Introduction
In May 2021, Google introduced a groundbreaking algorithm known as MUM—Multitask Unified Model—during its I/O conference. This update is more than just another ranking tweak; it’s a quantum leap in how Google understands search intent and delivers information. MUM is designed to understand complex queries, process information across multiple languages, and provide more intuitive, human-like responses. Let’s explore what MUM is, why it was needed, how it works, and the transformation it has brought to search.
What is MUM?
MUM (Multitask Unified Model) is a multimodal AI algorithm built on a transformer architecture similar to BERT but 1,000 times more powerful. It is capable of:
- Understanding language across 75+ languages
- Generating content (not just understanding it)
- Processing multiple modalities such as text, images, and eventually video and audio
- Performing multitasking: answering complex queries that require combining knowledge from multiple sources.
MUM is trained across many tasks at once, allowing it to develop a more nuanced understanding of both search queries and web content.
Why Was MUM Needed?
Prior to MUM, Google used BERT (2019), which significantly improved Google’s understanding of natural language. But even BERT had limitations in:
- Handling complex queries (e.g., “I’ve hiked Mt. Adams and want to hike Mt. Fuji next fall. What should I do differently?”)
- Understanding non-English content when the query is in English.
- Navigating multimodal content like images or maps with embedded text.
A Google study showed that people need an average of 8 queries to complete complex tasks. MUM aims to reduce this to one conversation.
The Evolution of Google’s Understanding Algorithms
| Year | Algorithm | Key Feature |
|---|---|---|
| 2015 | RankBrain | Machine learning-based understanding of query intent |
| 2019 | BERT | Natural language understanding at the sentence level |
| 2021 | MUM | Multimodal, multilingual, multitask transformer |
MUM isn’t just a ranking tool—it’s a knowledge integration engine.
How MUM Transformed Google Search
1. Multilingual Knowledge Transfer
MUM can learn from sources in other languages and apply that knowledge to English queries. For example, it can read research papers in Japanese and answer English-language questions using that content.
2. Multimodal Search
Google Lens integrated MUM to allow users to take a picture (e.g., of bike parts) and ask a question like, “How do I fix this?” This multimodal capability was impossible with earlier algorithms.
3. Fewer Queries, More Context
Instead of typing follow-up queries, MUM understands contextual layers within a single question. Google now recommends “Things to know” and deeper subtopics automatically.
4. Search Redesign Features Using MUM
- Things to Know
- Topic Zoom (e.g., zoom in to subtopics or out to broader concepts)
- Visual Explorations
- Video Moments (automatically suggest moments in videos based on intent)
Real-World Applications & Examples
📷 Google Lens + MUM: Snap a photo of a hiking boot and ask, “Can I use this for Mt. Fuji hike?”
🌐 Cross-language support: Use Polish articles to answer questions posed in Spanish.
🔍 Travel: “Best time to visit Kerala for houseboat tours” now leads to a rich blend of weather data, local news, and curated travel blogs.
Benefits of MUM
| Benefit | Description |
|---|
| 🔍 Deeper Search Understanding | Handles nuanced, multi-part questions |
| 🌎 Cross-Language Access | Breaks the language barrier in knowledge |
| 🎥 Multimodal Input | Processes text, image, and (soon) video/audio |
| 🧠 Knowledge Synthesis | Draws insights from multiple sources |
| ⏱️ Time Savings | Reduces the number of search iterations |
Genuine Statistics & Impact
1000x more powerful than BERT in terms of training parameters.
Covers 75 languages, improving global accessibility.
As per Google’s internal testing, MUM has shown a dramatic increase in precision for complex queries, though exact precision rates are proprietary.
Featured in Google Lens updates and Passage Ranking improvements since 2022.
Conclusion
Google’s MUM algorithm marks a pivotal moment in the evolution of search. By incorporating natural language understanding, multilingual capabilities, and multimodal learning, MUM is reshaping how we interact with the web. For marketers, content creators, and everyday users, the future of search is no longer keyword-based—it’s context-based, intent-focused, and rich in multidimensional intelligence.
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