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Machine Learning Examples For The Real World

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Machine Learning Examples For The Real World

As an SEO professional, you’ve heard about ChatGPT and BARD – or even used them yourself.

You might also be familiar with a handful of machine learning models from Google, such as BERT and RankBrain.

These are all great applications of machine learning.

But it isn’t always immediately apparent how machine learning can be useful for your BAU (business as usual) tasks or daily SEO to-dos.

There’s also the question of ethics, as in data privacy and fairness, when it comes to using machine learning in your work.

However, understanding how machine learning works in search (and in real life) can only work to your advantage as an SEO pro – whether you’re technical or not.

In this comprehensive article, learn the types of machine learning and ethics associated with the technology, then take inspiration from seven machine learning examples that have had a great impact on our world.

As an added bonus, at the end of this article, explore eight machine learning examples directly applied to SEO.

What Is Machine Learning?

Machine learning is a subset of artificial intelligence (AI) which enables systems to learn and improve without the need to be explicitly programmed. This is achieved by using large data sets that train the parameters in the model.

Perhaps the most well-known examples of machine learning currently are ChatGPT and BARD – and while this post won’t be focusing on them, I decided to ask them both to explain to me what machine learning is. Here are their responses:

ChatGPT

“Machine learning is a type of artificial intelligence that allows software applications to learn from the data and become more accurate in predicting outcomes without explicit programming. In other words, it is a method of teaching computers to learn from the data they are given, and then to make decisions or predictions based on that learning.

The goal of machine learning is to develop algorithms that can learn patterns in data, and then use those patterns to make decisions or predictions about new data. This is done by training the machine learning algorithm on a dataset of known inputs and outputs, and then using that knowledge to make predictions on new, unseen data.

Some common applications of machine learning include image recognition, natural language processing, fraud detection, and recommendation systems.”

Screenshot from ChatGPT, April 2023

BARD

“Machine learning is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a subset of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.

Machine learning can be used for a variety of tasks, including:

  • Classification: Sorting data into categories. For example, a spam filter might classify emails as spam or not spam.
  • Regression: Predicting a continuous value. For example, a weather forecast might predict the temperature tomorrow.
  • Clustering: Grouping data points together based on their similarity. For example, a recommender system might cluster users together based on their past purchases.
  • Anomaly detection: Identifying unusual data points. For example, a fraud detection system might identify transactions that are likely to be fraudulent.

Machine learning is a powerful tool that can be used to solve a variety of problems. However, it is important to note that machine learning algorithms are only as good as the data they are trained on. If the data is biased, the algorithm will be biased as well. Additionally, machine learning algorithms can be susceptible to overfitting, which is when the algorithm learns the training data too well and does not generalize well to new data.

Despite these challenges, machine learning is a rapidly growing field with a wide range of applications. As the amount of data available to us continues to grow, machine learning is likely to play an even greater role in our lives in the future.”

BARD definition of machine learningScreenshot from BARD, April 2023

Supervised Learning

As the name suggests, in supervised learning, the model learns under supervision. It is trained with labeled data, and the output is manually reviewed.

The machine learns from the labeled data, which is then used to make future predictions.

Once the output is received, the model remembers it and will use it for the next operation.

Supervised learning visualImage from author, April 2023
Supervised predictive learning visualImage from author, April 2023

There are two main types of supervised learning: classification and regression.

Classification

Classification is when the output variable is categorical, with two or more classes that the model can identify; for example, true or false and dog or cat.

Examples of this include predicting whether emails are likely to be spam or whether an image is of a dog or cat.

In both of these examples, the model will be trained on data that is either classified as spam or not spam, and whether an image contains a dog or cat.

Regression

This is when the output variable is a real or continuous value, and there is a relationship between the variables. Essentially, a change in one variable is associated with a change that occurs in the other variable.

The model then learns the relationship between them and predicts what the outcome will be depending on the data it is given.

For example, predicting humidity based on a given temperature value or what the stock price is likely to be at a given time.

Unsupervised Learning

Unsupervised learning is when the model uses unlabeled data and learns by itself, without any supervision. Essentially, unlike supervised learning, the model will act on the input data without any guidance.

It does not require any labeled data, as its job is to look for hidden patterns or structures in the input data and then organize it according to any similarities and differences.

For example, if a model is given pictures of both dogs and cats, it isn’t already trained to know the features that differentiate both. Still, it can categorize them based on patterns of similarities and differences.

