Tech giants are investing heavily in machine learning.
In 2019, Microsoft invested in 11 artificial intelligence (AI) startups, with $1 billion for OpenAI alone. And they aren’t even the biggest source of corporate venture capital flooding into AI startups.
In that same year, Intel Capital made 19 investments, and Google Ventures made 16 investments.
That huge influx of capital 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, let’s define what we mean by AI.
There are 3 types of AI:
- Narrow or Weak AI: This type of AI is designed to perform specialized tasks that must be “taught” to the algorithm (think Google’s 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.
- General or Strong AI: 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.
- Artificial Superintelligence: 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.
What’s their goal in going all-in on machine learning?
Well, according to Google, 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.”
Besides those lofty goals for the future, humanity is already seeing these machine learning advancements on a smaller scale in something we interact with every day – search engine algorithms.
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.
1. Pattern Detection
Search engines are using machine learning for pattern detections that help identify spam or duplicate content.
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. It also utilizes data from user interactions to detect when new spam structures and techniques are being used, recognize the new patterns, and successfully flag those, as well.
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.
This way, Google is able to automatically sift through pages to weed out low-quality content before an actual human has to get involved.
Machine learning is an ever-evolving technology, so the more pages that are analyzed, the more accurate it is (at least in theory).
2. Identification of New Signals
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.
Now, while there are still human engineers working on the algorithm, RankBrain quietly works in the background running tests and gauging how the changes affect user interactions.
RankBrain solves some of the tricky problems that Google used to face with traditional algorithms – including how to handle search terms that have never before been entered into Google.
According to Google’s Gary Illyes in a 2019 Reddit AMA:
“RankBrain is a PR-sexy machine learning ranking component that uses historical search data to predict what would a user [sic] most likely click on for a previously unseen query.”
As search engines are able to teach technology how to run predictions and data on their own, there can be less manual labor and employees can move toward other things machines can’t do, like innovation or human-centered projects.
3. It’s Weighted as a Small Portion
However, even though machine learning is slowly transforming the way search engines find and rank websites, it doesn’t mean it has a major, significant impact (currently) on our SERPs.
In a 2019 Webmaster Central Office Hours discussion, Google’s John Mueller references how machine learning helps Google’s engineers better understand various issues, but he’s careful to note that:
“…machine learning isn’t just this one black box that does everything for you where you feed the internet in on one side and the other side comes out search results.”
More recently, in a May 2021 Office Hours discussion, he explained that machine learning may adjust the weight of various ranking signals. But again, there are still real people manually checking and adjusting those values.
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.
4. Custom Signals Based on Specific Query
Google’s current privacy policies discuss how the search engine currently creates personalized search results based on a user’s behavior.
Google’s personalized search patent, US20050102282A1, states that:
“…personalized search generates different search results to different users of the search engine based on their interests and past behavior.”
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.
For instance, if I search [New York Football stadium] in an incognito browser, I get the answer [MetLife Stadium].
Next, if I search in the same browser for just [jets], Google assumes that because my last query was about a football stadium, then this query is also about football.
As I continue my search, Google learns when my interest starts to change.
Searching for [Jaguars] in the same browser will bring up information about the NFL team the Jacksonville Jaguars (which is related to my last two searches).
But the moment I start to search [zoo near San Diego] and type [zoo] in the query box, Google suggests [zoos with jaguars] even though I haven’t searched jaguars a second time.
Search history is just one component of the search experience that machine learning uses to provide better results.
5. Natural Language Processing
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 phrase.
We use the same word to describe different things. Sometimes, we’re even purposefully ambiguous.
However, as more people are using and searching new phrases online, machine learning is able to display more accurate information for those queries.
Google Trends is a great front-facing example of this. A new phrase or word that gains traction (e.g., “glow up” or “spill the tea”) may have nonsensical search results at first.
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.
6. Image Search to Understand Photos
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.
This is how Google is able to not only catalog images for Google Image search results but also powers its reverse image search, which allows users to search using an image instead of a text query.
Users can then find other instances of the photo online, as well as similar photographs that have the same subjects or color palette and information about the subjects in the photo.
In turn, the way the user interacts with these results can shape their SERPs in the future.
7. Ad Quality & Targeting Improvements
Just like its organic search results, Google wants to provide the most relevant ads for its individual users. According to Google U.S. patents US20070156887 and US9773256 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.
