When Google announced the introduction of Rankbrain in 2015 for the better interpretation of search queries, there were a lot of assumptions and opinions from the SEO industry. For example, there was talk of the birth of machine learning or AI at Google. As explained in my article Importance of Machine Learning, AI & Rankbrain for SEO & Google, Google has been working intensively on deep learning since 2011 as part of the Google Brain project. However, Rankbrain was the first official confirmation from Google that machine learning is also being used in Google search.
Probably the most precise information from Google about israel phone number data Rankbrain so far was in the Bloomberg interview from October 2015.
A statement in the interview caused many misunderstandings about Rankbrain.
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.
Many SEO media have interpreted the statement to mean that Rankbrain is one of the three most important ranking factors. But a signal is not a factor. Rankbrain has a major influence on how search results are selected and ordered, but is not a factor such as certain components of content or links.
There is another interesting statement in this interview:
RankBrain uses artificial intelligence to embed vast amounts of written language into mathematical entities — called vectors — that the computer can understand. If RankBrain sees a word or phrase it isn't familiar with, the machine can make a guess as to what words or phrases might have a similar meaning and filter the result accordingly, making it more effective at handling never-before-seen search queries .
Rankbrain identifies entities in a search query and compares them with the facts from the Knowledge Graph. If the terms are ambiguous, Google uses vector space analysis to find out which of the possible entities best fit the term. The surrounding words in the search term are an initial indication for determining the context. This may also be a reason why Rankbrain is used for long-tail search queries, among other things.
The problem in the pre-Rankbrain era was the lack of scalability in identifying and creating entities in the Knowledge Graph. The Knowledge Graph is currently still based primarily on information from Wikidata, which is verified by Wikipedia entities, and Wikipedia itself - i.e. manually maintained and therefore rather static and thus not scalable system.