Danny Sullivan of SearchEngineLand wrote an interesting piece on how RankBrain has now become the third most important ranking factor behind content and links. According to a report on BackChannel RankBrain is being used on almost ALL search queries helping determine the most relevant results and their order:
Google is characteristically fuzzy on exactly how it improves search (something to do with the long tail? Better interpretation of ambiguous requests?) but Jeff Dean says that RankBrain is “involved in every query,” and affects the actual rankings “probably not in every query but in a lot of queries.” What’s more, it’s hugely effective. Of the hundreds of “signals” Google search uses when it calculates its rankings (a signal might be the user’s geographical location, or whether the headline on a page matches the text in the query), RankBrain is now rated as the third most useful.
“It was significant to the company that we were successful in making search better with machine learning,” says John Giannandrea. “That caused a lot of people to pay attention.” Pedro Domingos, the University of Washington professor who wrote The Master Algorithm, puts it a different way: “There was always this battle between the retrievers and the machine learning people,” he says. “The machine learners have finally won the battle.”
RankBrain announced in October 2015 in a Bloomberg article and video (below) is a machine learning algorithm that uses big data from billions of search queries, who’s searching, who clicks what, geolocation, etc. to better determine what a user expects to see in the search results.
Google has been very secretive about RankBrain since October but did references it recently in a post on their Google Cloud Platform Blog. The post, by Google Hardware Engineer Norm Jouppi, was about how machine learning is now powering most of Google’s applications including Street View, Inbox Smart Reply, voice search and Google Search. Norm commented about a “stealthy project at Google several years ago to see what we could accomplish with our own custom accelerators for machine learning applications.” This “stealthy project” resulted in Tensor Processing Unit (TPU) which is now powering machine learning features at Google.
It’s fascinating how fast Google implements new technology in its applications:
TPU is an example of how fast we turn research into practice — from first tested silicon, the team had them up and running applications at speed in our data centers within 22 days.
TPUs already power many applications at Google, including RankBrain, used to improve the relevancy of search results and Street View, to improve the accuracy and quality of our maps and navigation. AlphaGo was powered by TPUs in the matches against Go world champion, Lee Sedol, enabling it to “think” much faster and look farther ahead between moves.