A: JAY CORRIGAN | PROFESSOR OF ECONOMICS
Slices of the economic pie are more lopsided than ever before. According to recent data, the richest 10 percent of American households earn just more than half of all U.S. income. That’s the highest fraction since the federal government started keeping these sorts of records 100 years ago. And the U.S. isn’t the only place where the gap between rich and poor is growing. During the last 40 years, the richest 10 percent gained ground in Canada, Germany and Japan.
So what explains this increase in income inequality across rich countries? Economists most often point to technological changes that have made the most talented workers ever more productive. As an example, consider that the only way to listen to professional musicians at the turn of the 20th century was to go to a live performance. The most talented performers played in the largest venues and, therefore, made more money than their less-talented peers, but the difference would have been relatively modest.
Today, most of us listen to recorded music. And because an iTunes download costs the same whether it’s recorded by the top artist in a genre or by someone less popular, the most-talented performers now capture a much larger share of our entertainment dollars. Thousands of musicians still are scratching out a living, but technology has increased the gap between the most-talented and the slightly less-talented. Something similar has happened in most industries.
To understand what, if anything, can be done to reduce income inequality, it helps to look back to the 1940s, ’50s and ’60s, when income inequality actually decreased. That's because the supply of highly skilled workers increased more rapidly than the demand for their services, keeping their incomes — and income inequality — in check. This increase in supply was due to an increase in college graduates and women entering the workforce. Unfortunately, both of those trends have leveled off since about 1980.
If there’s one area where there’s still low-hanging fruit, it’s immigration. Immigrants create about half of all successful startups, but we make it hard for highly skilled immigrants to live and work in the U.S. Increasing the cap on the number of visas issued to highly skilled immigrants each year — or removing the cap entirely — would increase the supply of top talent, reducing income inequality.
KATHERINE ELKINS | Associate Professor of Comparative Literature and Humanities JON CHUN | Visiting Instructor of Humanities, Affiliated Scholar in Scientific Computing
When IBM Deep Blue defeated chess champion Garry Kasparov in 1997, The New York Times reported it would take 100 years before a computer could defeat a human Go master. Go is a vastly more complex game than chess that requires computers to rely on heuristic shortcuts that mimic human intuition.
Yet it was just 20 years later when AlphaGo twice defeated Lee Sedol, an 18-time world Go champion, and became the first computer to beat a top-ranked human in a Go match.
Astonishing advances like this have led to a global “arms race” in artificial intelligence, as companies compete to acquire top AI talent. Last year, China announced a multibillion-dollar initiative to become the world leader in AI and, recently, Russian President Vladimir Putin proclaimed, “Whoever becomes the leader in this sphere will become the ruler of the world.”
While AI experts work to develop smarter AI applications for all of us — from driverless cars to personal assistants — fewer have taken up the broader challenge to ensure we don’t become, in Henry David Thoreau’s words, “the tools of our tools.”
What we need now are humanists conversant in AI who can critique and shape the future that AI may restructure. After all, AI forces us to ask questions about what it means to be human. And answering these questions will, in the end, be more important than AI milestones like AlphaGo. The only way to answer these questions is to develop an understanding of the world that is both broad and deep, since these questions cannot be answered within any single discipline or major.
No one in 1997 could have predicted the advances in big data, computational power and algorithms that are making AI increasingly powerful and inexpensive. How, then, can we predict what AI will look like 20 years from now? Even the experts are poor at forecasting this future. But the rapid and revolutionary changes being brought on by AI compel us to continue putting the human at the center of our technological world.