Analysis of Rankbrain Algorithm Using Machine Learning

RankBrain is Google’s name for a machine-learning artificial intelligence system that’s used to help process its search results. Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. The methods Google already uses to refine queries generally all flow back to some human being somewhere doing work, either having created stemming lists or synonym lists or making database connections between things. Sure, there’s some automation involved. But largely, it depends on human work. True artificial intelligence, or AI for short, is where a computer can be as smart as a human being, at least in the sense of acquiring knowledge both from being taught and from building on what it knows and making new connections. In terms of RankBrain, it seems to us ‘RankBrain and AI’ are fairly synonymous. You may hear them both used interchangeably, or you may hear machine learning used to describe the type of artificial intelligence approach being employed. RankBrain is nothing, but a part of Google’s overall search “algorithm,” a computer program that’s used to sort through the billions of pages it knows about and find the ones deemed most relevant for particular queries. RankBrain is one of the “hundreds” of signals that go into an algorithm that determines what results appear on a Google search page and where they are ranked. In the few months it has been deployed, RankBrain has become the third-most important signal contributing to the result of a search query. Thus, it is helping to increase the efficiency of Hummingbird algorithm using machine learning.