[maweki]

That's not symbolic AI though. That's only statistical methods. The statistical methods are all the rage now, but explainable AI that can reason is an important area of computer science (and research) and uses formal methods.

Edit: yeah, you can downvote this, but current AI research splits right along this line, whether it's symbolic or statistical. Some AI courses will use NNs, others will use Prolog and ASP. You can't just dismiss a whole field of research by reducing AI to statistical methods.

[unishark]

"Expert systems" were the hot research area in AI prior to machine learning (data driven methods, basically). Old methods and problems from that era like automated reasoning still have some research and applications going on, but aren't remotely as big an area as machine learning.

[laichzeit0]

When I see "symbolic AI" I immediately think of Gary Marcus and immediately feel disdain towards the topic because of his behaviour on Twitter and other places.

[maweki]

I don't know the dude. I "only" know that my field of research is deductive reasoning in interactive applications and that this area falls under "Logic Programming" and LP is an area of AI.

I know that AI researchers are usually a bit dismissive about the other area. I don't like statistics either. Reducing the whole of AI research to statistical approaches (and NNs are one of those) is disingenious and dismisses hundreds of researchers doing important work.

You may not want to have rule-based image recognition, but if your car decides to run over somebody, I feel we better have an explanation for this behaviour based on reasoning and logic.

[laichzeit0]

I don’t think anyone is dismissing symbolic AI. As far as I can see, it’s just not beating current SOTA results of NNs? It’s not really about ideology, it’s about what currently has superior performance. Model interpretability is not always a requirement.