> I was referring to the stuff I mention later in my comment: Anomaly detection, image classification/segmentation, upscaling data, even interpolation, complex forecasting, text generation, text to speech, speech recognition, etc.

I think most/all of this was not target of GOFAI.

> I'm curious about where gofai is still outperforming modern techniques in complex tasks. As in, genuinely curious, because I want to be wrong on this!

theorem proving, equation solving as example. NN still suck in deep symbolic math and reasoning.

True, I didn't consider those as being AI tasks but that's just proving your point!

Though I think that a lot of those (certainly image classification, face recognition, TTS, etc) were tasks that were very important in the field back in the 1970/80/90s. A lot of resources were spent on AI specifically for those purposes.

> Though I think that a lot of those (certainly image classification, face recognition, TTS, etc) were tasks that were very important in the field back in the 1970/80/90s. A lot of resources were spent on AI specifically for those purposes.

Maybe, it is hard for me to see how you came to such conclusion. But discussion is about specific term GOFAI. I would look at following as source of truth:

- wiki definition, which explicitly states that GOFAI is symbolic AI(rule based reasoning, like prolog, cog, cyc).

- table of content of 2nd edition of Norvig book: https://aima.cs.berkeley.edu/2nd-ed/contents.html which has very little about what you described, but mostly focuses on search, discrete algos and reasoning.