“Bid amount, your auction-time ad quality (including expected clickthrough rate, ad relevance, and landing page experience), the Ad Rank thresholds, the context of the person’s search” gets fed into the system on a keyword-by-keyword basis, to determine what thresholds are considered by Google for each keyword.
8. Synonyms Identification
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.
Google even highlights the synonyms in some cases, further indicating that it’s recognizing the synonyms.
9. Query Clarification
One of my favorite subjects is search query user intent.
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.
Furthermore, a single keyword could be useful to one or any of these intents.
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.
An example can be seen with the query “best colleges” in a Google search.
The results are reviews and a list of colleges all in one SERP, with the universities listed at the top. This demonstrates Google’s understanding of the possible intents behind the search.
This is changing how SEOs look at link structure and placement as Google’s algorithm uses tools like BERT to get better and better at evaluating the context of where those links are placed.
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.
In 2018, Pew Research conducted a poll in which 63% of respondents said that they are hopeful for the future of humanity as it relates to AI – agreeing that by 2030, humans will be better off with the help of artificial intelligence.
One way we’re already seeing that enhancement to quality of life is with search. As Google and other search engines revolutionize machine learning, we’re able to more easily find the information and services we need, when we need it.
More Machine Learning Resources:
- A Beginner’s Guide to SEO in a Machine Learning World
- How Machine Learning in Search Works: Everything You Need to Know
All screenshots taken by author, June 2021
How do search engines use machine learning? ›
Search engines are using machine learning for pattern detections that help identify spam or duplicate content. 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.What is 10 times rule in machine learning? ›
The most common way to define whether a data set is sufficient is to apply a 10 times rule. This rule means that the amount of input data (i.e., the number of examples) should be ten times more than the number of degrees of freedom a model has. Usually, degrees of freedom mean parameters in your data set.How does Google search use machine learning? ›
Google told us neural matching helps Google understand how queries relate to pages by looking at the entire query or content on the page and understanding it within the context of that page or query.How does a search engine work short answer? ›
Search engines work by crawling hundreds of billions of pages using their own web crawlers. These web crawlers are commonly referred to as search engine bots or spiders. A search engine navigates the web by downloading web pages and following links on these pages to discover new pages that have been made available.What are the 10 uses of search engine? ›
- With search engine, you can search for information from the World Wide Web (WWW).
- It is used to store information for future use.
- It is used to keep records.
- Search engine is used to get accurate details/information.
- It is used to create awareness.
Google uses machine learning algorithms to provide its customers with a valuable and personalized experience. Gmail, Google Search and Google Maps already have machine learning embedded in services.What is the golden rule of machine learning? ›
Golden rule of machine learning: – The test data cannot influence training the model in any way. Fundamental trade-off: – Trade-off between getting low training error and having training error approximate test error.What is the first rule of machine learning? ›
Rule #1: Don't be afraid to launch a product without machine learning. Machine learning is cool, but it requires data. Theoretically, you can take data from a different problem and then tweak the model for a new product, but this will likely underperform basic heuristics.What are first order rules in machine learning? ›
variables — e.g. A, B, C. predicate symbols — e.g. male, father (True or False values only) function symbols — e.g. age (can take on any constant as a value) connectives — e.g. ∧, ∨, ¬, →, ←
Search engines can be used to:
- To carry out research.
- To search for information about peoples, places and products.
- Get the definition of words, acronyms, etc.
- Download applications from the internet.
- To lookup other websites.
How does Google use AI for search? ›
The biggest way search engines use AI is to rank webpages, videos, and other content in search results. Google (and other search engines) rely on complex AI to determine how content gets ranked.
Explanation: Recursive best-first search will mimic the operation of standard best-first search, but using only the linear space. 7. Which method is used to search better by learning? Explanation: This search strategy will help to problem solving efficiency by using learning.How does a search engine works step by step? ›
Search engines work by simply crawling billions of pages using the web crawlers they have developed. These are commonly referred to as search engine spiders or bots. A search engines spider then navigates the web by following links on a new web page it discovers to find new pages and so forth.How does Google get its answers? ›
We continuously map the web and other sources to connect you to the most relevant, helpful information. We present results in a variety of ways, based on what's most helpful for the type of information you're looking for.How search engines work using a 3 step process? ›
There are three basic steps a search engine takes when searching for content: crawling, indexing, and ranking.What are the 5 most commonly used in search engine? ›
- 1 The Best Search Engine in The World: Google.
