Earlier quoted context omitted.
The recommendation, translation, & image classification algorithms are all done with deep-learning; that's considered AI now. There was a time, not all that long ago, when SVMs, Bayesian networks, and perceptrons were considered AI. That's behind the spam filters, predictive keyboards, and most of the search signals. There was a time, a bit longer ago, when beam search and A* were considered AI. That's behind the gam…
This is my point: the term AI has always been BS. It was BS when beam search was AI, it was BS when expert systems were AI, and it is equally as BS when applied to neural networks. It comes to the same thing: the 'AI' tools we use are increasingly good function approximators. That's it. It's still reaching the moon by building successively taller ladders.
As much as I look into what’s being done with deep learning, I see they’re all stuck there on the level of associations. Curve fitting. That sounds like sacrilege, to say that all the impressive achievements of deep learning amount to just fitting a curve to data. From the point of view of the mathematical hierarchy, no matter how skillfully you manipulate the data and what you read into the data when you manipulate it, it’s still a curve-fitting exercise, albeit complex and nontrivial.
And
I left the arena to pursue a more challenging task: reasoning with cause and effect. Many of my AI colleagues are still occupied with uncertainty. There are circles of research that continue to work on diagnosis without worrying about the causal aspects of the problem.