a model of thi

This article doesn’t have any substance. It’s full of anecdata like shifting by 3 pixels to mess up a video game AI or some vague nonsense about “a model of this chair or this tv mounted to the wall.” It’s all casual hypotheticals. There’s plenty of research on Bayesian neural networks for causal inference. But even more, a lot of causal inference problems are “small data” problems where choosing a strongly informative prior to pair with simple models is needed to prevent overfitting and poor generalization and to account for domain expertise. Deep learning practitioners generally know plenty about this stuff and fully understand that deep neural networks are just one tool in the tool box, not applicable to all problems and certainly not approaching any kind of general AI solution that supersedes causal inference, feature engineering, etc. This article is just a sensationalist hit job trying to capitalize on public anxieties about AI to raise the profile of this academic and try to sell more copies of his book. I’d say, let’s not waste time on this crap. There are engineering problems that deep learning allows us to safely & reliably solve where other methods never could. We absolutely can trust these models for specific use cases. Let’s just get on with doing the work.