The New World of AI-Enabled Work

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The New World of AI-Enabled Work

As we discussed a few issues ago in our analysis of the trend Harnessing AI-Driven Growth, artificial intelligence will transform one industry after another over the coming decade, spurring economic growth in the United States and throughout the world.1  According to a study by the Accenture Institute for High Performance, AI technologies will increase labor productivity by as much as 40 percent, while doubling annual economic growth rates for most countries by 2035.2

How is this possible?  To understand AI’s potential, we only have to look at how another technology (computing) increased productivity and unleashed economic growth.  As Ajay Agrawal, Joshua S. Gans, and Avi Goldfarb of the University of Toronto’s Rotman School of Management explained recently in MIT Sloan Management Review, the semiconductor revolution reduced the cost of arithmetic

Half a century ago, it would take hundreds of high-salaried engineers using slide rules and calculators several months to solve problems that a computer can solve in an instant today.  In fact, everything that computers enable us to do today, from streaming music to designing spacecraft, is based on the ability of machines to process information and make calculations at incredible speed and minimal cost.

Similarly, artificial intelligence is turning another capability that has until now been expensive into one that will be cheap, and therefore abundant.  That capability is prediction.  Agrawal and his colleagues define prediction as “the ability to take information you have and generate information you previously didn’t have.”

The ability of machines to predict what will happen is the driving force behind autonomous vehicles, image recognition systems, and language translation services, as well as countless applications that haven’t been imagined yet.

The aspect of AI that makes prediction possible is known as “machine learning.”  Machines are said to “learn” when programmers feed them millions of inputs so they can establish rules and recognize patterns.  For example, after processing a multitude of images of basketballs, a computer will learn that basketballs are orange and round. 

But to distinguish a basketball from other round orange objects, such as orange fruit or the sun, the machine learns to use context in order to improve its accuracy.  For instance, it learns that a round orange object that is pictured near a basketball hoop is likely to be a basketball, while a round orange object that is shown in a bowl next to a banana is not.

In this way, the computer “predicts” whether a round orange object is a basketball, which is useful for image recognition systems...

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