In terms of practicalities, AI and AGI share two letters in common, and that's about it. OpenCog / CogPrime is at core nothing more than an interface language specification built on hypergraphs which is capable of storing inputs, outputs, and trace data for any kind of narrow AI application. It is most importantly a platform for integrating narrow AI techniques. (If you read any of the official documentation, you'll find most of it covers the specific narrow AI components they've selected, and the specific interconnect networks they are deploying. But those are secondary details to the more important contribution: the universal hypergraph language of the atomspace.)
So when you say:
I am not familiar with OpenCog, but I do not see how it can address these sorts of issues.
It doesn't really make sense. OpenCog solves these issues in the same way: through traditional text parsing and logical inference techniques. What's different is that the inputs, outputs, and the way in which these components are used are fully specified inside of the system, in a data structure that is self-modifying. Think LISP: code is data (albeit using a weird hypergraph language instead of s-expressions), data is code, and the machine has access to its own source code.
That's mostly what AGI is about: the interconnects and reflection layers which allow an otherwise traditional narrow AI program to modify itself in order to adapt to circumstances outside of its programmed expertise.
Personal opinion: OpenCog is attempting to get as general as it can within the logic-and-discrete-maths framework of Narrow AI. They are going to hit a wall as they try to connect their current video-game like environment to the real world, and find that they failed to integrate probabilistic approaches reasonably well. Also, without probabilistic approaches, you can't get around Rice's Theorem to build a self-improving agent.
Wellll.... the agent could make "narrow" self-improvements. It could build a formal specification for a few of its comp...
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.