Tomado de "The Economist", edición Online; 11 de marzo de 2016.
The first is software. This week AlphaGo, a program which plays the ancient game of Go, beat Lee Sedol, one of the best human players, in the first two of five games scheduled in Seoul. Go is of particular interest to computer scientists because of its complexity: there are more possible board positions than there are particles in the universe. As a result, a Go-playing system cannot simply rely on computational brute force, provided by Moore’s law, to prevail. AlphaGo relies instead on “deep learning” technology, modelled partly on the way the human brain works. Its success this week shows that huge performance gains can be achieved through new algorithms. Indeed, slowing progress in hardware will provide stronger incentives to develop cleverer software.
The second area of progress is in the “cloud”, the networks of data centres that deliver services over the internet. When computers were stand-alone devices, whether mainframes or desktop PCs, their performance depended above all on the speed of their processor chips. Today computers become more powerful without changes to their hardware. They can draw upon the vast (and flexible) number-crunching resources of the cloud when doing things like searching through e-mails or calculating the best route for a road trip. And interconnectedness adds to their capabilities: smartphone features such as satellite positioning, motion sensors and wireless-payment support now matter as much as processor speed.
The third area of improvement lies in new computing architectures—specialised chips optimised for particular jobs, say, and even exotic techniques that exploit quantum-mechanical weirdness to crunch multiple data sets simultaneously. There was less need to pursue these sorts of approaches when generic microprocessors were improving so rapidly, but chips are now being designed specifically for cloud computing, neural-network processing, computer vision and other tasks. Such specialised hardware will be embedded in the cloud, to be called upon when needed. Once again, that suggests the raw performance of end-user devices matters less than it did, because the heavy lifting is done elsewhere.
