LOOKING backward, it is clear that American productivity growth has been pretty disappointing over the past 40 years, with the exception of an IT boomlet lasting from about 1996 to 2004. But past performance need not imply continued disappointment in future. The impressive gathering of innovative momentum in recent years is extraordinarily promising. And we are beginning to get a glimpse of the productivity potential of machine intelligence. A recent, fascinating Wired piece begins:
It was one of the most tedious jobs on the internet. A team of Googlers would spend day after day staring at computer screens, scrutinizing tiny snippets of street photographs, asking themselves the same question over and over again: “Am I looking at an address or not?’ Click. Yes. Click. Yes. Click. No.
This was a critical part of building the company’s Google Maps service. Knowing the precise address of a building is really helpful information for mapmakers. But that didn’t make life any easier for those poor Googlers who had to figure out whether a string of numbers captured by Google’s roving Street View cars was a phone number, a graffiti tag, or a legitimate address.
Then, a few months ago, they were relieved of their agony, after some Google engineers trained the company’s machines to handle this thankless task. Traditionally, computers have muffed this advanced kind of image recognition, and Google finally cracked the problem with its new artificial intelligence system, known as Google Brain. With Brain, Google can now transcribe all of the addresses that Street View has captured in France in less than an hour.
As is explained later, it takes a high level of expertise and experience to advance the science of machine learning, but applying the machine learning techniques Google has already developed to new problems—recognising other images, understanding or even translating speech, deriving meaningful relationships from large datasets (in web search, for instance, but also in academic research)—is relatively straightforward.
The IT productivity boom emerged in a particular way. Initial gains from IT were simply too small to affect productivity statistics, because the affected part of the economy was simply too small. When the contribution became large enough to drive faster national productivity growth it was initially due to very rapid improvements within the IT sector itself. Then, by the early 2000s, productivity gains began to fan out to sectors and firms that merely relied on the new IT technologies.
It will take a while for recent innovations to retrace these steps. The story above suggests we are in the first phase, when very rapid productivity gains become possible in parts of the economy that are simply too small to affect the overall numbers. As Google and other companies (Google is not alone in developing machine intelligence projects) improve on these technologies and find new ways to use them, the tech sector should experience greater productivity gains over a greater range of businesses, potentially nudging measured productivity upward. And then, one suspects, the “general purpose” phase of machine-intelligence-driven productivity will commence, partly as the tech companies themselves eat more of the economy, and partly as their applications are sold to or used by firms in other parts of the economy.
But this strikes me as a hugely important development. People in occupations that span the skill spectrum devote large amounts of time to ridiculous little tasks that are trivially easy for people but nonetheless time consuming. Think paralegals or lawyers sifting through boxes full of documents or researchers fixing all the little irregularities in a dataset. Maybe the end of that sort of thing, and the beginning of a new round of faster productivity growth, is in sight.
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