searchenginejournal.com
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Learn more about artificialintelligence with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
A huge influx of capital by tech giants, especially search engines, means that AI computing power is making rapid advancements in a range of sectors from healthcare to construction to marketing and search engine optimization.
However, before we get into the implications of machine learning for SEO professionals, letâs define what we mean by AI.
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This type of AI is designed to perform specialized tasks that must be âtaughtâ to the algorithm (think Googleâs search algorithms).
While extremely specialized in scope, narrow AI (ANI) is able to quickly recognize patterns and perform tasks in a way that outpaces human ability.
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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 brainâs neural networks, allowing the algorithm to make decisions without instruction.
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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 âunsolvableâ problems of our time.
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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.
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Back in 2016, Google declared its intention to become a âmachine learning firstâ company. Since then, theyâve 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 ânew ways of looking at existing problems, from rethinking healthcare to advancing scientific discovery.â
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Google has been making steady progress in the way it connects users to the content theyâre searching for, including these nine ways we know search engines are using machine learning right now.
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Low-quality content typically has distinct similarities, such as:
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.
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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, Googleâs 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.
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Googleâs end goal is to use technology to provide users with a better experience. They donât want to automate the entire process if that means the user wonât have the experience they are looking for.
So donât 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.
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Googleâs current privacy policies discuss how the search engine currently creates personalized search results based on a userâs behaviour.
Googleâs personalized search patent states that:
ââŠpersonalized search generates different search results to different users of the search engine based on their interests and past behaviour.â
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.
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Itâs 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 â BERT, for short â is a natural learning processing framework that Google uses to better understand the context of a userâs search query.
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People donât 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.
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Every second, approximately 1087 photos are uploaded to Instagram, and 4000 are uploaded to Facebook. Thatâs 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 itâs 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.
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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 âotherwise weak statistical model.â
This means that Ad Rank can be influenced by a machine learning system.
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When you see search results that donât include the keyword in the snippet, itâs likely due to Google using RankBrain to identify synonyms.
When searching for [forest preservation], youâll see various results with the word âprotectionâ as it can be used interchangeably with âpreservationâ in this case.
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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 userâs search.
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While machine learning isnât (and probably never will be) perfect, the more humans interact with it, the more accurate and âsmarterâ it will get.
This could be alarming to some, creating visions of Skynet from the âTerminatorâ 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
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