Unsupervised learning visualImage from author, April 2023

There are also two main types of unsupervised learning: clustering and association.

Clustering

Clustering is the method of sorting objects into clusters that are similar to each other and belong to one cluster, versus objects that are dissimilar to a particular cluster and therefore belong in another.

Examples of this include recommendation systems and image classifying.

Association

Association is rule-based and is used to discover the probability of the co-occurrence of items within a collection of values.

Examples include fraud detection, customer segmentation, and discovering purchasing habits.

Semi-supervised Learning

Semi-supervised learning bridges both supervised and unsupervised learning by using a small section of labeled data, together with unlabeled data, to train the model. It, therefore, works for various problems, from classification and regression to clustering and association.

Semi-supervised learning can be used if there is a large amount of unlabeled data, as it only requires a small portion of the data to be labeled to train the model, which can then be applied to the remaining unlabeled data.

Google has used semi-supervised learning to better understand language used within a search to ensure it serves the most relevant content for a particular query.

Reinforcement Learning

Reinforcement learning is when a model is trained to return the optimum solution to a problem by taking a sequential approach to decision-making.

It uses trial and error from its own experiences to define the output, with rewards for positive behavior and negative reinforcement if it is not working towards the goal.

The model interacts with the environment that has been set up and comes up with solutions without human interference.

Human interference will then be introduced to provide either positive or negative reinforcement depending on how close to the goal the output is.

Examples include robotics – think robots working in a factory assembly line – and gaming, with AlphaGo as the most famous example. This is where the model was trained to beat the AlphaGo champion by using reinforcement learning to define the best approach to win the game.

Machine Learning Ethics

There is no doubt that machine learning has many benefits, and the use of machine learning models is ever-growing.

However, it’s important to consider the ethical concerns that come with using technology of this kind. These concerns include:

  • The accuracy of a machine learning model and whether it will generate the correct output.
  • Bias in the data that is used to train models, which causes a bias in the model itself, and, therefore, a bias in the outcome. If there is historical bias in data, that bias will often be replicated throughout.
  • The fairness in outcomes and the overall process.
  • Privacy – particularly with data that is used to train machine learning models – as well as the accuracy of the outcomes and predictions.

7 Machine Learning Examples In The Real World

1. Netflix

Netflix uses machine learning in a number of ways to provide the best experience for its users.

The company is also continually collecting large amounts of data, including ratings, the location of users, the length of time for which something is watched, if content is added to a list, and even whether something has been binge-watched.

This data is then used to further improve its machine learning models.

Content Recommendations

TV and movie recommendations on Netflix are personalized to each individual user’s preferences. To do this, Netflix deployed a recommendation system that considers previous content consumed, users’ most viewed genres, and content watched by users with similar preferences.

Auto-generated Thumbnails

Netflix discovered that the images used on the browse screen make a big difference in whether users watch something or not.

It, therefore, uses machine learning to create and display different images according to a user’s individual preferences. It does this by analyzing a user’s previous content choices and learning the kind of image that is more likely to encourage them to click.

These are just two examples of how Netflix uses machine learning on its platform. If you want to learn more about how it is used, you can check out the company’s research areas blog.

2. Airbnb

With millions of listings in locations across the globe at different price points, Airbnb uses machine learning to ensure users can find what they are looking for quickly and to improve conversions.

There are a number of ways the company deploys machine learning, and it shares a lot of details on its engineering blog.

Image Classification

As hosts can upload images for their properties, Airbnb found that a lot of images were mislabeled. To try and optimize user experience, it deployed an image classification model that used computer vision and deep learning.

The project aimed to categorize photos based on different rooms. This enabled Airbnb to show listing images grouped by room type and ensure the listing follows Airbnb’s guidelines.

In order to do this, it retrained the image classification neural network ResNet50, with a small number of labeled photos. This enabled it to accurately classify current and future images uploaded to the site.

Search Ranking

To provide a personalized experience for users, Airbnb deployed a ranking model that optimized search and discovery. The data for this model came from user engagement metrics such as clicks and bookings.

Listings started by being ordered randomly, and then various factors were given a weight within the model – including price, quality, and popularity with users. The more weight a listing had, the higher it would be displayed in listings.

This has since been optimized further, with training data including the number of guests, price, and availability also included within the model to discover patterns and preferences to create a more personalized experience.

