What if search engines could understand your query just like a human expert would? That revolutionary shift is exactly what we're witnessing with Google MUM, perhaps the most significant advancement in search technology since the introduction of Bert in 2019.
As an experienced SEO consultant, I've been closely monitoring how Google's algorithms have evolved over the years. From the early days of keyword stuffing to the sophisticated neural networks powering today's search results, the journey has been nothing short of remarkable. In this comprehensive guide, I'll take you through everything you need to know about Google's Multitask Unified Model (MUM) and how it's reshaping the search landscape.
Google MUM (Multitask Unified Model) is an AI-powered algorithm announced by Google in May 2021 during their I/O conference. MUM represents a significant leap forward in Google's mission to understand and organize the world's information. Built on a transformer architecture similar to BERT, MUM is 1,000 times more powerful and possesses remarkable multimodal capabilities.
The name itself reveals much about its functionality:
MUM's primary objective is to reduce the number of searches users need to complete complex tasks by understanding context, nuance, and information across different formats and languages. According to Google, MUM can understand 75 different languages and can process text, images, and eventually video and audio simultaneously.
At its core, Google MUM is built on the T5 text-to-text framework, a variation of the transformer architecture that has revolutionized natural language processing. This architecture allows MUM to process and understand information in ways that previous algorithms couldn't.
Transformers represent a significant advancement in machine learning, particularly for natural language processing. Unlike recurrent neural networks (RNNs) that process data sequentially, transformers can process entire sequences of data simultaneously. This parallel processing capability, combined with attention mechanisms, enables transformers to better understand context and relationships between different parts of a query or content.
What truly sets MUM apart is its multimodal capabilities. While BERT and other previous algorithms were primarily text-based, MUM can understand and generate insights from:
| Modality | Examples | How MUM Processes It |
|---|---|---|
| Text | Articles, web pages, queries | Semantic understanding, context analysis |
| Images | Photos, infographics, diagrams | Object recognition, visual context analysis |
| Video | YouTube content, instructional videos | Scene understanding, action recognition |
| Audio | Podcasts, voice searches | Speech recognition, tonal analysis |
This multimodal capability enables MUM to understand queries and content at a deeper level, providing more comprehensive and helpful responses to user queries.
Another remarkable feature of MUM is its ability to understand and transfer knowledge across 75 different languages. This means MUM can find relevant information in one language and apply it to queries in another language, effectively breaking down language barriers in search.
While both MUM and BERT are built on transformer architecture, MUM represents a significant advancement over its predecessor. Here's how they compare:
| Feature | BERT | MUM |
|---|---|---|
| Power | Baseline | 1,000x more powerful |
| Languages | Primarily English-focused | 75 languages |
| Modalities | Text only | Text, images, video, audio |
| Tasks | Single-task focused | Multitasking capabilities |
| Context Understanding | Good | Exceptional |
| Query Complexity | Better with simple queries | Excels with complex queries |
BERT (Bidirectional Encoder Representations from Transformers) was groundbreaking when it was introduced in 2019, affecting roughly 10% of all Google searches. It improved Google's ability to understand natural language, particularly the context and relationship between words in a search query.
MUM builds on BERT's foundation but takes search intelligence to a new level. While BERT could understand the context of words in a sentence, MUM can understand concepts, generate connections between ideas, and process information across different formats and languages simultaneously.
Google MUM introduces several capabilities that fundamentally change how search works:
MUM excels at understanding complex, nuanced queries that would previously require multiple searches to resolve. For example, a query like "I've hiked Mt. Adams and now want to hike Mt. Fuji next fall, what should I do differently to prepare?" would traditionally require multiple separate searches about hiking gear, weather conditions, fitness requirements, and seasonal considerations.
MUM can understand the complexity of this query and provide comprehensive information that addresses all aspects of the comparison between the two mountains and the preparation differences.
One of MUM's most impressive abilities is connecting information that exists across different sources, formats, and languages. This means it can:
MUM can identify information gaps and suggest topics users might want to explore that they didn't explicitly ask about. For instance, when researching hiking preparation, MUM might suggest looking into altitude sickness prevention—something the user might not have considered in their original query.
The introduction of Google MUM has significant implications for SEO professionals and content creators:
MUM rewards comprehensive, expert content that thoroughly addresses topics from multiple angles. Shallow content that merely scratches the surface of topics will struggle to compete as MUM can identify content that demonstrates true expertise and depth.
