The Use of Artificial Intelligence in Google Search Engine Algorithms

Artificial Intelligence in Google Search Algorithms

The search engine giant Google has always been focused on understanding and responding to its users’ needs. Over the past few years, we have witnessed countless changes in Google’s algorithm, and it’s always been something important to industries.

However, while most of Google’s algorithms changes weren’t earth-shattering, this current evolution just might be it. The introduction of its latest Artificial Intelligence (AI) algorithm called MUM is something we haven’t seen before.

However, MUM isn’t the first AI algorithm used by Google. In fact, there have been many more smart algorithms including, BERT, neural matching, and RankBrain.

Going forward, Google Search will be far more intuitive and inclusive to global users like never before. It’s important as it will impact your search engine visibility and, of course, your Search Engine Optimization (SEO) campaigns.

What is MUM?

Google’s MUM stands for Multitask Unified Model. First announced in June 2021 as a new AI-based technology, MUM is equipped to understand and answer complex users’ search queries and improve its already robust search capabilities.

In this scenario, AI agents perform tasks towards achieving a specific goal. They can also establish actions that bring about the desired result. A series of these actions provide solutions to problems and happen every time you click on the search button.

AI agents don’t rest until they find the best solution for potential problems by trawling through all possible alternatives or solutions. The search algorithms that work in tandem with AI agents operate in two primary phases:

  1. Defining the problem
  2. Searching for solutions

Before MUM, search engines were hardly intuitive, but that wasn’t the only driving force behind the algorithm’s creation. Google also aims to help its global user base overcome language and geographical barriers while performing searches.

This means that MUM will now eliminate the need to conduct multiple searches and compare results to gain deeper insights into user queries. It’s possible as the smart algorithm has access to data in more than 75 different languages to deliver comprehensive and holistic search experiences.

The company has been training algorithms to translate words and phrases for years. So, it’s now possible to unleash these smart algorithms to do much more than support translation work.


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How is MUM Different from Previous AI Algorithms?

Unlike Google’s previous algorithm updates, MUM has the potential to help users engage in complex tasks. Leveraging the T5 text-to-text framework, which is a thousand times more powerful than BERT, MUM not only understands the language it can also generate it.

Comprehensive Understanding of Search Queries

As the algorithms train on over different languages and tasks simultaneously, they can better understand the world and what’s going on within it. This wasn’t possible before MUM.

As such, this multimodal algorithm can understand information across images, texts, and probably audio and video in the near future. By understanding information stored in different formats, Google is essentially building a foundation for the future.

This approach will also help enhance voice assistants who will now better serve users by understanding the context of search queries. For example, if a user asks about hiking Mount Everest, the search results will show its elevation and relevant trail information.

In a year or two, you might be able to take a picture of your hiking boots and ask the search engine if they were suitable for the terrain. A simple question like “can I hike up Mount Everest in these boots?” will get you the answer you’re looking for and connect you to related content like a blog about the most appropriate hiking gear to climb Mount Everest.

The smart algorithm may also understand that you’re searching in the context of hiking to “prepare” and include results about the food and fitness training recommendations, and so on. As MUM keeps learning from search queries, you can say that it will only get better at “reading” our minds.

Smashing Language Barriers

As Google’s AI algorithms use massive AI training datasets of languages and information to learn from, MUM is primed to break down language barriers. This approach will help the search giant provide users worldwide with seamless access to data across languages and accelerate knowledge transfer.

With this latest innovation, Google can access, learn, and provide you with information written in a different language. This enables access to information users didn’t even know existed. In the same vein, MUM can also help users in the developing world expand their knowledge base and better engage the greater world around them.

For example, if there is helpful information about Mount Everest but it’s written in Nepali, you wouldn’t have found it before MUM. In a post-MUM world, Google will be able to translate and transfer that knowledge that only users from Nepal were able to access in years past.

Google has made the planet’s information more accessible from day one. However, AI has helped the company take online search to the next level. As the new update was rigorously evaluated and rated by humans for accuracy, the risk of machine learning bias is low.

The research and development phase of MUM is still ongoing, so you can expect this latest update to evolve and grow in the months ahead. But this doesn’t mean that you must start writing for robots. Instead, it’s vital to keep writing for a “human audience” and not bots.

Google continues to use various individual AI-based algorithms (including BERT and RankBrain), and they will all work together to help better understand a query. It will also continue to help with ranking.

At the time of writing, BERT, RankBrain, and neural matching continued to power web search, local search, and shopping. Other verticals like image search use different specialized AI systems.

The success of the MUM project will depend on the quality of the database used to train the algorithms. As we’re talking about different languages and the cultural influences and nuances that go with them, this is no easy feat. But with the company’s extensive resources and a large pool of diverse datasets, they just might be able to achieve what was once deemed impossible.


Andrew Zola