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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.
At MIT he teaches courses on the economics of information and the Analytics Lab.
Brynjolfsson was among the first researchers to measure IT’s productivity contributions and the complementary role of organizational capital and other intangibles.
Let’s start by exploring what AI is already doing and how quickly it is improving.
The biggest advances have been in two broad areas: perception and cognition.
In the former category some of the most practical advances have been made in relation to speech.
The most important of these are what economists call general-purpose technologies — a category that includes the steam engine, electricity, and the internal combustion engine.
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.
Within just the past few years machine learning has become far more effective and widely available. First, we humans know more than we can tell: We can’t explain exactly how we’re able to do a lot of things — from recognizing a face to making a smart move in the ancient Asian strategy game of Go. They can achieve superhuman performance in a wide range of activities, including detecting fraud and diagnosing disease.
We can now build systems that learn how to perform tasks on their own. Prior to ML, this inability to articulate our own knowledge meant that we couldn’t automate many tasks. Excellent digital learners are being deployed across the economy, and their impact will be profound.