I've been reflecting on exponentials and humans in light of coronavirus.
AlphaGo showed the same dynamic: I don't know if everyone remembers, but before the Lee Sedol tournament, most 'sensible' 'respectable' people assumed that there was no way AlphaGo would win, any claims it would stomp Lee Sedol were hyperbolic AI alarmism, and the only question was whether DeepMind would be saved from acute embarrassment by eking out one victory over Lee Sedol or if he would simply clean the board with AG. And in the absolute worst-case scenario, it might be a fairly even match. After all, anyone could look at the Fan Hui vs AG games which were released a few months before and see that AG was really crappy, hardly even human pro level; Lee Sedol wouldn't break a sweat. Yes, it would improve if they trained it more and tweaked it more, but so what? There were so many ELO points between Fan Hui and Lee Sedol, and AIs lack human intuition. Just drawing a line is mindless curve-fitting, which sensible respectable people know better than to do.
Meanwhile, people like Eliezer Yudkowsky considered the training time and thought through the exponential graph, and concluded that, given all those months between Fan Hui and the tournament and the fact that DM was willing to do the tournament at all with so much money on the lines, AG would either lose terribly or could well go 5-0. As it happens, his prediction was wrong. It actually went 4-1, because Lee Sedol got lucky and accidentally triggered a 'delusion' (fixed in AlphaZero).
We went from 'the best MCTS might be somewhat competitive with a very low-ranked human pro head-on' to 'superhuman' in a matter of months, because AG performance scaled with compute. We've seen similar capability leaps with DoTA2 or SC2: the best non-cheating AI was so low level it basically didn't exist (DoTA2) or was nowhere near competitive with any pros (SC2) and then within a few months of finding a working architecture (compare Oriol Vinyal's demo at Blizzcon to the matches just a few months later with SC2 pros), they reach better-than-most-pro levels even if they are not strictly superhuman yet.
That's all fun and games when it's just board games or computer games, of course...
That wasn't quite the dynamic; the version that played against Fan Hui was pretty good. That would have been more than enough to claim that the AI was highly competitive against human professionals.
The surprise was that it was assumed that Neural Nets would scale exponentially like other computer projects - over the medium term, with a requirement to rewrite the program to take advantage of new hardware opportunities. The idea that the same program + 6 months could scale exponentially was pretty radical even to people who knew a lot about computers. I was certainly surprised because I'd made the assumption that AlphaGo could scale exponentially with improvements in graphics cards as opposed to exponentially with Google's commitment of computer hours. Obviously wrong in hindsight, but it wasn't a misunderstanding of exponentials, it was a misunderstanding of neural nets.
And we only really had one data point at the time, which was the Fan games. Couldn't even really draw a line without knowing a lot about the field.
AlphaGo showed the same dynamic: I don't know if everyone remembers, but before the Lee Sedol tournament, most 'sensible' 'respectable' people assumed that there was no way AlphaGo would win, any claims it would stomp Lee Sedol were hyperbolic AI alarmism, and the only question was whether DeepMind would be saved from acute embarrassment by eking out one victory over Lee Sedol or if he would simply clean the board with AG. And in the absolute worst-case scenario, it might be a fairly even match. After all, anyone could look at the Fan Hui vs AG games which were released a few months before and see that AG was really crappy, hardly even human pro level; Lee Sedol wouldn't break a sweat. Yes, it would improve if they trained it more and tweaked it more, but so what? There were so many ELO points between Fan Hui and Lee Sedol, and AIs lack human intuition. Just drawing a line is mindless curve-fitting, which sensible respectable people know better than to do.
Meanwhile, people like Eliezer Yudkowsky considered the training time and thought through the exponential graph, and concluded that, given all those months between Fan Hui and the tournament and the fact that DM was willing to do the tournament at all with so much money on the lines, AG would either lose terribly or could well go 5-0. As it happens, his prediction was wrong. It actually went 4-1, because Lee Sedol got lucky and accidentally triggered a 'delusion' (fixed in AlphaZero).
We went from 'the best MCTS might be somewhat competitive with a very low-ranked human pro head-on' to 'superhuman' in a matter of months, because AG performance scaled with compute. We've seen similar capability leaps with DoTA2 or SC2: the best non-cheating AI was so low level it basically didn't exist (DoTA2) or was nowhere near competitive with any pros (SC2) and then within a few months of finding a working architecture (compare Oriol Vinyal's demo at Blizzcon to the matches just a few months later with SC2 pros), they reach better-than-most-pro levels even if they are not strictly superhuman yet.
That's all fun and games when it's just board games or computer games, of course...