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.