John Mueller from Google gave one of the clearest and easiest to understand explanations on how Google uses machine learning in web search. He basically said Google uses it for “specific problems” where automation and machine learning can help improve the outcome. The example he gave was with canonicalization and the example clears things up.

This is from the Google webmaster hangout starting at 37:47 mark. The example is this “So for example we we use machine learning for canonicalization. So what that kind of means is we have all of those factors that we talked about before. And we give them individual weights. That’s kind of the traditional way to do it. And we say well rel canonical has this much weight and redirect has this much weight and internal linking has this much weight. And the traditional approach would be to say well we will just make up those weights, at those numbers and see if it works out. And if we see that things don’t work out we will tweak those numbers a little bit. And with machine learning what we can essentially do is say well this is the outcome that we want to have achieved and machine learning algorithms should figure out these weights on their own.”

This was the first part of the answer around how Google debugs its search algorithm.

Here is the full transcript of this part.

The question:

John’s full answer:

Here is the video embed:

Here is how Glenn Gabe summed it up on Twitter:

Forum discussion at Twitter.