The Google MUM Algorithm is capable of more than just ranking websites

Google’s John Mueller was asked how many search queries were affected by the MUM algorithm. John replied that he didn’t know and then explained that the Google MUM algorithm is multi-purpose and could be used for purposes other than ranking.

Question Regarding Google’s Use of MUM Technology

The MUM algorithm is impressive because it can search for answers across web documents in any language and can even include images in the search query.

So it’s understandable that the person who asked the question wanted to know how much MUM influenced the search results.

Google’s John Mueller responded to the question by attempting to put MUM into context without the use of hyperbole.

The following is the question that was posed:

A couple of 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?”

MUM Affects How Many Searches?

John Mueller admitted that he didn’t know how many searches MUM influenced and then explained why it might be difficult to quantify MUM’s impact on search results.

His response began with the numbers for BERT and then moved on to MUM.

John Mueller responded:

“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 similar to a multi-purpose machine learning library

John Mueller then elaborated on MUM, claiming that it can be applied to a wide range of tasks other than ranking.

He said:

“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.”

Google is pleased with MUM

The person who asked the question followed up with a follow-up question, to which John responded with a non-hype description of MUM, depicting it as doing things that aren’t necessarily as flashy as they may appear from the outside looking in.

The next question is:

“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 responded:

“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.”