- 2 Search Engine #2. Bing.
- 3 Search Engine #3. Baidu.
- 4 Search Engine #4.Yahoo!
- 5 Search Engine #5. Yandex.
- 6 Search Engine #6. Ask.
- 7 Search Engine #7. DuckDuckGo.
- 8 Search Engine #8. Naver.
PageRank (PR) is an algorithm used by Google Search to rank web pages in their search engine results. It is named after both the term "web page" and co-founder Larry Page.Which search engine is used for studying and learning? ›
Google Scholar is the clear number one when it comes to academic search engines. It's the power of Google searches applied to research papers and patents. It not only let's you find research papers for all academic disciplines for free, but also often provides links to full text PDF file.
What machine learning algorithm does Google use? ›
RankBrain is basically a deep neural network that is helpful in providing the required search results. It is one of the factors in the Google Search algorithm that determines which search pages are displayed.What is the 80/20 rule in machine learning? ›
The 80-20 rule, also known as the Pareto Principle, is a familiar saying that asserts that 80% of outcomes (or outputs) result from 20% of all causes (or inputs) for any given event. In business, a goal of the 80-20 rule is to identify inputs that are potentially the most productive and make them the priority.Which coding style is appropriate for machine learning? ›
For instance, most of the machine learning engineers prefer to use Python for NLP problems while also preferring to use R or Python for sentiment analysis tasks, and some are likely to use Java for other machine learning applications like security and threat detection.What is the number 1 golden rule? ›
“Do unto others as you would have them do unto you.” This seems the most familiar version of the golden rule, highlighting its helpful and proactive gold standard.What are the 7 steps of machine learning? ›
- Data Collection. → The quantity & quality of your data dictate how accurate our model is. ...
- Data Preparation. → Wrangle data and prepare it for training. ...
- Choose a Model. ...
- Train the Model. ...
- Evaluate the Model. ...
- Parameter Tuning. ...
- Make Predictions.
- Supervised Learning.
- Unsupervised Learning.
- Semi-Supervised Learning.
- Reinforced Learning.
Q-learning is a model-free, off-policy reinforcement learning that will find the best course of action, given the current state of the agent. Depending on where the agent is in the environment, it will decide the next action to be taken.What is descriptive rule learning in machine learning? ›
Definition. Supervised descriptive rule induction (SDRI) is a machine learning task in which individual patterns in the form of rules (see Classification rule) intended for interpretation are induced from data, labeled by a predefined property of interest.What is explanation-based learning in machine learning? ›
Explanation-Based Learning (EBL)
In terms of Machine Learning, it is an algorithm that aims to understand why an example is a part of a particular concept to make generalizations or form concepts from training examples. For example, EBL uses a domain theory and creates a program that learns to play chess.
The biggest way search engines use AI is to rank webpages, videos, and other content in search results. Google (and other search engines) rely on complex AI to determine how content gets ranked.
How is artificial intelligence used in search engines? ›
Google and other search engines rely on complex AI to determine how content gets ranked. The algorithms used by these AI systems have many rules that prioritize different factors, from the types of keywords in your content to your site's user experience.Does SEO use machine learning? ›
Search engines use sophisticated AI, machine learning, and deep learning to process a search query, then predict which results will satisfy any given search. As any SEO expert following Google algorithm updates knows, search engines don't reveal exactly how their AI systems work, but do give clues.How machine learning is used in Google assistant? ›
Using eight machine learning models together, the algorithm can differentiate intentional interactions from passing glances in order to accurately identify a user's intent to engage with Assistant. Once within 5ft of the device, the user may simply look at the screen and talk to start interacting with the Assistant.How does a search engine work step by step? ›
Search engines work by crawling billions of pages using web crawlers. Also known as spiders or bots, crawlers navigate the web and follow links to find new pages. These pages are then added to an index that search engines pull results from. Understanding how search engines function is crucial if you're doing SEO.How to build search engine machine learning? ›
Following is the step by step procedure for building the search engine: 1) Collect data from WWW using web crawler. 2) Perform data cleaning using NLP. 3) Study and compare the existing page ranking algorithm. 4) Merge the selected page rank algorithm with current technologies in machine learning.