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January 2, 2018
Technological progress is going to leave behind some people, perhaps even a lot of people, as it races ahead. As we’ll demonstrate, there’s never been a better time to be a worker with special skills or the right education, because these people can use technology to create and capture value. However, there’s never been a worse time to be a worker with only ‘ordinary’ skills and abilities to offer, because computers, robots, and other digital technologies are acquiring these skills and abilities at an extraordinary rate.
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Spread, however, is not so great; it’s ever-bigger differences among people in economic success—in wealth, income, mobility, and other important measures.
The car recognized all the surrounding vehicles, not just the nearest ones, and it remained aware of them no matter where they moved. It was a car without blind spots. But the software doing the driving was aware that cars and trucks driven by humans do have blind spots. The laptop screen displayed the software’s best guess about where all these blind spots were and worked to stay out of them.
Our ride that day on the 101 was especially weird for us because, only a few years earlier, we were sure that computers would not be able to drive cars. Excellent research and analysis, conducted by colleagues who we respect a great deal, concluded that driving would remain a human task for the foreseeable future. How they reached this conclusion, and how technologies like Chauffeur started to overturn it in just a few years, offers important lessons about digital progress.
In any sensible economic system, people should focus on the tasks and jobs where they have a comparative advantage over computers, leaving computers the work for which they are better suited.
Our brains are extraordinarily good at taking in information via our senses and examining it for patterns, but we’re quite bad at describing or figuring out how we’re doing it, especially when a large volume of fast-changing information arrives at a rapid pace.
“We know more than we can tell.”
A 2004 review of the previous half-century’s research in automatic speech recognition (a critical part of natural language processing) opened with the admission that “Human-level speech recognition has proved to be an elusive goal,”
“We’re at the beginning of a ten-year period where we’re going to transition from computers that can’t understand language to a point where computers can understand quite a bit about language.”
“The main lesson of thirty-five years of AI research is that the hard problems are easy and the easy problems are hard.
The digital progress we’ve seen recently is certainly impressive, but it’s just a small indication of what’s to come. It’s the dawn of the second machine age. To understand why it’s unfolding now, we need to understand the nature of technological progress in the era of digital hardware, software, and networks.
Logarithmic graphs have a wonderful property: they show exponential growth as a perfectly straight line.
In short, sensing a sizable area and immediately crunching all the resulting data were thorny problems preventing real progress with SLAM. Until, that is, a $150 video-game accessory came along just two years after the sentences above were published.
Some early commercial LIDAR systems available around the year 2000 cost up to $35 million, but in mid-2013 Velodyne’s assembly for self-navigating vehicles was priced at approximately $80,000,
“Information is costly to produce but cheap to reproduce.”
“Productivity isn’t everything, but in the long run it is almost everything.”
“Innovation is the outstanding fact in the economic history of capitalist society . . . and also it is largely responsible for most of what we would at first sight attribute to other factors.”
All that remained after 1970 were second-round improvements,
“Somehow, I thought, it had to be an illusion. . . . It was too easy. . . . There was not a single unknown in the scheme. Every step involved had been done already.”
Economic growth occurs whenever people take resources and rearrange them in ways that make them more valuable.
digital innovation is recombinant innovation in its purest form.
And in the second competition, none of the top three finishers had any formal training in artificial intelligence beyond a free online course offered by Stanford AI faculty and open to anyone in the world who wanted to take it.
“It is your mind that matters economically, as much or more than your mouth or hands. In the long run, the most important economic effect of population size and growth is the contribution of additional people to our stock of useful knowledge. And this contribution is large enough in the long run to overcome all the costs of population growth.”
The main impediment to progress has been that, until quite recently, a sizable portion of the world’s people had no effective way to access the world’s stock of knowledge or to add to it.
Fish prices stabilized immediately after phones were introduced, and even though these prices dropped on average, fishermen’s profits actually increased because they were able to eliminate the waste that occurred when they took their fish to markets that already had enough supply for the day. The
“Freedom of the press is limited to those who own one.”
“Most economic fallacies derive from the tendency to assume that there is a fixed pie, that one party can gain only at the expense of another.” —Milton Friedman
