Thoughts I found interesting:

The failures indicate that, instead of being threads in a majestic general theory, the successes were just narrow, isolated solutions to problems that turned out to be easier than they originally appeared.

Interesting because I don't think it's true. I think the problem is more about the need of AI builders to show results. Providing a solution (or a partial solution or a path to a solution) in a narrow context is a way to do that when your tools aren't yet powerful enough for more general or mixed approaches. Given the variety of identifiable structures in the human brain that gives us intelligence I strongly expect that an AI will be built by combining many specialized parts that will probably be based on multiple research areas we'd recognize today.

One obvious influence comes from computer science, since presumably AI will eventually be built using software. But this fact appears irrelevant to me, and so the influence of computer science on AI seems like a disastrous historical accident.

Interesting because it forced me to consider what I think AI is outside the context of computer science - something I don't normally do.

In my view, AI can and must become a hard, empirical science, in which researchers propose, test, refine, and often discard theories of empirical reality.

Interesting because I'm very curious to see what this means in the context of your coming proposal.