The United States Can Be a World Leader in AI. Here's How.
The artificial intelligence (AI) revolution is upon us. Artificial general intelligence—machines that could perform the full range of intellectual tasks better than humans—are still far off. But narrow AIs built to do specific tasks better than humans are proliferating. We interact with them daily, whether in the form of smartphone apps, self-driving cars or drones. Nations are already jostling for advantage. As Russian President Vladimir Putin said last month, “Whoever becomes the leader in [AI] will become the ruler of the world.” Some are more cautious. SpaceX and Tesla CEO Elon Musk tweeted a response to Putin: “Competition for AI superiority at national level [is the] most likely cause of WW3.”
The United States must grapple with how best to take advantage of this new industrial revolution. This isn’t just a question about how to harness AI’s full potential. It’s also a question about how to mitigate the risks posed by AI proliferation at home and abroad.
Industrial revolutions are historically periods of massive change. They start with a revolution in production. The first states to capitalize on those revolutions are often the ones with access to critical resources. In the eighteenth-century, confronted by rising wages, British innovators sought alternatives to human labor. The steam engine was just such an alternative—but it needed fuel. Fortunately, Britain sat atop massive, easily-accessible coal reserves, which went on to fuel the first industrial revolution.
What will fuel the AI revolution? Three resources are critical: data, people and ideas.
If artificial intelligence is the engine powering the new industrial revolution, data is its fuel. Machine learning algorithms need to learn from large datasets. This poses a few dilemmas to states trying to capitalize on the AI revolution. Data is not distributed evenly worldwide. China is expected to be home to 20 percent of the world’s data by 2020, by virtue of its massive population and its citizens’ increasing use of smart technologies. This may give China a strategic advantage over competitors. The more data available, the more opportunity there is to train machine learning algorithms.
Access to data is not only a technical problem; it’s also a policy issue. People are leaving behind an ever-growing data trail as smartphones, social media, wearable devices and internet-enabled cars and household items become more commonplace. Who owns this data? Who can access it and for what purpose? Regulation should balance privacy concerns with the need to make data available for AI innovation.
For example, a McKinsey study found that AI integration into healthcare could save the United States $300 billion annually. But that would require allowing more companies access to consumers’ personal health information. Different governments might balance these tradeoffs in different ways. China’s new national AI strategy emphasizes “military-civil fusion,” using commercial advances to spur military improvements. This would imply that China’s military will be capitalizing directly on—if not using—data produced by consumers as it improves its own efficiencies.
Because of its central role in enabling machine learning, data could fast become a strategic resource for competition, like oil in the era of the internal combustion engine. Nation-states and corporations could engage in data poisoning or outright data theft to undermine competitors. They could also create synthetic data to train machines, or watermark their own data to ensure its integrity.