My two cents here are just:
1) Narrow AI is still the botteneck to Strong AI, and a feedback loop of development especially in the area of NLP is what's going to eventualy crack the hardest problems.
2) OpenCog's Hypergraphs do not seem especially useful. The power of a language cannot overcome the fact that without sufficiently strong self-modification techniques, it will never be able to self-modify into anything useful. Interconnects and reflection just allow a program to mess itself up, not become more useful, and scale or better NLP modules alone aren't a solution.
Cross-posted from my blog.
Yudkowsky writes:
My own projection goes more like this:
At least one clear difference between my projection and Yudkowsky's is that I expect AI-expert performance on the problem to improve substantially as a greater fraction of elite AI scientists begin to think about the issue in Near mode rather than Far mode.
As a friend of mine suggested recently, current elite awareness of the AGI safety challenge is roughly where elite awareness of the global warming challenge was in the early 80s. Except, I expect elite acknowledgement of the AGI safety challenge to spread more slowly than it did for global warming or nuclear security, because AGI is tougher to forecast in general, and involves trickier philosophical nuances. (Nobody was ever tempted to say, "But as the nuclear chain reaction grows in power, it will necessarily become more moral!")
Still, there is a worryingly non-negligible chance that AGI explodes "out of nowhere." Sometimes important theorems are proved suddenly after decades of failed attempts by other mathematicians, and sometimes a computational procedure is sped up by 20 orders of magnitude with a single breakthrough.