Discover how Google’s AI technologies, such as RankBrain, BERT, and MUM, shape search results and their implications for your SEO approach.

Are your SEO efforts falling short of your expectations, leaving you puzzled about the reasons?

Conventional SEO strategies are becoming increasingly ineffective. While you concentrate on keywords and backlinks, Google’s AI is advancing swiftly, fundamentally altering the ranking criteria for search results.

This transformation is occurring behind the scenes, making it harder to discern why your content may not be achieving optimal performance.

Grasping the workings of Google’s AI systems is crucial for adapting your SEO tactics. This article delves into the development of Google’s AI technologies—RankBrain, neural matching, BERT, and MUM—and outlines how these innovations are redefining search processes.

By understanding these concepts, you’ll be better positioned to produce content that aligns with Google’s AI-focused methods, enhancing your chances of improving your search result rankings.

Google’s AI Technologies

Since around 2015, Google has been leveraging various forms of AI to identify, evaluate, and rank URLs, starting with its inaugural AI system, RankBrain.

In 2018, Ben Gomes, Google’s Senior Vice President of Learning and Education and former Head of Search, referred to AI as the “next chapter of Search.”

Gomes articulated that AI would enable Google to enhance the user experience, extending beyond just the query itself. He outlined three significant shifts in how search operates:

• From answers to journeys: “To assist you in continuing tasks you’ve started and exploring new interests and hobbies, we’re introducing new features to Search that cater to ongoing information needs.”
• From queries to a queryless approach for accessing information: “We can present relevant information related to your interests, even when you don’t have a specific query in mind.”
• From text to a more visual method of discovering information: “We’re integrating more visual content into Search and completely redesigning Google Images to facilitate easier access to information.”

The Shift Initiated by RankBrain

RankBrain (2015)

RankBrain marked the initial advancement in enabling the search engine to “comprehend how words relate to concepts.”

This understanding of the relationship between words and their concepts represents a sophisticated capability, signifying Google’s first move toward interpreting content in a manner akin to human understanding.

For instance, if you were to search for “What’s the color of the sky?” the AI can recognize that “sky” is a concept associated with a specific color. As a result, Google can return answers that don’t necessarily contain those exact words but still effectively address the inquiry.

Neural Matching (2018)

A few years later, Google enhanced its ability to associate words with concepts through a system known as neural matching.

This system was designed to aid Google in grasping how “queries relate to pages,” especially for more complex concepts.

For example, if you search for “tie my laces,” which can have multiple interpretations, neural matching allows Google to deduce that “laces” refers to shoelaces and provides results related to how to tie them.

BERT (2019)

BERT, which stands for Bidirectional Encoder Representations from Transformers, is considered a rel=”nofollow” significant breakthrough in search technology.

You can think of BERT as an evolution of both RankBrain and neural matching, enabling Google to comprehend how multiple words within a sentence relate to various words on a page and the underlying concepts.

BERT plays a crucial role in entity recognition, allowing Google to identify a brand name, a person’s identity, and even their expertise in specific topics.

This AI model underpins advancements in generative AI and AI Overviews, and Google has been utilizing it since 2019.

• In relation to BERT is a “deep learning system” known as DeepRank. As noted in Panda Nayuk’s testimony during the DOJ trial, DeepRank essentially functions as BERT when it is applied to ranking.
• Additionally, DeepRank has largely taken the place of RankBrain.

MUM (2021)

Google asserts that the Multitask Unified Model (MUM) is “1,000 times more powerful than BERT.”

While BERT focuses on understanding language, MUM is capable of generating it. Furthermore, it can comprehend both text and images, and possibly video as well.

Pandu Nayak, Google’s Chief Scientist for Search and former VP of Search, described MUM in this manner:

“Consider the question about hiking Mt. Fuji: MUM can grasp that you’re comparing two mountains, meaning elevation and trail details could be significant. It can also recognize that, in the context of hiking, to ‘prepare’ may involve fitness training as well as selecting the appropriate gear.

Given MUM’s extensive knowledge of the world, it can point out that although both mountains have similar elevations, the fall season is rainy on Mt. Fuji, indicating you might require a waterproof jacket.”

MUM’s effectiveness in enhancing search results related to COVID-19 vaccine information exemplifies the system’s power.

Nayak mentioned that MUM assists in distinguishing between different vaccine brand names and providing the “most recent trustworthy information about the vaccine.”

This underscores how Google can now improve search results more rapidly than ever before.

Leveraging AI for SEO: What’s Possible?

What generative AI can accomplish, Google can replicate through its AI-driven ranking system. Take a moment to consider that.

While ChatGPT might have an IQ of up to 155, it’s reasonable to believe that Google’s AI can evaluate sources with a level of human-like discernment.

When a person assesses the quality and relevance of a page in relation to their intent, they might ask the following questions:

• Are you a knowledgeable expert on the subject matter you are discussing or writing about?
• Are other credible experts acknowledging your expertise?
• Do you have a negative reputation for attempting to manipulate Google rankings?
• How do your statements about a topic align with those of other specialists in the field?
• Is this the optimal product for my search query?

However, it’s important to remember that Gomes mentioned AI will transition “from answers to journeys.” This is a crucial insight, suggesting that Google can monitor how you and your audience engage with or create content related to your brand or internal experts.

As a result, Google can provide much more relevant answers to questions such as:

• Do users find value in your product or service?
• Is one website or company related to another, with customers utilizing both?
• Are customers discussing your product and subsequently searching for it on Google?

It’s time to shift our perspective on SEO from merely ranking signals to understanding how humans search for information and the motivations behind their queries.

At SEO Guru NYC, a leading local SEO company in New York, we understand the intricacies of Google’s AI technologies and their impact on search results. Our team is dedicated to helping businesses navigate the evolving landscape of SEO, ensuring your content resonates with both search engines and users alike. Whether you’re struggling with SEO performance or looking to enhance your online visibility, our expert strategies are designed to drive results and elevate your brand’s presence in search. Partner with us to harness the power of AI for your SEO success!