With MUM's ability to understand different content formats, websites that leverage multiple media types (text, images, video, audio) to explain concepts will have an advantage. Creating complementary content across formats helps MUM understand the full context of your information.
Google's focus on Experience, Expertise, Authoritativeness, and Trustworthiness (e-e-a-t) becomes even more critical in the MUM era. As MUM better understands content quality and context, signals that demonstrate genuine expertise will carry more weight.
Traditional keyword research approaches need to evolve. Instead of focusing solely on specific keyword phrases, SEO professionals must think about:
Rather than optimizing for "best hiking boots for Mt. Fuji," content might need to address the broader context of Japanese mountain hiking conditions, seasonal considerations, and equipment comparisons.
One of the first practical applications of MUM was its integration with Google Lens. Users can now take a picture of an item and ask related questions about it. For example:
This multimodal search capability demonstrates how MUM connects visual information with textual queries to provide more helpful responses.
Google used MUM to address the complex information needs around COVID-19 vaccines. When users searched for information about different vaccines, MUM could:
This capability enabled Google to provide more comprehensive vaccine information and address information gaps, potentially reducing vaccine hesitancy.
Google's "Things to know" feature, powered by MUM, helps users explore different aspects of their search topics. For example, when searching for acrylic painting techniques, the feature might show:
| Category | Examples |
|---|---|
| Styles | Impasto, Glazing, Stippling |
| Materials | Canvas preparation, Brush selection, Palette setup |
| Learning path | Beginner exercises, Common mistakes, Advanced techniques |
This feature demonstrates MUM's ability to understand the different dimensions of a topic and present them in an organized way that helps users explore more deeply.
To ensure your website performs well in the age of Google MUM, consider implementing these strategies:
Create content that thoroughly addresses topics from multiple angles:
Structured data helps MUM better understand your content:
With MUM's multimodal capabilities, visual elements become increasingly important:
If you serve international audiences, MUM's language capabilities might affect your strategy:
MUM rewards websites that demonstrate comprehensive knowledge in specific areas:
Google MUM represents a significant milestone in search technology, but it's just one step in the ongoing evolution of search. Here's what we might expect in the coming years:
Search is becoming more conversational and context-aware. Future iterations will likely understand ongoing conversations, remembering previous queries and building on them to provide more personalized and relevant results.
As MUM continues to evolve, we'll see even more seamless integration between text, image, video, and audio search. Users might start a search with an image, refine it with text, and receive video results—all within a single search journey.
Google's helpful content update aligns perfectly with MUM's capabilities. The future of search will continue to prioritize content that genuinely helps users accomplish their goals rather than content created primarily for search engines.
MUM's ability to understand complex queries and identify information gaps positions Google to become more predictive, anticipating what users might need before they explicitly ask for it.
MUM is being gradually implemented across Google Search features rather than being released as a single massive update. Google has indicated they're taking a careful approach to introducing MUM capabilities to ensure accuracy and helpfulness.
No, MUM doesn't replace BERT. Rather, it builds upon BERT's foundation and works alongside it and other algorithms in Google's search system.
While MUM itself isn't a ranking algorithm, it will affect how Google understands content and user queries, which indirectly impacts rankings. Websites with comprehensive, multimodal, expert content are more likely to benefit from MUM's implementation.
Monitor changes in search traffic patterns, particularly for complex queries and questions. Pay attention to Google Search Console insights and track performance on queries that might benefit from MUM's understanding, such as comparison queries or complex questions.
While MUM understands 75 languages, its implementation and impact may vary across different languages and regions. English-language searches will likely see the most immediate effects, with other languages following as the technology matures.
As Google continues to refine and expand MUM's capabilities, staying informed about its development and implications will be crucial for SEO professionals and content creators. The evolution of search technology demands an evolution in our approach to creating and optimizing content—focusing less on technical tricks and more on genuinely helpful, comprehensive, and multimodal information delivery.
The future of search is about understanding—not just matching keywords but truly comprehending user needs and providing the most helpful information possible. With Google MUM, we're taking a significant step toward that future.
This article was written by Gaz Hall, a UK based SEO Consultant on 25th May 2022. Gaz has over 25 years experience working on SEO projects large and small, locally and globally across a range of sectors. If you need any SEO advice or would like me to look at your next project then get in touch to arrange a free consultation.
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