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At Google I/O earlier last year, Google announced that it’s exploring a new technology called MUM (Multitask Unified Model) internally to help its ranking systems better understand language.
Dubbed “a new AI milestone for understanding information,” MUM is designed to make it easier for Google to answer complex needs in search.
Google promised MUM is 1,000 times more powerful than its NLP transfer learning predecessor, BERT.
It uses a model called T5, the Text-To-Text Transfer Transformer, to reframe NLP tasks into a unified text-to-text format and develop a more comprehensive understanding of knowledge and information.
According to Google, MUM can be applied to document summarization, question answering, and classification tasks such as sentiment analysis.
Clearly, MUM is a major priority inside the Googleplex – and something that important to the search team had better on the SEO industry’s radar, as well.
But is it a ranking factor in Google’s search algorithms?
The Claim: MUM As A Ranking Factor
Many who read the news about MUM when it was first revealed naturally wondered how it might impact search rankings (especially their own).
Google makes thousands of updates to its ranking algorithms each year and while the vast majority go unnoticed, some are impactful.
BERT is one such example.
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Read Full Story: https://www.searchenginejournal.com/ranking-factors/google-mum/
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