August 7, 2023
by Shreya Mattoo / August 7, 2023
Do you also burst into tears when you don't get the right responses to your searches? It can’t just be me, right? Right?
Thankfully, we don’t have that experience so often because Google’s search operations are evolving every day. From the introduction of helpful content updates to E-EAT to now Google MUM, Google has tapped into our hearts. As generative AI expands, Google has been on the cusp of refining its search algorithms to don the crown of “best search engine ever.”
Generative AI has brought a lot of businesses under its belt, but Google is not far behind the race. The newest Google MUM (multitask unified model) update has enhanced search capabilities, SERP relevance, and personalized user journeys in ways unimaginable.
What sort of web content will appeal to which user persona? What is the user's feeling while searching for a resource? The self-evolving architecture of generative AI software in the MUM model can capture all this and more.
Google multitask unified model, or Google MUM is a multimodal technique posed to refine the value of search results. It was announced in May 2021 by Pandu Nayak, VP of Search at Google. MUM has replaced the bidirectional encoder representations from transformers (BERT) based web search responses into a more illustrative and giving search experience.
MUM strives to change Google’s user interface (UI) and bring a cohesive palette of resources to the curious audience. For example, Prabhakar Raghavan, Senior Vice President at Google, asserted that Google MUM can answer anything. It asked Google to compare and contrast climbing Mt. Adams and Mt. Fuji, given that he has already trekked Mt. Addams. Not only did Google return the list of differences or similarities, but it also added additional shop links for trek gear and video links.
As an upgraded AI technology, the MUM update improves the functionality of the BERT model. The primary reason for launching MUM was to give users a 360° search experience.
While both neural network architectures have dominated the search algorithm, MUM has a slight edge over BERT.
BERT is a 2019 Google update that uses natural language processing to resolve search queries. Based on a transformer neural network, this model contextualizes and encodes search queries to understand the intent behind it. With this update, Google can personalize answers, summarize text, and define the intent and categories of search queries.
Google MUM is a 2021 update derived from a T5 (text-to-text) framework, specifically catering to long-tail queries or a combination of complex queries. It declutters the SERP data and highlights a slew of resources for brand awareness. MUM uses cookie data, web stream data, user search query data, and crawl data to filter out content from reliable sites.
We’ve come a long way from the 1980s when the Advanced Research Projects Agency Network (ARPANET) was launched. The exchange of information was restricted to two or more workstations, as data was transmitted over wired servers. Fast forwarding to the internet era, Google used edge computing and serverless containerization to store, retrieve, and send data from servers. Over time, the strategy by which Google treated its users changed.
In the following years, Google released several updates.
Google MUM combines several technologies to make Google search more holistic and contextual. The large language model (LLM) behind MUM works in over 75 languages. Initially, this Google search algorithm functioned on the concept of retrieval systems. That means the search keyword was compared against a set of keys in the Google database. If there was a match, that value of the key was displayed.
Now, Google MUM uses sequence-to-sequence template matching to enhance user knowledge. Usually, when someone is stuck between a decision to purchase a product or a service, a hearty call to action helps. But MUM’s strategic approach puts forth a ton of images, videos, and media resources for that query and also presents answers for alternate questions.
MUM produces a calculated SERP that contains a far-stretched perspective of user needs in the main interface. This is also known as “simultaneous query processing.” The machine learning (ML) algorithm converts words into vectors, transfers knowledge to the server, and responds with valuable information. With MUM, non-organic content ranks faster, resulting in lower click-through rates (CTRs) but more content engagement.
Essentially, in a sales funnel, customers struggle to make decisions between the “evaluation” and the “awareness” stage. Organic websites and content are used to convert web experiences into sales, whereas MUM focuses on bringing a swath of digital assets in the form of multimedia. Users are treated to the best of the best so that they “evaluate all options” before striking a deal.
Do you remember iGoogle? It was a personalized Google homepage custom set with Ajax in 2005. By analyzing previous web behavior, it offered immersive insights in one window. The concept of iGoogle formed the foundation of Google MUM, where the idea was hardwired with AI.
Currently, no one can predict the gamut of features Google MUM will bring with its release. It’s still being cross-validated for accuracy. When launched, MUM might represent three main levels.
