Yes; if anything the hype is even stronger today. The difference is that neural nets on modern hardware really do quite a few useful things, while expert system

Yes; if anything the hype is even stronger today. The difference is that neural nets on modern hardware really do quite a few useful things, while expert systems on 80s hardware mostly did not. The reason the hype of today is more dangerous is what I call the grandmother issue: In the 80s we dreamed of a program that would recognize your grandmother when she walked in the room. That was one of our holy grails. Good news: The grandmother recognizer was finally built with deep neural nets in the 2000s and it works pretty well. It's not perfect but if you tell it to play grandma's favorite song when she walks in the room it's no big deal if it occasionally fails to recognize her, or if it plays the song when someone other than your grandma walks in the room. Bad news: We now have people who (effectively) want to attach the grandmother recognizer to a machine gun with instructions to shoot your grandmother and only your grandmother, and never to fail to shoot her if she walks in the room. Suddenly the Type 1 and Type 2 errors are a whole lot more consequential. Modern NNs are simply not fit for that job. We have pretty good AI for low-consequence purposes, but it cannot be used for high-consequence purposes without a lot more fundamental research. Incremental improvements to deep learning are not going to get us there.