Google is always up to something new and fascinating, and one of the things that they have been working on is the evolution of Google Search from machine learning to something more. Originally, a group of Googles engineers worked on the search engines recognition of synonyms. With users inputting different words interchangeable with one another, Google would implement its knowledge to understand better what they were searching for.
The next step came with the translation of websites, where the engineers fed the system with a large number of translated documents, teaching Google how one language is mapped to another. This way, Google was able to translate sites into languages that none of the engineers spoke. And now, the ultimate step in this evolution is deep learning.
What is deep learning?
Deep learning is based on the notion of digital neurons, which are organized into layers. Every layer takes features of a higher level one from the data that it receives, and passes them to the next layer. The result of this is that higher layers can understand the notions from the input data. Take images for example.
The first layer receives an input of pixels and is taught to recognize shapes from them. Higher layers then combine the shapes in order to comprehend the objects that are in the given picture. So, if the digital neurons are fed with thousands of images of faces, in the end, they will comprehend the concept and be able to recognize faces in any image.
The modern neural network
The amazing aspect of modern neural networks is that they dont need a human to teach them. The contemporary concept of unsupervised learning enables the machine to determine concepts behind the data that its given without any pre-determined labels.
In 2012, Google engineers used a neural network that consists out of 9 layers and are made out of 1 billion connections, in order to recognize faces from 10 million 200×200 pixel photos. The results were amazing there was a 15.8% accuracy for 22,000 object categories, such as faces, bodies, and the like. And there was no information previously given to the system.
Deep learning and Google
It all started in 2007 with Geoffrey Hinton and his creation of neural network systems. This lead to Jeff Dean and Andrew Ng to take up the process of building an enormous neural network in 2011. A year later, the results lead to the project changing the name from Google Brain to Deep Learning Project. What they focused on among other things was speech recognition and image recognition. Their results were implemented into Googles products.
In 2013, Google took control over DeepMind, an AI company from London. Their main goal is to create an AI machine that can process any given information, and then comprehend what to do with it next, similarly to the human brain. The latest Googles accomplishment in artificial intelligence is RankBrain, which has highly affected search engine optimization.
Googles RankBrain
RankBrain is Googles new deep learning approach. What it does is that it takes Googles pre-existing core algorithms and learns what combination is the best applied to every kind of search results. For example, it may determine that the most important factor for the given search results is the meta title. But, while this might lead to better search positioning for one website, for another something else might be the deciding factor, such as PageRank.
This basically means that Google has a specific mix of algorithms for every search result. Therefore, contemporary regression analysis of websites needs to be based on improving the specific part of a website based on those unique search results, because RankBrain works on keyword level and customizes every search result.
RankBrain and website quality
What RankBrain is also taught to do is determine the difference between high and low-quality websites. The same as it determines the algorithms differently for every search result, it checks the quality of the website based on different factors such as CRM, templates and data structures. What it does is that it learns what the correct settings for the particular environment are. It compares the structure of a website to one that is reputable in the industry and positions it accordingly.
RankBrain and backlinks
The way RankBrain works also has an effect on the backlinks you use. From what we have ascertained before, we can conclude that it is essential your backlinks are related to your niche because otherwise, RankBrain will notice there is a difference between the given website and others that are in your vertical.
The future of AI and SEO
As we can see from this article, the future of Googles RankBrain and other AI forms out there is quickly accelerating and it might eventually exceed the human brain. At this point, everything about the upcoming technical advances is left to speculation. What is certain is that:
- Every environment based on a competitive keyword will require its own examination process
- Almost every site will have to focus on their niche and be careful not to get misclassified
- Every website should look up to the structure and composition of renowned websites in the same industry
This does two things for search engine optimization. On one hand, it makes things easier because technologies like RankBrain make sure that there are no loopholes in the system. On the other hand, it makes things harder because everything about SEO will grow even more technical, in order to stay ahead of contemporary analytics and big data.
