A billion Bill Clintons
Machine learning was the kingmaker in the 2012 presidential election. The factors that usually decide presidential elections-the economy, likability of the candidates, and so on-added up to a wash, and the outcome came down to a few key swing states. Mitt Romney’s campaign followed a conventional polling approach, grouping voters into broad categories and targeting each one or not. Neil Newhouse, Romney’s pollster, said that “if we can win independents in Ohio, we can win this race.” Romney won them by 7 percent but still lost the state and the election.
In contrast, President Obama hired Rayid Ghani, a machine-learning expert, as chief scientist of his campaign, and Ghani proceeded to put together the greatest analytics operation in the history of politics. They consolidated all voter information into a single database; combined it with what they could get from social networking, marketing, and other sources; and set about predicting four things for each individual voter: how likely he or she was to support Obama, show up at the polls, respond to the campaign’s reminders to do so, and change his or her mind about the election based on a conversation about a specific issue. Based on these voter models, every night the campaign ran 66,000 simulations of the election and used the results to direct its army of volunteers: whom to call, which doors to knock on, what to say.
In politics, as in business and war, there is nothing worse than seeing your opponent make moves that you don’t understand and don’t know what to do about until it’s too late. That’s what happened to the Romney campaign. They could see the other side buying ads in particular cable stations in particular towns but couldn’t tell why; their crystal ball was too fuzzy. In the end, Obama won every battleground state save North Carolina and by larger margins than even the most accurate pollsters had predicted. The most accurate pollsters, in turn, were the ones (like Nate Silver) who used the most sophisticated prediction techniques; they were less accurate than the Obama campaign because they had fewer resources. But they were a lot more accurate than the traditional pundits, whose predictions were based on their expertise.
You might think the 2012 election was a fluke: most elections are not close enough for machine learning to be the deciding factor. But machine learning will cause more elections to be close in the future. In politics, as in everything, learning is an arms race. In the days of Karl Rove, a former direct marketer and data miner, the Republicans were ahead. By 2012, they’d fallen behind, but now they’re catching up again. We don’t know who’ll be ahead in the next election cycle, but both parties will be working hard to win. That means understanding the voters better and tailoring the candidates’ pitches-even choosing the candidates themselves-accordingly. The same applies to entire party platforms, during and between election cycles: if detailed voter models, based on hard data, say a party’s current platform is a losing one, the party will change it. As a result, major events aside, gaps between candidates in the polls will be smaller and shorter lived. Other things being equal, the candidates with the better voter models will win, and voters will be better served for it.
One of the greatest talents a politician can have is the ability to understand voters, individually or in small groups, and speak directly to them (or seem to). Bill Clinton is the paradigmatic example of this in recent memory. The effect of machine learning is like having a dedicated Bill Clinton for every voter. Each of these mini-Clintons is a far cry from the real one, but they have the advantage of numbers; even Bill Clinton can’t know what every single voter in America is thinking (although he’d surely like to). Learning algorithms are the ultimate retail politicians.
Of course, as with companies, politicians can put their machine-learned knowledge to bad uses as well as good ones. For example, they could make inconsistent promises to different voters. But voters, media, and watchdog organizations can do their own data mining and expose politicians who cross the line. The arms race is not just between candidates but among all participants in the democratic process.
The larger outcome is that democracy works better because the bandwidth of communication between voters and politicians increases enormously. In these days of high-speed Internet, the amount of information your elected representatives get from you is still decidedly nineteenth century: a hundred bits or so every two years, as much as fits on a ballot. This is supplemented by polling and perhaps the occasional e-mail or town-hall meeting, but that’s still precious little. Big data and machine learning change the equation. In the future, provided voter models are accurate, elected officials will be able to ask voters what they want a thousand times a day and act accordingly-without having to pester the actual flesh-and-blood citizens.
One if by land, two if by Internet
Out in cyberspace, learning algorithms man the nation’s ramparts. Every day, foreign attackers attempt to break into computers at the Pentagon, defense contractors, and other companies and government agencies. Their tactics change continually; what worked against yesterday’s attacks is powerless against today’s. Writing code to detect and block each one would be as effective as the Maginot Line, and the Pentagon’s Cyber Command knows it. But machine learning runs into a problem if an attack is the first of its kind and there aren’t any previous examples of it to learn from. Instead, learners build models of normal behavior, of which there’s plenty, and flag anomalies. Then they call in the cavalry (aka system administrators). If cyberwar ever comes to pass, the generals will be human, but the foot soldiers will be algorithms. Humans are too slow and too few and would be quickly swamped by an army of bots. We need our own bot army, and machine learning is like West Point for bots.
Cyberwar is an instance of asymmetric warfare, where one side can’t match the other’s conventional military power but can still inflict grievous damage. A handful of terrorists armed with little more than box cutters can knock down the Twin Towers and kill thousands of innocents. All the biggest threats to US security today are in the realm of asymmetric warfare, and there’s an effective weapon against all of them: information. If the enemy can’t hide, he can’t survive. The good news is that we have plenty of information, and that’s also the bad news.
The National Security Agency (NSA) has become infamous for its bottomless appetite for data: by one estimate, every day it intercepts over a billion phone calls and other communications around the globe. Privacy issues aside, however, it doesn’t have millions of staffers to eavesdrop on all these calls and e-mails or even just keep track of who’s talking to whom. The vast majority of calls are perfectly innocent, and writing a program to pick out the few suspicious ones is very hard. In the old days, the NSA used keyword matching, but that’s easy to get around. (Just call the bombing a “wedding” and the bomb the “wedding cake.”) In the twenty-first century, it’s a job for machine learning. Secrecy is the NSA’s trademark, but its director has testified to Congress that mining of phone logs has already halted dozens of terrorism threats.
Terrorists can hide in the crowd at a football game, but learners can pick out their faces. They can make exotic bombs, but learners can sniff them out. Learners can also do something more subtle: connect the dots between events that individually seem harmless but add up to an ominous pattern. This approach could have prevented 9/11. There’s a further twist: once a learned program is deployed, the bad guys change their behavior to defeat it. This contrasts with the natural world, which always works the same way. The solution is to marry machine learning with game theory, something I’ve worked on in the past: don’t just learn to defeat what your opponent does now; learn to parry what he might do against your learner. Factoring in the costs and benefits of different actions, as game theory does, can also help strike the right balance between privacy and security.