When I was an undergrad doing AI at the start of the 90s, the 'general AI architecture' seemed to already be a passing phase. Though many of the proposed archi

When I was an undergrad doing AI at the start of the 90s, the 'general AI architecture' seemed to already be a passing phase. Though many of the proposed architectures were not symbolic, the approach felt part of the symbolic AI bubble that had recently burst. The idea is appealing, but the difficulty in showing success even in isolated tasks made the claims of general architectures rather bald and self-aggrandising. I felt at the time that it would take a lot of progress before it was warranted. We'd need to actually develop tools that were widely applicable and solve certain fundamental problems (the frame problem, the problem of behavior composition, the problem of inference). I'm not entirely convinced 'deep learning' is as widely applicable or capable as its hype, and we haven't found too many interesting new general purpose cognitive algorithms in the last 30 years. So it still feels way too early to be talking about general models of cognition, when we don't have convincing data that shows the generation of cognition in any context. An interesting paper, but it feels very 1980s, to me. At root a combination of AI techniques glued together with hand waving and optimism.