What is RankBrain? The Evolution of the Google Algorithm

What is RankBrain—and how is it changing Google Search? Since 2015, Google has been relying on the self-learning AI system RankBrain to interpret search queries. It helps in re­cog­nising user intent even with new or complex search terms and delivers relevant results. The algorithm is based on machine learning and is con­sidered part of Google’s long-term AI strategy, which also includes DeepMind.

What is RankBrain? The defin­i­tion

RankBrain is a self-learning AI system that has been used since early 2015 as part of the over­arch­ing Google search algorithm ‘Hum­ming­bird’. The primary task of RankBrain is to interpret keywords and search phrases with the aim of de­term­in­ing user intent.

According to their own data, Google receives around 8.5 billion queries daily through its web search. About 16 percent of user inputs consist of keywords and word com­bin­a­tions that have never been entered in that form before— including col­lo­qui­al terms, neo­lo­gisms, or complex long-tail phrases.

How does RankBrain work?

RankBrain helps Google interpret user inputs and find exactly the webpages from the Google search index—a database around 100 million gigabytes in size—that best match the user’s search intent. The AI system goes well beyond merely matching search terms. Instead of analysing each word of a query in­de­pend­ently, RankBrain captures the semantics of the entire user input and thus de­term­ines the intent of the searcher. This way, even with a long-tail phrase, users quickly get to the answer they are hoping for.

As a machine-learning system, RankBrain draws on its ex­per­i­ence with previous search queries. It creates con­nec­tions and makes pre­dic­tions based on that about what the user is searching for and how to best answer their query. The aim is to resolve am­bi­gu­ities and decipher the meaning of pre­vi­ously unknown terms (e.g., neo­lo­gisms). However, Google does not disclose exactly how RankBrain tackles this challenge. SEO experts suspect that RankBrain trans­lates queries into a form using word vectors that allows computers to interpret con­tex­tu­al meanings.

What provides the basis for RankBrain’s semantic analyses?

According to state­ments from several Google engineers, RankBrain is partly based on concepts like Word2Vec and uses similar vector space tech­niques to grasp the meaning of words. In fact, Google released the open-source machine-learning software Word2Vec back in 2013, which allows for the con­ver­sion, meas­ure­ment, and com­par­is­on of semantic re­la­tion­ships between words into a math­em­at­ic­al rep­res­ent­a­tion. The found­a­tion of this analysis is lin­guist­ic text corpora.

Creation of the Vector Space

To ‘learn’ con­tex­tu­al re­la­tion­ships between words, Word2Vec starts by creating an “n”-di­men­sion­al vector space in which each word of the un­der­ly­ing text corpus (referred to as “training data”) is rep­res­en­ted as a vector. The “n” indicates how many vector di­men­sions a word should be mapped to. The more di­men­sions chosen for the word vectors, the more relations to other words the program captures.

Ad­just­ment of the Vector Space

In the second step, the created vector space is fed into an ar­ti­fi­cial neural network (ANN), which makes it possible to adjust it using a learning algorithm so that words used in the same context also form a similar word vector. The sim­il­ar­ity between word vectors is cal­cu­lated using the so-called cosine distance as a value between -1 and +1.

The role of Word2Vec

When Word2Vec receives a text corpus as input, it generates word vectors that reflect the semantic re­la­tion­ships between the words. These vectors make it possible to evaluate how closely related the words are in meaning. Faced with new input, Word2Vec can update its vector space using its learning algorithm—forming new semantic as­so­ci­ations or revising previous ones as needed: The neural network is ‘trained’.

Of­fi­cially, Google does not establish a con­nec­tion between the func­tion­ing of Word2Vec and the search algorithm component RankBrain—but it can be assumed that the AI system relies on similar math­em­at­ic­al op­er­a­tions.

RankBrain as a ranking factor in search engine op­tim­isa­tion (SEO)

Even more sur­pris­ing than the an­nounce­ment that Google’s ar­ti­fi­cial in­tel­li­gence research impacts web search is how deeply this tech­no­logy is embedded: Since 2016, Google has used RankBrain to interpret every search query. According to Greg Corrado, Senior Research Scientist at Google, the self-learning AI system has become the third most important ranking factor in the search algorithm.

For website operators and SEO experts, the per­spect­ive on keyword strategies has notably changed. As a semantic search engine, Google can leverage back­ground knowledge in the form of concepts and re­la­tion­ships to determine the content’s meaning in texts and search queries. Whether a website ranks well for a specific term is less about con­tain­ing that term and more about whether the website’s (text) content is relevant to the concept that RankBrain as­so­ci­ates with the search term. The focus is not on the keyword itself, but on the content relevance of a website.

Thanks to RankBrain and the con­tinu­ous de­vel­op­ment of BERT and other tech­no­lo­gies, content relevance and user intent are in­creas­ingly the focus of search engine op­tim­isa­tion.

These AI modules com­ple­ment RankBrain

RankBrain was in­tro­duced in 2015 and was con­sidered a break­through in Google’s in­ter­pret­a­tion of search queries at that time. The tech­no­logy has since evolved. Today, RankBrain remains an important part of the Google algorithm, es­pe­cially in in­ter­pret­ing search terms and de­term­in­ing user intent. However, it is no longer the sole factor de­term­in­ing the in­ter­pret­a­tion of search queries.

BERT as support for RankBrain

Since 2019, Google has in­tro­duced BERT (Bi­d­irec­tion­al Encoder Rep­res­ent­a­tions from Trans­formers), another AI model that com­ple­ments RankBrain in pro­cessing natural language inputs. While RankBrain primarily aids in the semantic analysis of long-tail search terms and un­fa­mil­i­ar word com­bin­a­tions, BERT is more utilised for the con­tex­tu­al­isa­tion of complete sentences and the con­sid­er­a­tion of word meanings in their specific context.

MUM and other AI tech­no­lo­gies for in­ter­pret­ing search queries

In addition to RankBrain, Google now uses other AI models like BERT and MUM (Multitask Unified Model) to better un­der­stand search queries. Es­pe­cially complex or ambiguous questions benefit from these ad­vance­ments. MUM is capable of combining in­form­a­tion from various sources and formats (e.g., text and images) and putting them into a mean­ing­ful context.

Even though Google has never fully disclosed exactly how RankBrain, BERT, and MUM work together, it’s clear that semantic search tech­no­logy has sig­ni­fic­antly evolved.

Important AI modules in the Google Algorithm:

• RankBrain: in­ter­prets search queries, es­pe­cially new or unusual ex­pres­sions
• BERT: analyses the context of words in search queries (e.g., sentence structure)
• MUM: un­der­stands complex search in­ten­tions and combines content from different formats

For search engine op­tim­isa­tion, this means: Classic SEO with keywords and tech­no­logy alone is no longer suf­fi­cient. What truly matters now is high-quality, user-centred content that considers search intent, context, and semantic relevance.