Dallas, TX, United States, 10/24/2021 / Agnes Zang /
Google’s John Mueller was recently asked about the number of searches that were being affected by the MUM algorithm. John said that he didn’t know the exact figure and then went on to explain that the MUM algorithm is multi-purpose and can be used in different contexts beyond just ranking.
Question About Google’s Application of MUM Algorithm
The MUM algorithm is quite impressive as it can search across multiple languages and can also use images to find an answer to a search query. So, it’s quite understandable that the person who asked the question wanted to know how much MUM was affecting the search results. John Mueller answered the question as best as he could and tried to put MUM into perspective without any hype or exaggeration.
This question was asked:
“A couple years ago Google noted that when it came to ranking results BERT would better understand and impact about ten percent of searches in the U.S.
My question is two-fold:
Has that percentage changed for BERT?
…What percentage is MUM expected to better understand and impact searches?”
How many searches does MUM affect?
John openly admitted that he didn’t know how many searches were getting affected by MUM and then proceeded to explain why it might be difficult to quantify the influence of MUM on the search results. John first answered the numbers for BERT and then addressed MUM. Here is what John said to answer the above-mentioned question:
“I have no idea…
I’m pretty sure that the percentage changed since then because everything is changing.
But I don’t know if we have a fixed number that goes for BERT or that goes for MUM.”
Mum is Like a Multi-purpose Machine Learning Library
John Mueller then opened up about MUM and said that it can be applied to a wide range of tasks that go well beyond search results.
“Mum, as far as I understand it is more like a multi-purpose machine learning library anyway.
So it’s something that can be applied to lots of different parts of search.
It’s not so much that you would isolate it to just ranking.
But rather you might be able to use it for understanding things on a very fine grained level and then that’s kind of interwoven in a lot of different kinds of search results.
But I don’t think we have any fixed numbers.”
The person who asked the initial question then asked another follow-up question that John answered with a non-hype description of MUM and portrayed MUM as doing things that aren’t necessarily as flashy as might appear from the outside.
The follow-up question:
“It seemed to me like it was going to open up more opportunities actually for different products or queries to be discovered.
It seemed like it was just sort of exponentially going to blow it out what one could learn.”
John Mueller’s response:
“I don’t know… we’ll see.
I think it’s always tricky to look at the marketing around machine learning algorithms, because it’s very easy to find …very exponential examples.
But that doesn’t mean that everything is as flashy as that.
…In talking with some of these search quality folks, they’re really happy with the way that these kinds of machine learning models are working.”
Mueller indicated that there are other tasks that MUM can perform and these tasks lie beyond the ranking part of Google’s algorithms and it can also play a role in other parts of the search.
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