It seems today people write off those demos as showy, shallow and misleadingly optimistic. But if you think about it, the exact same thing can be said about modern ML demonstrations. A lot of them hint at nearly human-level intelligence, but that's often achieved through careful restriction on the problem domain and by choosing the most successful examples.
Considering that old-school AI was operating on laughable hardware with tiny hand-made datasets, with no crowd-sourcing options... I wonder, did attempts to generalize some of that research fail because the approach was fundamentally flawed, or was it because such efforts themselves weren't as good as initial projects?
I mean, the article on Wikipedia talks about "more realistic level of ambiguity and complexity", but the demonstrated domain is already more complex than 90% of all programming problems I solve on day-to-day basis.
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I though Minsky also did something similar, but with physical blocks and a robotic arm, and without the NLP interface. Anyone knows about that? Or am I making it up? I was looking for info about this recently and couldn't find anything.