My two cents: they are separate because there is no current algorithm that can take us from modeling (whether classical statistics or neural net) to intelligence. Applying our current specialized techniques to AI generation has not gotten us there. That is because the techniques are mostly model tweaking techniques. The models are generated and trained for each problem domain. A combined solution may be developed soon, but I doubt it.
There was a great article recently on HN that highlights the current problems:
Just because we may acquire the processing power estimated to be used in the brain (in operations per second) doesn't mean we know how to write the software to accomplish the task. It is very clear current algorithms won't cut it.
Also, I think we are a few orders of magnitude off on raw processing requirements because I think it is a bandwidth issue as much as an operations per second issue.
TL;DR - you could throw as much processing power and data as you want at any current deep NN or their derivatives and you wouldn't get general intelligence.
That said I don't think the winter will be as bad as before because, like OP says, specialized AI is useful.
There was a great article recently on HN that highlights the current problems:
http://www.theverge.com/2016/10/10/13224930/ai-deep-learning...
https://news.ycombinator.com/item?id=12684417
Just because we may acquire the processing power estimated to be used in the brain (in operations per second) doesn't mean we know how to write the software to accomplish the task. It is very clear current algorithms won't cut it.
Also, I think we are a few orders of magnitude off on raw processing requirements because I think it is a bandwidth issue as much as an operations per second issue.
TL;DR - you could throw as much processing power and data as you want at any current deep NN or their derivatives and you wouldn't get general intelligence.
That said I don't think the winter will be as bad as before because, like OP says, specialized AI is useful.