3. Spotify

Spotify also uses several machine learning models to continue revolutionizing how audio content is discovered and consumed.

Recommendations

Spotify uses a recommendation algorithm that predicts a user’s preference based on a collection of data from other users. This is due to numerous similarities that occur between music types that clusters of people listen to.

Playlists are one way it can do this, using statistical methods to create personalized playlists for users, such as Discover Weekly and daily mixes.

It can then use further data to adjust these depending on a user’s behavior.

With personal playlists also being created in the millions, Spotify has a huge database to work with – particularly if songs are grouped and labeled with semantic meaning.

This has allowed the company to recommend songs to users with similar music tastes. The machine learning model can serve songs to users with a similar listening history to aid music discovery.

Natural Language

With the Natural Processing Language (NLP) algorithm enabling computers to understand text better than ever before, Spotify is able to categorize music based on the language used to describe it.

It can scrape the web for text on a particular song and then use NLP to categorize songs based on this context.

This also helps algorithms identify songs or artists that belong in similar playlists, which further helps the recommendation system.

4. Detecting Fake News

While AI tools such as machine learning content generation can be a source for creating fake news, machine learning models that use natural language processing can also be used to assess articles and determine if they include false information.

Social network platforms use machine learning to find words and patterns in shared content that could indicate fake news is being shared and flag it appropriately.

5. Health Detection

There is an example of a neural network that was trained on over 100,000 images to distinguish dangerous skin lesions from benign ones. When tested against human dermatologists, the model could accurately detect 95% of skin cancer from the images provided, compared to 86.6% by the dermatologists.

As the model missed fewer melanomas, it was determined to have a higher sensitivity and was continually trained throughout the process.

There is hope that machine learning and AI, together with human intelligence, may become a useful tool for faster diagnosis.

Other ways image detection is being used in healthcare include identifying abnormalities in X-rays or scans and identifying key markups that may indicate an underlying illness.

6. Wildlife Security

Protection Assistant for Wildlife Security is an AI system that is being used to evaluate information about poaching activity to create a patrol route for conservationists to help prevent poaching attacks.

The system is continually being provided with more data, such as locations of traps and sightings of animals, which helps it to become smarter.

The predictive analysis enables patrol units to identify areas where it is likely animal poachers will visit.

8 Machine Learning Examples In SEO

1. Content Quality

Machine learning models can be trained to improve the quality of website content by predicting what both users and search engines would prefer to see.

The model can be trained on the most important insights, including search volume and traffic, conversion rate, internal links, and word count.

A content quality score can then be generated for each page, which will help inform where optimizations need to be made and can be particularly useful for content audits.

2. Natural Language Processing

Natural Language Processing (NLP) uses machine learning to reveal the structure and meaning of text. It analyzes text to understand the sentiment and extract key information.

NLP focuses on understanding context rather than just words. It is more about the content around keywords and how they fit together into sentences and paragraphs, than keywords on their own.

The overall sentiment is also taken into account, as it refers to the feeling behind the search query. The types of words used within the search help to determine whether it is classified as having a positive, negative, or neutral sentiment.

The key areas of importance for NLP are;

  • Entity – Words representing tangible objects such as people, places, and things that are identified and evaluated.
  • Categories – Text separated into categories.
  • Salience – How relevant the entity is.

Google has a free NLP API demo that can be used to analyze how text is seen and understood by Google. This enables you to identify improvements to content.

Recommendations In The World Of NLP

  • NLP is also being used to review and understand anchor text that is used to link pages. Therefore, it is more important than ever to ensure anchor text is relevant and informative.
  • Ensuring each page has a natural flow, with headings providing hierarchy and readability.
  • Answering the question the article is querying as quickly as possible. Ensure that users and search engines can discover key information without making too much effort.
  • Ensure you have the correct spelling and punctuation used to display authority and trustworthiness.

3. Google’s Models

AI and machine learning is used throughout Google’s many products and services. The most popular use of it in the context of search is to understand language and the intent behind search queries.

It’s interesting to see how things have evolved in search due to advancements in the technology used, thanks to machine learning models and algorithms.

Previously, the search systems looked for matching words only, which didn’t even consider misspellings. Eventually, algorithms were created to find patterns that identified misspellings and potential typos.

There have been several systems introduced throughout the last few years after Google confirmed in 2016 its intention to become a machine learning first company.

RankBrain

The first of these was RankBrain, which was introduced in 2015 and helps Google to understand how different words are related to different concepts.

