Google uses AI in Google Search, where several AI concepts like RankBrain, neural matching, BERT, and MUM work together to provide the results for the user’s query.

Artificial intelligence (AI) is gradually evolving as an emerging concept in mainstream companies with an enormous amount of data and examining the right technologies to explore and leverage it. That’s why expected that 80% of emerging technologies have AI foundations. Likewise, Google also uses AI in Google Search, and SEO agency in Chennai succeed in their projects with deep knowledge of AI concepts.

How AI is transforming Business?

AI should be top of mind for most business leaders. AI means integrating sources of data and operating that data to enrich the prioritization, personalization, and range of marketing campaigns using machine learning concepts. 

While many associations are just initiating to understand how AI can match into their digital approaches, it’s fastly evolving as a necessity to leverage data virtually.

As Google persists to leverage more useful artificial intelligence and machine learning in Google Search, one may wonder in what ways AI and machine learning use Google Search to perform its daily tasks. 

A few months ago we sent Google several queries about how Google uses its AI in search, including RankBrain, neural matching, BERT, and Google’s latest AI breakthrough – MUM. Google uses AI, where it does in Google Search, various AI concepts may perform together, some changed over the years, and what, if anything, search marketers require to know when it comes to how Google uses AI in search.

It begins by writing content for humans

You hear it all the time, from Google voices and many SEOs: document content for humans. In the older days of SEO, when the concepts were simpler. Now, there is predominantly Google, with a tiny bit of Bing and some ruffling from DuckDuckGo – but the concepts are much more complicated with machine learning and AI, the concepts comprehend language more like a human.

Some criteria are given by Google to proceed with writing for humans, and that can’t standardize your spot for BERT or any AI. If you document content for human understanding, then the concepts and AI search engines will apprehend it. In brief, this article is not sought of trying to provide SEO tips to standardize your sites for any distinct AI, but instead to express how Google uses AI in Google Search.

Overview of AI used in Google Search


It begins with RankBrain, Google’s first shot at using AI in search dates back to 2015. Google advised us that RankBrain allows Google to comprehend how terms are affiliated to concepts and can bring all-around queries and sufficiently describe how that query connects to real-world concepts. While it was established in 2015, 15% of queries operated, Google said it is now, in 2022, widely operated in multiple queries and all terminologies and regions. RankBrain especially helps Google rank search outcomes and is the element of the ordering algorithm.

  • Year Launched: 2015
  • Used For Ranking: Yes
  • Looks at the query and content language
  • Works for all languages
  • Very commonly used for any queries

Here is a model delivered by Google regarding the performance of the RankBrain. If you explore “Title of the consumer at the most elevated level of a food chain”, Google’s systems understand from looking at those words on multiple pages that the concept of a food chain may have to do with animals, and not human consumers. By comprehending and comparing these words to their corresponding concepts, RankBrain assists Google to understand that you’re glancing for “apex predator.”

Neural matching

Neural matching was the successive AI Google released for search, it was published in 2018 and then inflated to the local search outcomes in 2019. We hold a report describing the differences between RankBrain and neural matching over here. Google told us neural matching helps Google understand how queries relate to pages by looking at the entire query or content on the page and understanding it within the context of that page or query. Today, neural matching is used in many, if not most, queries, for all languages, in all regions, and across most verticals of search. Neural matching does specifically help Google rank search results and is part of the ranking algorithm.

  • Year Launched: 2018
  • Used For Ranking: Yes
  • Looks at the query and content language
  • Works for all languages
  • Very commonly used for any queries

BERT, Bidirectional Encoder Representations from Transformers, established in 2019, is a neural network-based approach for natural language processing pre-training. Google advised us BERT enables Google to understand mixtures of words that convey different meanings and purposes, including exploring the arrangement of words on a page, so even apparently insignificant words in your queries are estimated.

When BERT was established, it was used in 10% of all English queries but extended to more languages and was used in almost all English queries early on. Today it is used in most queries and is invested in all languages. BERT especially helps Google rank search effects and is part of the ranking concepts.

  • Year Launched: 2019
  • Used For Ranking: Yes
  • Looks at the query and content language
  • Works for all languages but Google said BERT “plays a critical role in almost every English query”
  • Very commonly used for any queries

Here is an example provided by Google of how BERT is used, if you search for “can you get medicine for someone pharmacy,” BERT helps us understand that you’re trying to figure out if you can pick up medicine for someone else. Before BERT, we took that short preposition for granted, mostly surfacing results about “Can I pick up a prescription for someone else?” Google told us.


MUM, Multitask Unified Model, Google’s most existing AI in search.

It was established in the year 2021 and then started expanding at the end of 2021 for more applications, with a bunch of promising uses for it in the future. Google told us that MUM assists Google with learning languages with developing languages, so it is used to comprehend divergences in new terms and languages. MUM is not used for any scaling objectives right now in Google Search but does sustain all languages and regions.

  • Established year: 2021
  • Used For Scaling: No
  • Not query or languages specific
  • Suits all languages but Google is not used for scaling purposes today
  • Used for a restricted number of purposes

Currently, MUM is used to enhance searches for COVID-19 vaccine information. Google stated, “Glimpsing forward to deliver more intuitive modes to search using an assortment of both text and images in Google Lens in the future”.

AI is used together in Google search

Google told us that all of these AI systems “are utilized to understand language including the question and potentially applicable results,” adding that “they are not organized to operate in isolation to examine just a query or a page.” Theretofore, it may have been considered and understood that one AI system may have glimpsed more at understanding the query and not the scope on the page, but that is not the case, at least not in 2022.

Google also established that RankBrain, neural matching, and BERT are used worldwide, in all languages that Google Search operates. The best SEO company in Chennai analyses the above concepts and then steps in their client websites for ranking.

And when it arrives at web tracking versus local search versus images, shopping, and other verticals, Google defined that RankBrain, neural matching, and BERT are used for web search. Other methods or verticals of Google Search such as photos or shopping ways use separate, technical AI systems, according to Google.

Core updates and AI

As described above, Google uses RankBrain, neural matching, and BERT in most questions you utilize Google Search, but Google also has core updates. The Google broad core updates that Google rolls out a rare time per year are usually glimpsed by site owners, publishers, and SEOs more than when Google removes these more extensive AI-based systems.

But Google said these all can perform together, with core updates. Google said these three, RankBrain, neural matching, and BERT are the larger AI systems they have. But they have many AI techniques within search and some within the core updates that Google rolls out.

Google does own other machine learning techniques in Google Search. “RankBrain, neural matching, and BERT are just some of our more effective and renowned systems”, Google said.