How do Spaun's neural nets compare with the type of HHMMs in Kurzweil's book (in terms of capability)?
[1] - http://www.newyorker.com/online/blogs/books/2012/11/ray-kurz...
My guess is that maybe some of the more biologically accurate spiking neural network simulations are more capable than the more typical neural nets that Kurzweil dismisses, but also less efficient than hierarchical hidden Markov models.
When I Google hierarchical hidden Markov models I see it being used in quite a lot of current research. I also see neural nets being mentioned, sometimes in the same project.
What research would you recommend?
The New Yorker apparently hired this guy to be an AI columnist, and he has no background in either neuroscience or computer science, only psychology.
If you read his articles, he veers wildly from "strong AI ain't gonna happen" to "AI could destroy us all", the only common thread being pessimism. Meanwhile, he displays a frightening lack of understanding of the subject matter, and perhaps most disturbingly, in some of his more alarmist articles advocates that computer scientists should step aside and make room for psychologists, philosophers, lawyers, and politicians to sort out these thorny issues at the big boy table.
Kurzweil is certainly fair game for legitimate criticism, but Marcus calling Kurzweil a joke is an even big joke in itself.
First, note that most of the stuff you read on HN about NNs is about practical applications. Resent research in NNs has lead to good results on hard problems, such as object and speech recognition. Nobody is claiming that these types of neural works are actually a good (low-level) model for how the brain works, they just give empirically good results on some tasks.
If you aim is making a model for how the brain works, note that all models are just that: models. Different models can be good at modeling different aspects of how the brain works.