WHEN GOOGLE AND Microsoft boast of their deep investments in artificial intelligence and machine learning, they highlight flashy ideas like unbeatable Go players andsociable 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 declined to say how close its recently published click-prediction system is to what it uses in its ad business. Researcher Gang Fu said in an email that there is still much more potential for using machine learning in ads. “It is still a technically challenging problem and also any (even slight) improvement on model accuracy would have great impact for many organizations,” he wrote. Microsoft tells WIRED that it constantly tests new machine learning technologies in its advertising system. In an email, John Cosley, director of marketing for Microsoft search advertising, described ads as “perhaps by far the most lucrative application of AI [and] machine learning in the industry.”
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 biasedtoward 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.
Continue at: https://www.wired.com/story/big-tech-can-use-ai-to-extract-many-more-ad-dollars-from-our-clicks/
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