zlacker

>>treebr+(OP)
A really simple approach we took while I was working on a research team at Microsoft for predicting when AGI would land was simply estimating at what point can we run a full simulation of all of the chemical processes and synapses inside a human brain.

The approach was tremendously simple and totally naive, but it was still interesting. At the time a supercomputer could simulate the full brain of a flatworm. We then simply applied a Moore's law-esque approach of assuming simulation capacity can double every 1.5-2 years (I forget the time period we used), and mapped out different animals that we had the capability to simulate on each date. We showed years for a field mouse, a corvid, a chimp, and eventually a human brain. The date we landed on was 2047.

There are so many things wrong with that approach I can't even count, but I'd be kinda smitten if it ended up being correct.

>>jjcm+ne
I assume simulation capacity takes into account the data bandwidth of the processing systems. It seems we are always an orders of magnitude or two behind in bytes/words per second to feed simulations compared to raw flops. When you consider there are multiple orders of magnitude more synapses between neurons than neurons (not to mention other cell types we are only beginning to understand) -- bandwidth limitations seem to put estimates about 10-15 years past computation estimates. By my napkin math, accounting for bandwidth limitations, we will get single-human-intelligence hardware capabilities 2053-2063. Whether or not we've figured out the algorithms by then is any guess. Maybe algorithm advances will reduce hardware needs, but I doubt it because computational complexity to solve hard problems is often a matter of getting all the bits to the processor to perform all the comparisons necessary. However, the massive parallelism of the brain is a point of optimism.