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Erik Brynjolfsson (@erikbryn) is the director of MIT’s Initiative on the Digital Economy, the Schussel Family Professor of Management Science at the MIT Sloan School of Management, and a research associate at NBER.
His research examines the effects of information technologies on business strategy, productivity and performance, digital commerce, and intangible assets.
Mc Afee was educated at Harvard and MIT, where he is a cofounder of the institute’s Initiative on the Digital Economy.
Like so many other new technologies, however, AI has generated lots of unrealistic expectations.
Vision systems, such as those used in self-driving cars, formerly made a mistake when identifying a pedestrian as often as once in 30 frames (the cameras in these systems record about 30 frames a second); now they err less often than once in 30 million frames.
The error rate for recognizing images from a large database called Image Net, with several million photographs of common, obscure, or downright weird images, fell from higher than 30% in 2010 to about 4% in 2016 for the best systems. ”) The speed of improvement has accelerated rapidly in recent years as a new approach, based on very large or “deep” neural nets, was adopted.
Image recognition is even replacing ID cards at corporate headquarters.
This article will cut through the noise to describe the real potential of AI, its practical implications, and the barriers to its adoption.
The term was coined in 1955 by John Mc Carthy, a math professor at Dartmouth who organized the seminal conference on the topic the following year.
We see business plans liberally sprinkled with references to machine learning, neural nets, and other forms of the technology, with little connection to its real capabilities.
Simply calling a dating site “AI-powered,” for example, doesn’t make it any more effective, but it might help with fundraising.