Yes, "GOFAI" overpromised and underdelivered and that was a major reason for the two AI winters that essentially destroyed the field by freezing funding and shrinking research positions and output.
Personally, I'm neither nostalgic of older approaches, nor dismissive of modern approaches. The important thing is to have a clear understanding of the capabilities available, regardless of approach. It's obvious to me that older systems could do things that modern systems can't do (principally, reasoning and knowledge representation) just as modern systems can do things that older systems couldn't do (learning). However, there are approaches that bridge the gap, such as symbolic machine learning], like the approaches I study that learn logic programs from examples using theorem-proving techniques. There is also, of course, continued research in other branches of symbolic AI, like planning and SAT solvers, that seem to have made great progress in the last years. I think the worst that can happen now is to nip such research in the bud by denying it funding just because it's not deep learning.
Gary Marcus' article quotes Emily Bender about how overpromising, this time by the deep learning community, "sucks the oxygen out of the room" for other kinds of research. This is apposite. Research can't become a monoculture, otherwise the ability to innovate will disappear. For innovation, there must be diversity of ideas. The risk I see right now is that such diversity will be lost and that, in the long run, progress in machine learning will stall. Throwing out everything that was learned in the fist 50 years of AI will not help anyone avoid the mistakes of the past, for sure.
I totally agree that there is room and even a need for more symbolic systems within deep learning but I'd argue that you can't at this point do away with the "deep layered" approaches.
The examples you cited are very important achievements, especially the very early ones, but I think they also show that they are also very limited in a lot of ways. For example, expert systems found a niche, but they still had a very hard time with edge cases and learning which imo is essential to intelligence. More traditional logic based algos can vastly outperform say, neural networks in a lot of situations but only when the problem space is in a way "known". Plus, the GOFAI school used to promise a lot, lot more than what those performant but usually hyper specialized systems ended up doing.
I see that my comment could come off as disrespectful for what was accomplished before. But it really isn't!
It's just that I don't agree with the "nostalgics" who usually dismiss the modern approaches and idealized some sort of symbolic vision of intelligence. Those aren't common, and most of my "old school" professors were just as excited by deep learning. But there is a vocal minority imo who are viewing the past with rose tinted glass, when I don't think it's controversial that there is no real way for traditional ("pure") symbolic AI to end up achieving either general intelligence or to outperform deeplearning with finely tuned hand crafted logic.