This enables Google to take a broad query and better define how it relates to real-world concepts.

Google’s systems learn from seeing words used in a query on the page, which it can then use to understand terms and match them to related concepts to understand what a user is searching for.

Neural Matching

Neural matching was launched in 2018 and introduced to local search in 2019.

This helps Google understand how queries relate to pages by looking at the content on a page, or a search query, and understanding it within the context of the page content or query.

Most queries made today make use of neural matching, and it is used in rankings.

BERT

BERT, which stands for Bidirectional Encoder Representations from Transformers, launched in 2019 and is one of the most impactful systems Google has introduced to date.

This system enables Google to understand how combinations of words express different meanings and intent by reviewing the whole sequence of words on a page.

BERT is now used in most queries, as it helps Google understand what a user is looking for to surface the best results related to the search.

MUM

MUM, which means Multitask Unified Model, was introduced in 2021 and is used to understand languages and variations in search terms.

LaMBDA

Language Models for Dialog Application, or LaMDA for short, is the newest model and is used to enable Google to have fluid and natural conversations.

This uses the latest advancements to find patterns in sentences and correlations between different words to understand nuanced questions – and even predict which words are likely to come next.

4. Predictive Prefetching

By combining historical website data on user behavior with the capabilities of machine learning, some tools can guess which page a user is likely to navigate to next and begin prefetching the necessary resources to load the page.

This is known as predictive prefetching and can enhance website performance.

Predictive prefetching can also apply to other scenarios, such as forecasting pieces of content or widgets that users are most likely to view or interact with and personalizing the experience based on that information.

5. Testing

Running SEO A/B tests is one of the most effective ways to provide the SEO impact of changes, and the ability to generate statistically significant results is possible with the use of machine learning algorithms and neural networks.

SearchPilot is an example of SEO A/B testing that is powered by machine learning and neural network models.

Starting with a bucketing algorithm that creates statistically similar buckets of control and variant pages to perform tests on, a neural network model then forecasts expected traffic to the pages the test is being run on.

The neural network model, which is trained to account for any and all external influences such as seasonality, competitor activity, and algorithm updates, will also analyze the organic search traffic to the variant pages and identify how they perform against the control group throughout the test.

This also enables users to calculate whether any difference in traffic is statistically significant.

(Disclaimer: I work for SearchPilot.)

6. Internal Linking

Machine learning can help with internal linking in two ways:

  • Updating broken links: Machine learning can crawl your site to spot any broken internal links and then replace them with a link to the best alternative page.
  • Suggesting relevant internal linking: These tools can leverage big data to suggest relevant internal links during the article creation process and over time.

The other internal linking task is an internal link audit. This includes analyzing the number of internal links to a page, the placement of the links together with the anchor text, and the overall crawl depth of the page.

Anchor text classification can also be performed to identify the phrases used most frequently in alt text and categorize them based on topics and whether they are branded or non-branded terms.

7. Image Captioning For Alt Text

As SEO pros, we understand the importance of image alt text. They improve accessibility for people who use screen readers while also helping search engine crawlers understand the content of the page they are placed on.

Language vision models can be used to automatically caption images, therefore providing content that can be used as alt text. Image captioning is used to describe what is shown within an image in a single sentence.

Two models are used for image captioning, both as important as the other. The image-based model will start by extracting features from the image, while the language-based model will translate those features into a logical sentence.

An example of image captioning in the real world is the Pythia deep learning framework.

8. Other SEO tasks

Other articles worth reviewing focus on using deep learning to automate title tag optimization and intent classification using deep learning.

If you’re interested in how machine learning can be used in daily SEO tasks, this article by Lazarina Stoy is a must-read – and if you would like to play around with some super interesting scripts, this collection of Colab notebooks from Britney Muller is the perfect place to start.

In Conclusion

Machine learning isn’t limited to just ChatGPT and BARD.

There are many practical applications for machine learning, both in the real world and specifically in the world of SEO – and these are likely just the beginning.

And while it will be vital to remain cognizant of the ethical questions associated with machine learning, it has exciting implications for the future of SEO.

More resources:


Featured Image: Phonlamai Photo/Shutterstock



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How To Become an SEO Expert in 4 Steps

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General SEO

With 74.1% of SEOs charging clients upwards of $500 per month for their services, there’s a clear financial incentive to get good at SEO. But with no colleges offering degrees in the topic, it’s down to you to carve your own path in the industry.

