My understanding is that it's mostly (all?) by himself, with $20M, anticipating ~60% chance by 2030, and by applying old methods that didn't work with ~1MHz com

My understanding is that it's mostly (all?) by himself, with $20M, anticipating ~60% chance by 2030, and by applying old methods that didn't work with ~1MHz computers but that might work with modern desktop computers. A lot of my own thinking mirrors some similar ideas. That is to say, the idea that some older AI approaches that "didn't work" Back In The Day may more accurately be said to have worked, but "didn't scale." Given the massive advances in hardware, computing architectures, parallel computing, etc. since then, I think there may be some value in revisiting some of this stuff and examining it under a more modern light.