For different systems, servers, and data transfers, MUM will work with a certain degree of efficiency. For now, three existing levels have already been implemented using Google MUM:
Do you know? MUM was able to list 800 variations of COVID-19 vaccines in more than 50 languages in a matter of seconds. After testing the findings, this data was used to deliver high-quality and critical vaccine information to different locations.
Currently, SERP is viewed as a “length x breadth” interface experience. Every search engine result page has a featured snippet and a length of blue links with the most suitable content. But with MUM, a newer spectrum of features will come into play that will make search more responsive, user-friendly, and fun.
The MUM algorithm will be a turning point for search engine optimization (SEO) enthusiasts. In the future, a lot of Google response techniques will be driven by MUM. Not only will this benefit web teams but also audiences.
Keep up with what’s really going down in marketing with the G2 Tea newsletter. Subscribe here
MUM has stepped up the volatility of web searches and internet browsing. But with every new feature-packed update comes unavoidable bugs and limitations.
MUM isn’t Google’s first AI sprint. For years, Google’s CEO, Sundar Pichai, has pushed the envelope of generative AI and its volumes of possibilities. Google aims to inject diversity, equity, and inclusion guidelines within MUM through artificial intelligence.
MUM can be classified as the next big AI milestone. The traditional way of tackling information and finding the best choice for your needs is being revolutionized. Soon, users will be able to virtualize related topics for the primary query. Finding quality content in one place will reduce their frustration and web consumption time. That’s what the network behind MUM is striving for.
Previous machine learning updates leaned towards stabilizing the search experience, avoiding bugs, and detecting blackhat links and plagiarized content on the web. In a couple of later updates, Google reinforced the “intent” mechanism. Using advanced ML, it mapped the search query language with underlying NLP processors to satisfy user intent and make Google more reliable as an engine.
Earlier AI updates like neural matching, Hummingbird, RankBrain, and BERT were focused on technical SEO and structured data alignment. They gave headroom for organic content and expert-written content. But with generative AI, the focus shifts to what is best for the user to see, regardless of it being organic or sponsored. Google aims to achieve the unimaginable by turning SERP into a distributed social and community network. With this in-depth SEO technique, users will be exposed to recent trends and news in the particular industry they’re looking for.
Google will not only minimize research efforts but also provide a wealth of information with AI.
"AI will impact every product across every company. For example, if you think about 5 to 10 years from now, you are going to have an AI collaborator with you. Let's say you have a hundred things to go through, it may say, "these are the most serious cases you need to look at first."
Sundar Pichai
CEO, Google Inc.
The good news for SEO marketers is that they can continue with their current analysis of how to make their websites rank better on Google. People are still debating on whether MUM will be a search engine ranking factor or simply a data-dispersing bridge.
To compete with the MUM update, brands need to bolster both organic and earned media strategies. While paid media doesn’t always give in CPCs, organic search, and SEO will help brands stay ahead. Even if a fair share of SERP does get affected by MUM, the highest-ranking pages and featured snippets will still be preferred.
Brands should start taking their on-page SEO strategies more seriously. Not just to rank higher but to identify their target audience and transfer learnings. Ideating and designing image packs, making introductory videos, and building awareness will help brands weather the MUM thunderstorm.
With MUM, newly sprung SEO strategies will come into play. Things-to-know sections, video search, visual search, zoom-ins, and voice search will lessen user tedium by giving them all answers in one place. At the same time, it is not a question-answer mechanism. Google aims to create a network of like-minded people to “go smart.”
MUM is an ocean of knowledge, information, and understanding of sentiments. It’s the beginning of a new web search era. Nothing will be too complex on the web or in real life with MUM. This newfound theoretical machine-learning technique has led us to a new digital path.
Learn how you can customize your audience needs with web personalization.
Shreya Mattoo is a Content Marketing Specialist at G2. She completed her Bachelor's in Computer Applications and is now pursuing Master's in Strategy and Leadership from Deakin University. She also holds an Advance Diploma in Business Analytics from NSDC. Her expertise lies in developing content around Augmented Reality, Virtual Reality, Artificial intelligence, Machine Learning, Peer Review Code, and Development Software. She wants to spread awareness for self-assist technologies in the tech community. When not working, she is either jamming out to rock music, reading crime fiction, or channeling her inner chef in the kitchen.
Search offers a far richer experience than ever.
In SEO, it’s hard to predict future trends.
Over the years, on-page SEO practices, for the most part, have remained the same.
Search offers a far richer experience than ever.
In SEO, it’s hard to predict future trends.