I'm a computer scientist who has been in a machine learning and natural language processing PhD program quite recently. I have an in-depth knowledge of machine learning, NLP and text mining.
In particular, I know that the broadest existing knowledge bases in the real-world (e.g. Google's knowledge Graph) are built on a hodge-podge of text parsing and logical inference techniques. These systems can be huge in scale and very useful, and reveal that a lot of knowledge is quite shallow even if it is apparently deeper, but also reveal the difficulty in dealing with knowledge that genuinely is deeper, by which I mean it relies on complex models of he world.
I am not familiar with OpenCog, but I do not see how it can address these sorts of issues.
The pitfall with private research is that nobody sees your work, meaning there's nobody to criticize it or tell you your assessment "the issues are solvable or solved but not yet integrated" is incorrect. Or, if it is correct and I'm dead wrong in my pessimism, nobody can know that either. Why would publishing it be dangerous (yeah, I get the general "AGI can be dangerous" thing, but what would be the actual marginal danger vs. not publishing and being left out of important conversations when they happen, assuming you've got something)?
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 thos...
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.