I’d say 20 total device-months is a bit optimistic, but maybe they will hit it, and even if they are anywhere close it will be impressive.
20 device-months for 1 mm^3 compared with 360 device-months with the devices I studied in 2012-14 is impressive. I hope they do it!
FWIW, my belief is that this line of research is probably more promising for strong AI than straight development of AI from e.g. meta-reinforcement learning, although in the end it probably will be a mix of these things.
: < https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5476429/#!po=3.... >
With 10,000 devices running in parallel, and assuming no failure rate (though with a device count this high, failures would happen constantly), that would still require 2000 months (or about 167 years) to image a whole brain.
Let's imagine the technology can undergo some type of Moore's Law (I don't know enough about the underlying SEM physics to know whether there is a clear ceiling on speedups achievable) and the time to image 1 cubic mm halves every two years. This might predict that in ~20 years, we could image a whole brain in about 4 months time (still requiring 10,000 parallel devices).
If you keep going past 20 years and continue the trend, but assume you would halve the device count but keep the 4-month timeline the same, then it would be about 42 years before a set of 5 devices could image the whole brain in 4 months time, or going on ~50 years until 5 devices could do it in less than one month, ~60 years before one device can do it in less than one month. Obviously, hugely gigantic error bars around such estimates.