Erik Brynjolfsson is the director of the MIT Initiative on the Digital Economy and co-author, with Andrew McAfee, of “Machine/Platform/Crowd.” Xiang Hui is an assistant professor of marketing at Washington University, where Meng Liu is a visiting assistant professor of marketing; both are research fellows at the MIT initiative.
After half a century of hype and false starts, artificial intelligence may finally be starting to transform the U.S. economy. An example is machine translation, as we found when analyzing eBay’s deployment in 2014 of an AI-based tool that learned to translate by digesting millions of lines of eBay data and data from the Web. The aim is to allow eBay sellers and buyers in different countries to more easily connect with one another. The tool detects the location of an eBay user’s Internet Protocol address in, say, a Spanish-speaking country and automatically translates the English title of the eBay offering.
After eBay unveiled its English-Spanish translator for search queries and item titles, exports on eBay from the United States to Latin America increased by more than 17 percent. Other language pairs produced similarly significant gains. But the machine-learning tool is imperfect — it doesn’t translate the entire description of an eBay offering. Refinements would almost certainly drive even larger increases.
The eBay machine-translation results show how two barriers to productivity improvement can be overcome. That’s a reason for optimism, but it also warrants a renewed effort to ensure that the economic gains from artificial intelligence are widely shared.
First, the reason for optimism: Language differences that throughout history have hindered trade are steadily going to become irrelevant as machine translation shrinks the world. Historically, countries separated by languages or by geographic distance engaged in less trade than countries without those hurdles. Our research found that the increased trade enabled by machine translation was the equivalent of cutting the distance between countries by 37 percent.
In addition to lowering the language barrier to trade, machine learning will overcome the historic barrier know as Polanyi’s Paradox: “We know more than we can tell.” Bilingual people switch almost seamlessly between languages, translating almost as a reflex, but they’ve been unable to effectively explain to computers how to do it. Unlike previous information technologies that required humans to explicitly codify tasks for computers, machine learning is designed essentially to teach itself by automatically studying millions of examples, such as pairs of corresponding English and Spanish sentences.
The machine-learning approach has proven remarkably powerful, not only for language translation but also for speech recognition (Apple’s Siri), facial recognition (Facebook’s photo tagging), product recommendations (Amazon) and even cancer diagnoses.
Machine translation is of course just part of a broader technological revolution that is on the cusp of transforming industries and professions. Bigger changes will come with smarter algorithms and faster computers. As companies adopt these technologies, we can expect a productivity boom.
Surging productivity and the general rise in incomes it brings would be welcome, of course, but that isn’t sufficient. The same questions being raised about the advance of robotics in the workplace apply to machine learning. While new jobs would be created, many existing jobs — from doctors and financial advisers to translators and call-center operators — are susceptible to displacement or much-reduced roles. No economic law guarantees that productivity growth benefits everyone equally. Unless we thoughtfully manage the transition, some people, even a majority, are vulnerable to being left behind even as others reap billions.
Shared prosperity depends less on technology itself than on the choices made by each of us, as workers and entrepreneurs, and as citizens and voters. Entrepreneurs need to invent new business models, workers need access to new skills, and policymakers need to be urged by voters to invest in research that will redesign approaches to human learning for an era of machine learning.
At the Massachusetts Institute of Technology, the Inclusive Innovation Challenge was inaugurated three years ago to help speed the transition to a high-growth and high-opportunity digital economy. More than $1 million is awarded annually to recognize and reward people and organizations that are working toward the goal of more widely shared participation in the digital economy.
Award recipients include Laboratoria, a six-month coding boot camp that trains low-income women in both technical skills and soft skills such as teamwork and collaboration. After graduation, 80 percent of Laboratoria’s students find jobs that pay three times what they earned before the program. Another recipient, Apli, uses an AI-enabled chatbot to “interview” students, single mothers, shift workers and other job-seekers, then uses machine learning to match them with employment opportunities. This approach connects them with jobs within 24 hours rather than the 52-day average recruiting cycle.
Artificial intelligence is beginning to transform the economy. Human intelligence is needed to make sure it benefits the many, not just the few.