The cost of training machines is becoming a problem

THE FUNDAMENTAL assumption of the computing industry is that number-crunching gets cheaper all the time. Moore's law, the industry's master metronome, predicts that the number of components that can be squeezed onto a microchip of a given size--and thus, loosely, the amount of computational power available at a given cost--doubles every two years. For many comparatively simple AI applications, that means that the cost of training a computer is falling, says Christopher Manning, an associate director of the Institute for Human-Centered AI at the University of Stanford. But that is not true everywhere. A combination of ballooning complexity and competition means costs at the cutting edge are rising sharply.