Government indifference toward AI could let the US lose ground to rival countries. But what would a good AI plan actually look like?
Politicians worldwide are stealing one of the US government’s best ideas by drawing up ambitious plans to make the most of advances in artificial intelligence.
These AI manifestos, penned in Paris, Beijing, and elsewhere, follow the example of the Obama administration, which released a report on the technology toward the end of its tenure. This report did not include funding, but it made it clear that AI should be a key focus of government strategy.
The Trump administration has abandoned this vision and has no intention of devising its own AI plan, say those working there. They say there is no need for an AI moonshot, and that minimizing government interference is the best way to make sure the technology flourishes.
That looks like a huge mistake. If it essentially ignores such a technological transformation, the US might never make the most of an opportunity to reboot its economy and kick-start both wage growth and job creation. Failure to plan could also cause the birthplace of AI to lose ground to international rivals.
France is the latest country to bet heavily on AI. Last week, President Emmanuel Macron announced an AI plan that includes $1.6 billion in funding, new research centers, data-sharing initiatives, and ethical guidelines. Macron also echoed a view held by many economists and policy experts by suggesting that the technology could boost overall economic productivity and lead to wage growth and job creation.
The most notable national AI plan has come from China, which last year announced a plan to dominate the industry by 2030 (see “China’s AI awakening”). Details of that undertaking are murky, but many billions of dollars will be fed into the industry. And experts there are also debating how the technology might affect the economy and society.
Other countries have also recognized the technology’s potential. The UK government has published several major studies on AI, and last year it committed $663 million to tech funding, much of which has been earmarked for AI. Canada has created several new AI institutes and introduced incentives to attract companies working on the technology. Germany is said to be preparing its own AI strategy.
If the US were to draw up a new AI master plan, what should it aim to do?
Funding AI research is the biggest priority, says Jason Furman, one of Obama’s top economic advisors and author of the 2016 AI report. Furman says basic research requires government backing and that advances made privately won’t benefit the country as a whole.
Furman adds that it is especially important to promote AI because it could provide precisely the economic boost that leads to wage growth and new employment opportunities. “Economists have found, in general, that we may be spending about a quarter of what we should be, based on the returns,” he says.
Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence, a nonprofit based in Seattle, points out that core AI breakthroughs have their origins in academia. “If you don’t fund the universities, you run the risk of starving the goose that lays the golden egg,” he says. “We are at a momentous point in history.”
Prepare for job losses
But a government AI policy must go beyond calls for research funding.
While AI can drive economic growth, it may also accelerate the eradication of some occupations, transform the nature of work in other jobs, and exacerbate economic inequality (see “The relentless pace of automation”). It is critical that governments prepare for this transformation. This might mean exploring ways to find training and employment opportunities for those who have lost jobs to automation and AI. Academic experts and institutions have been sounding the alarm over this issue and have warned that it might have serious social consequences. Those problems will surely require government action.
“Planning for job displacement that AI will cause is best done by government,” says Andrew Ng, a prominent AI researcher who was previously the chief scientist at Baidu and is now involved with several different AI projects.
Another key consideration should be attracting and holding onto talented AI experts. The US has a long history of drawing the top academic talent, and this has fed directly into industry. The academics most associated with the field of deep learning, for example—Geoffrey Hinton, Yann LeCun, Yoshua Bengio, and Andrew Ng—were all born in other countries but came to universities and companies in the US.
Other countries now aim to retain or recapture talent. The UK government commissioned an AI report not long after Google acquired DeepMind, a UK startup that has made fundamental AI advances, in 2014. “[The government] said we want to make sure the UK is well positioned to have jobs in AI, and we want to keep our startups in the UK,” says Dame Wendy Hall, a professor at Southampton University and the author of the report. It recommends, for instance, measures that would help startups access more data and funding, perhaps making them less likely to agree to an acquisition.
The US government is currently on the opposite path. By tightening its visa program, taking an aggressive stance on immigration, and threatening funding for graduate students, it is turning talent away (see “The US leads in artificial intelligence, but for how long?”). “The openness of our society is a huge advantage,” Etzioni adds. “If we lose our openness on immigration, we are putting a nail in our scientific coffin.”
Education should be a key part of the picture. New AI scientists will fuel the industry, but broader AI expertise across different industries is also an important goal.
Tess Posner, who leads the nonprofit AI4All, says beyond boosting the industry, education can help address the its shortcomings.
“There is not only a talent crisis in AI; there is a diversity crisis, and that is problematic,” she says.
Posner suggests that the government should not be responsible for educating AI experts, but it should be at the forefront of the effort. “It’s such an important tech that there should be a national focus on it,” she says.
Again, other countries are stealing a march here. The UK has, for example, launched new university courses focused on AI and added funding for doctoral students at top universities.
The US doesn’t necessarily need new rules and regulations for AI, but there may be situations where the government could step in to help apply existing laws. In the case of autonomous cars, for example, adapting existing regulations had been an effective approach. Recent accidents involving self-driving vehicles are being investigated by federal agencies to make sure no rules were broken.
The same is true in in dealing with algorithmic bias, says Solon Barocas, an assistant professor at Cornell University and an expert on fairness in machine learning. New York City has launched an effort to make algorithmic decision-making more accountable using the existing regulatory framework.
But Barocas says regulators may need guidance in figuring out how to apply current laws. “There are limits to how far you can go with existing regulations,” he says. “There needs to be a lot more work done to help regulators.”
It may also be important to consider new challenges that might arise as a result of AI. A report authored recently by academics and industry experts speculates about ways that AI could be misused by criminals, terrorists, or foreign governments.
Understand the technology
Above all, the government needs to understand what AI is and what it will do. Since artificial intelligence is such a complex and fast-moving field, it is especially important for experts to be brought in to brief policymakers and the administration. Without technical acumen, it will be a challenge to act effectively in any area relevant to AI.
But technical expertise is distressingly lacking in the US government today. There is no leader at the Office of Science and Technology Policy, a key role in advising the president, and many other jobs there reportedly remain unfilled.
“Whenever there is technological disruption, leadership matters,” says Ng.
This feature originally appeared in Technology Review.