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Google MUM has the potential to be a game-changer in search and SEO
26 October 2021 (Edited )
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MUM is multilingual (75 languages) and multi-modal (both text and images) and has 1000X the question-answering power of BERT

The Google Multitask Unified Model (MUM) has the potential to reduce the number of queries a user needs to run to answer one specific question by a factor of 8.

It does that by using a natural language processing (NLP) model that Google research scientists have dubbed the T5: Text-to-Text Transfer Transformer.

The 11-billion-parameter T5 model reframes all NLP tasks into a text-to-text format in which inputs and outputs are always text strings, which makes the model usable for any NLP task including:

  • Machine translation
  • Document summarization
  • Question answering
  • Sentiment analysis
  • Regression analysis

In finding the best response to a query, MUM uses its AI-powered algorithm to search relevant materials in 75 languages, and returns a result in the language of the original query.

MUM can also accept as query input a combination of text and images - and Google plans also to train the model to search for information in audio and video recordings.

These multilingual and multi-media capabilities are what make the model able to reduce the number of individual queries needed to get sufficient information to answer the original query by a factor of 8.

Implications for SEO

  • Using keywords in a natural context becomes more important.
  • Understanding user intent and how people translate it into queries will be key.
  • Images - and later audio and video - will become more important content elements.
  • Competition will increase across industries, markets and products as MUM incorporates information from other countries and languages into query responses.
  • Even more than with BERT, effective technical SEO - including structured data markup that helps MUM understand your content - will give you an edge.

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Subhead MUM is multilingual (75 languages) and multi-modal (both text and images) and has 1000X the question-answering power of BERT
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