Much of the current excitement concerns a subfield of it called “deep learning”, a modern refinement of “machine learning”, in which computers teach themselves tasks by crunching large sets of data. Algorithms created in this manner are a way of bridging a gap that bedevils all AI research: by and large, tasks that are hard for humans are easy for computers, and vice versa. The simplest computer can run rings around the brightest person when it comes to wading through complicated mathematical equations. At the same time, the most powerful computers have, in the past, struggled with things that people find trivial, such as recognising faces, decoding speech and identifying objects in images.
In 2014 Facebook unveiled an algorithm called DeepFace that can recognise specific human faces in images around 97% of the time, even when those faces are partly hidden or poorly lit. That is on a par with what people can do. Microsoft likes to boast that the object-recognition software it is developing for Cortana, a digital personal assistant, can tell its users the difference between a picture of a Pembroke Welsh Corgi and a Cardigan Welsh Corgi.