“Breakthroughs” are not really how synthetic intelligence has progressed so far. Look at speech recognition, for example. So far, that has mostly been a long, gradual slog. Maybe we are doing it wrong—and there is an easier way. However, that’s not an isolated example—and if there are easier ways, we don’t seem to be very good at finding them.
The idea of a “breakthrough” denotes a sudden leap forwards. There have been some of those.
One might cite back propagation, for example—but big breakthroughs seem rare, and most progress seems attributable to other factors—much as Robin Hanson claims happens in general: “in large systems most innovation value comes from many small innovations”.
“Breakthroughs” are not really how synthetic intelligence has progressed so far. Look at speech recognition, for example. So far, that has mostly been a long, gradual slog. Maybe we are doing it wrong—and there is an easier way. However, that’s not an isolated example—and if there are easier ways, we don’t seem to be very good at finding them.
Of course, “breakthroughs” is a cumulative impression: now you don’t know how to solve the problem or even how to state it, and 10 years later you do.
The idea of a “breakthrough” denotes a sudden leap forwards. There have been some of those.
One might cite back propagation, for example—but big breakthroughs seem rare, and most progress seems attributable to other factors—much as Robin Hanson claims happens in general: “in large systems most innovation value comes from many small innovations”.