How Search Engines Use Machine Learning: 9 Things We Know For Sure

Capable of autonomously learning and solving problems, general AI (AGI) takes machine learning to the next level.

This AI is powered by deep learning processes designed to mirror the human brain’s neural networks, allowing the algorithm to make decisions without instruction.

At the moment, artificial superintelligence (ASI) still lands fully in the category of science fiction.

This type of AI would, theoretically, be capable of outperforming human capabilities to solve the “unsolvable” problems of our time.

While companies like OpenAI and Conversion.ai are moving toward developing general AI for natural language processing, there are currently no clear-cut examples of AGI.

To progress from ANI to AGI, deep learning will be the key to creating stronger AI capable of using deductive reasoning to analyze complex, unstructured data and make independent decisions.

Back in 2016, Google declared its intention to become a “machine learning first” company. Since then, they’ve made steady strides toward that goal, launching Google AI in 2017 and rolling out BERT in 2019.

They want to not only make our lives easier but also use AI to find “new ways of looking at existing problems, from rethinking healthcare to advancing scientific discovery.”

Google has been making steady progress in the way it connects users to the content they’re searching for, including these nine ways we know search engines are using machine learning right now.

Low-quality content typically has distinct similarities, such as:

• The presence of several outbound links to unrelated pages.
• Lots of uses of stop words or synonyms.
• The occurrence rate of identified “spammy” keywords.

Machine learning recognizes these patterns and flags them.

Even though Google still uses human quality raters, utilizing machine learning to detect these patterns drastically cuts down on the amount of manpower necessary to review the content.

RankBrain is the machine learning algorithm developed by Google that not only helps identify patterns in queries, but also helps the search engine identify possible new ranking signals.

Before RankBrain, Google’s algorithm was coded entirely by hand. It depended on a team of engineers to analyze search query results, run tests to improve the quality of those results and implement the changes.

Google’s end goal is to use technology to provide users with a better experience. They don’t want to automate the entire process if that means the user won’t have the experience they are looking for.

So don’t assume machine learning will soon take over all search ranking; it is simply a small piece of the puzzle search engines have implemented to hopefully make our lives easier.

Google’s current privacy policies discuss how the search engine currently creates personalized search results based on a user’s behaviour.

Google’s personalized search patent states that:

“…personalized search generates different search results to different users of the search engine based on their interests and past behaviour.”

We can clearly see this in action. Often used in conference presentations, proving this process is as simple as typing a string of queries into Google in one sitting and seeing how the results change depending on what you last searched.

It’s important for a search engine to be able to recognize how similar one piece of text is to another. This applies not just to the words being used but also their deeper meaning.

Bidirectional Encoder Representations from Transformers – BERT, for short – is a natural learning processing framework that Google uses to better understand the context of a user’s search query.

People don’t always speak like a machine would expect them to. We play with language to come up with new turns of phrases.

BERT is designed to replicate human recognition as closely as possible to decode those contextual nuances by learning how users interact with the content and matching search queries with more relevant results.

As language develops and transforms, machines are better able to predict our meanings behind the words we say and provide us with better information.

Every second, approximately 1087 photos are uploaded to Instagram, and 4000 are uploaded to Facebook. That’s hundreds of millions of photos being uploaded to those two social networks alone every day.

Analyzing and cataloging that many submissions would be an arduous (if not impossible) task for a human, but it’s perfect for machine learning.

Machine learning analyzes color and shape patterns and pairs them with any existing schema data about the photograph to help the search engine understand what an image actually is.

Just like its organic search results, Google wants to provide the most relevant ads for its individual users. According to Google U.S. patents on ad quality, machine learning can be used to improve an “otherwise weak statistical model.”

This means that Ad Rank can be influenced by a machine learning system.

When you see search results that don’t include the keyword in the snippet, it’s likely due to Google using RankBrain to identify synonyms.

When searching for [forest preservation], you’ll see various results with the word “protection” as it can be used interchangeably with “preservation” in this case.

There are many reasons to fire up a search engine. Users may be searching to buy (transactional), research (informational), or find resources (navigational) for any given search.

By analyzing click patterns and the content type that users engage with (e.g., CTRs by content type) a search engine can leverage machine learning to determine the intent behind the user’s search.

While machine learning isn’t (and probably never will be) perfect, the more humans interact with it, the more accurate and “smarter” it will get.

This could be alarming to some, creating visions of Skynet from the “Terminator” movies.

However, the actual result may be a better experience with technology that solves complex problems and allows humans to focus on driving creativity and innovation