The algorithms that we use today for speech recognition, for natural language translation, for chess playing, for logistics planning, have evolved remarkably in the past decade. It’s difficult to quantify the improvement, though, because it is as much in the realm of quality as of execution time.
So… speech recognition has plateaued for the last decade. (Quality is easily quantifiable by error rate.) I don’t know about the others, though I hear there is improvement in Go-playing algorithms.
For speech recognition recent progress has varied by problem. Speech recognition on a conversation with multiple speakers and background noise has not made good progress recently, but restricted conditions (e.g. one speaker doing voice dictation or interacting with a computational agent) have shown good progress, e.g. Dragon NaturallySpeaking.
So… speech recognition has plateaued for the last decade. (Quality is easily quantifiable by error rate.) I don’t know about the others, though I hear there is improvement in Go-playing algorithms.
For speech recognition recent progress has varied by problem. Speech recognition on a conversation with multiple speakers and background noise has not made good progress recently, but restricted conditions (e.g. one speaker doing voice dictation or interacting with a computational agent) have shown good progress, e.g. Dragon NaturallySpeaking.