As one of the more recent, visible efforts at general AI, OpenCog is automatically worth
some attention (in fact, you've given it some..).
I agree that Open cog will fail but I think it's failure should be looked at.
"if we glue together enough parts that intuitively seem relevant to AGI, maybe AGI will pop out!" may sound dumb but sooner or later, an "intelligence" program needs to reach a boot-strapping stage so it is actually credible that there is some algorithm with the properties that if you add enough processing power to it, intelligence will indeed "pop" out.
Many believe the Mammalian neocortex essentially involves such a generic algorithm. But the problem is there are fairly good arguments that the kinds of algorithms we've tried so-far could include not this algorithm. They are not algorithms that can reach the "critical mass" level.
The first failure of AI was based on the now clearly-false belief that intelligent behavior was essentially logical deduction - these efforts main appeared in the 1970's. Shredlu was the most successful.
The second failure of AI seems to be the belief that intelligent behavior involves primarily something like statistical inference. Narrow "machine learning", OpenCog, and Jeff Hawkins stuff all seem to fall out of this later approach.
My sense is that the world humans live in is too chaotic for pure logic yet much more orderly than phenemena suited to pure statistical inference ("machine learning" can work but its failure is obvious in excessive time it take make "obvious" (to us) inferences). The most uniquely human intelligence involves neither making complex deductions nor filtering scattered and chaotic masses of data but rather putting "semi-structured" data together in a "common sense" fashion. Humans follow patterns immediately, not after seeing them over-and-over again. Humans are "terrible" at pure calculation but also "terrible" at pure inference problems like gambling games (and the way human fail at gambling is very instructive - we tend to see too many patterns in random data. )
The challenge is how to create a "third way" beyond these two failed approaches (keeping in mind that both approaches can have successes too).
I'll leave my grandiose announcement of this algorithm for a different post.