[colllectorof]

> I was around at the time of, and affected by, the AI winter. There was certainly no consensus among those working in AI that they had got as far as they could. Work stopped when funding was cut, often for political reasons.

I wasn't around, but I got curious about symbolic systems after listening to MIT's AI course[1]. Did some reading about the subject. The impression I got matches what you describe.

It's ridiculous how many people here dogmatically recite statements about failures of symbolic systems without (apparently) knowing anything about how those systems were used and what they achieved. If you listen to the comments, it sounds as if research on symbolic systems only ever produced crude, useless toys. That was certainly my impression before I took some time to actually look into it. A bit of straightforward Googling can show that it's a gross misrepresentation of history. For example, MIT's lecture on knowledge engineering [2] has some really interesting info on this subject.

[1] http://ocw.mit.edu/courses/electrical-engineering-and-comput...

[2] http://ocw.mit.edu/courses/electrical-engineering-and-comput...

[nl]

I've done symbolic AI work. It's great within limits. Deep learning on its own isn't the complete solution either, but statistics and learning are more important than symbolics for achieving breakthrough performance.

I'd invite you to read "The Master Algorithm" to understand exactly how they failed the first time and how they aren't the route forward: https://en.m.wikipedia.org/wiki/The_Master_Algorithm