Facebook:
https://www.facebook.com/yudkowsky?pnref=storyEliezer Yudkowsky on the Go AI that beat the European champion player:
> People occasionally ask me about signs that the remaining timeline [to general AI] might be short. It's *very* easy for nonprofessionals to take too much alarm too easily. Deep Blue beating Kasparov at chess was *not* such a sign. Robotic cars are *not* such a sign.
> This is. [...]
> As the authors observe, this represents a break of at least one decade faster than trend in computer Go.
> This matches something I've previously named in private conversation as a warning sign - sharply above-trend performance at Go from a neural algorithm. What this indicates is not that deep learning in particular is going to be the Game Over algorithm. Rather, the background variables are looking more like "Human neural intelligence is not that complicated and current algorithms are touching on keystone, foundational aspects of it." What's alarming is not this particular breakthrough, but what it implies about the general background settings of the computational universe.
> To try spelling out the details more explicitly, Go is a game that is very computationally difficult for traditional chess-style techniques. Human masters learn to play Go very intuitively, because the human cortical algorithm turns out to generalize well. If deep learning can do something similar, *plus* (a previous real sign) have a single network architecture learn to play loads of different old computer games, that may indicate we're starting to get into the range of "neural algorithms that generalize well, the way that the human cortical algorithm generalizes well".
> This result also supports that "Everything always stays on a smooth exponential trend, you don't get discontinuous competence boosts from new algorithmic insights" is false even for the non-recursive case, but that was already obvious from my perspective. Evidence that's more easily interpreted by a wider set of eyes is always helpful, I guess.
> Next sign up might be, e.g., a similar discontinuous jump in machine programming ability - not to human level, but to doing things previously considered impossibly difficult for AI algorithms.
I hope that everyone in 2005 who tried to eyeball the AI alignment problem, and concluded with their own eyeballs that we had until 2050 to start really worrying about it, enjoyed their use of whatever resources they decided not to devote to the problem at that time.