They’ve learned within months for certain problems where learning can be done at machine speeds, ie game-like problems where it can “play against itself” or problems where huge amounts of data are available in machine-friendly format. But that isn’t the case for every application. For example, developing self driving cars up to perfection level has taken way, way longer than expected, partially because it has to deal with freak events that are outside the norm, so a lot more experience and data has to be built up, which takes human time. (of course, humans are also not great at freak events, but remember we’re aiming for perfection here). I think most tasks involved in taking over the world will look a lot more like self-driving cars than playing Go, which inevitably means mistakes, and a lot of them.
They’ve learned within months for certain problems where learning can be done at machine speeds, ie game-like problems where it can “play against itself” or problems where huge amounts of data are available in machine-friendly format. But that isn’t the case for every application. For example, developing self driving cars up to perfection level has taken way, way longer than expected, partially because it has to deal with freak events that are outside the norm, so a lot more experience and data has to be built up, which takes human time. (of course, humans are also not great at freak events, but remember we’re aiming for perfection here). I think most tasks involved in taking over the world will look a lot more like self-driving cars than playing Go, which inevitably means mistakes, and a lot of them.