When Google and Microsoft boast of their deep investments in artificial intelligence and machine learning, they highlight flashy ideas like unbeatable Go players and sociable chatbots. They talk less often about one of the most profitable, and more mundane, uses for recent improvements in machine learning: boosting ad revenue.
AI-powered moonshots like driverless cars and relatable robots will doubtless be lucrative when—or if—they hit the market. There’s a whole lot of money to be made right now by getting fractionally more accurate at predicting your clicks.
Many online ads are only paid for when someone clicks on them, so showing you the right ones translates very directly into revenue. A recent research paper from Microsoft’s Bing search unit notes that “even a 0.1 percent accuracy improvement in our production would yield hundreds of millions of dollars in additional earnings.” It goes on to claim an improvement of 0.9 percent on one accuracy measure over a baseline system.
Google, Microsoft, and other internet giants understandably do not share much detail on their ad businesses’ operations. But the Bing paper and recent publications from Google and Alibaba offer a sense of the profit potential of deploying new AI ideas inside ad systems. They all describe significant gains in predicting ad clicks using deep learning, the machine learning technique that sparked the current splurge of hope and investment in AI.
Google CEO Sundar Pichai has taken to describing his company as “AI first.” Its balance sheet is definitively ads first. Google reported $22.7 billion in ad revenue for its most recent quarter, comprising 87 percent of parent company Alphabet’s revenue.
Earlier this month, researchers from Google’s New York office released a paper on a new deep learning system to predict ad clicks that might help expand those ad dollars further. The authors note that a company with a large user base can greatly increase revenues with “a small improvement,” then show their new method beats other systems “by a large amount.” It did so while also requiring much less computing power to operate.
Alibaba, the Chinese ecommerce company and one of the world’s largest retailers, also has people thinking about boosting its billions in annual ad revenue with deep learning. A June paper describes something called a deep interest network, which can predict what product ads a user will click. It was tested on anonymized logs from some of the hundreds of millions of people who use its site each day.
Alibaba’s researchers tout the power of deep learning to outperform conventional recommendation algorithms, which can sometimes stumble on the sheer diversity of users’ online lives. For example, a young man may sometimes be shopping for himself and sometimes for kids clothing.
It’s hard to know what effect deep learning is having on tech giants’ ad revenues. Many factors affect the online ad markets, and companies don’t reveal everything about their technology or businesses. Google has reported steady growth in ad revenue for many years; Microsoft has called out strong growth in Bing search ad revenue and in average revenue per search in its past five quarterly earnings releases.
Google couldn’t make anyone available for interview before publication. Microsoft tells WIRED that it constantly tests new machine learning technologies in its advertising system. “Online advertising is perhaps by far the most lucrative application of AI [and] machine learning in the industry,” says John Cosley, director of marketing for Microsoft search advertising. Bing recently started using new deep learning algorithms to better understand the meaning of search queries and find relevant ads, he says.
Research papers on using deep learning for ads may undersell both its true power and the challenge of tapping into it. Companies carefully scrub publications to avoid disclosing corporate secrets. And researchers tend to describe simplified versions of the problems faced by engineers who must target and serve ads at huge scale and speed, says Suju Rajan, head of research at computational advertising company Criteo. The company has released anonymized logs of millions of ad clicks that Google and others have used in papers on improving click predictions.
Perhaps not surprisingly, Rajan believes deep learning still has much more to offer the ad industry. For example, it could figure out long-term cause and effect relationships between what you see or do online today and what you click on or buy next week. “Being able to model the timeline of user interest is something that the deep models are able to do a lot better,” she says.
That Google and Microsoft are getting better at predicting our desires and clicks can be seen as a good thing. It gets them closer to the long-sought goal of serving up ads that don’t feel like ads because they’re useful. And it helps advertisers reach the people they want to reach.
But online ad companies are also subject to incentives less well aligned with consumers or other companies. Benjamin Edelman, a professor at Harvard Business School, has published research suggesting Google search is biased toward the company’s own services and designed to unfairly force corporations into spending heavily on ads for their own trademarks. (Google has been fined $2.7 billion for the former and successfully defended multiple lawsuits alleging the latter.)
Such market-warping practices could be boosted by machine learning too. “If machine learning can improve the efficiency of their advertising platform by showing the right ad to the right guy, then more power to them—they are creating value,” Edelman says. “But a lot of the things that Google has done haven’t enlarged the market.” In advertising, as in many other areas, AI can give tech companies great power—and responsibility.