There are many ways to do this; some take longer than others.

In this post, I’ll share how I’d go from zero to SEO pro if I had to do it all over again. 

1. Take a beginner SEO course

Understanding what search engine optimization really is and how it works is the first state of affairs. While you can do this by reading endless blog posts or watching YouTube videos, I wouldn’t recommend that approach for a few reasons:

  • It’s hard to know where to start
  • It’s hard to join the dots
  • It’s hard to know who to trust

You can solve all of these problems by taking a structured course like our SEO course for beginners. It’s completely free (no signup required), consists of 14 short video lessons (2 hours total length), and covers:

  • What SEO is and why it’s important
  • How to do keyword research
  • How to optimize pages for keywords
  • How to build links (and why you need them)
  • Technical SEO best practices

Here’s the first lesson to get you started:

Lesson 1: SEO Basics: What is SEO and Why is it Important? Watch now

2. Make a website and try to rank it

It doesn’t matter how many books you read about golf, you’re never going to win a tournament without picking up a set of clubs and practicing. It’s the same with SEO. The theory is important, but there’s no substitute for getting your hands dirty and trying to rank a site.

If you don’t have a site already, you can get up and running fairly quickly with any major website platform. Some will set you back a few bucks, but they handle SEO basics out of the box. This saves you time sweating the small stuff.

As for what kind of site you should create, I recommend a simple hobby blog. 

Here’s a simple food blog I set up in <10 minutes: 

A blog that I set up in just a few minutes. It's nothing special, but it does the jobA blog that I set up in just a few minutes. It's nothing special, but it does the job

Once you’re set-up, you’re ready to start practicing and honing your SEO skills. Specifically, doing keyword research to find topics, writing and optimizing content about them, and (possibly) building a few backlinks.

For example, according to Ahrefs’ Keywords Explorer, the keyword “neopolitan pizza dough recipe” has a monthly traffic potential of 4.4K as well as a relatively low Keyword Difficulty (KD) score:

Keyword metrics for "neopolitan pizza dough" via Ahrefs' Keywords ExplorerKeyword metrics for "neopolitan pizza dough" via Ahrefs' Keywords Explorer

Even better, there’s a weak website (DR 16) in the top three positions—so this should definitely be quite an easy topic to rank for.

Page from a low-DR website ranking in the top 3. This indicates an easy-to-rank-for keywordPage from a low-DR website ranking in the top 3. This indicates an easy-to-rank-for keyword

Given that most of the top-ranking posts have at least a few backlinks, a page about this topic would also likely need at least a few backlinks to compete. Check out the resources below to learn how to build these.

3. Get an entry-level job

It’s unlikely that your hobby blog is going to pay the bills, so it’s time to use the work you’ve done so far to get a job in SEO. Here are a few benefits of doing this: 

  • Get paid to learn. This isn’t the case when you’re home alone reading blog posts and watching videos or working on your own site.
  • Get deeper hands-on experience. Agencies work with all kinds of businesses, which means you’ll get to build experience with all kinds of sites, from blogs to ecommerce. 
  • Build your reputation. Future clients or employers are more likely to take you seriously if you’ve worked for a reputable SEO agency. 

To find job opportunities, start by signing up for SEO newsletters like SEO Jobs and SEOFOMO. Both of these send weekly emails and feature remote job opportunities: 

SEO jobs in SEOFOMO newsletterSEO jobs in SEOFOMO newsletter

You can also go the traditional route and search job sites for entry-level positions. The kinds of jobs you’re looking for will usually have “Junior” in their titles or at least mention that it’s a junior position in their description.

Junior SEO job listing exampleJunior SEO job listing example

Beyond that, you can search for SEO agencies in your local area and check their careers pages. 

Even if there are no entry-level positions listed here, it’s still worth emailing and asking if there are any upcoming openings. Make sure to mention any SEO success you’ve had with your website and where you’re at in your journey so far.

This might seem pushy, but many agencies actually encourage this—such as Rise at Seven:

Call for alternative roles from Rise at SevenCall for alternative roles from Rise at Seven

Here’s a quick email template to get you started:

Subject: Junior SEO position?

Hey folks,

Do you have any upcoming openings for junior SEOs?

I’ve been learning SEO for [number] months, but I’m looking to take my knowledge to the next level. So far, I’ve taken Ahrefs’ Beginner SEO course and started my own blog about [topic]—which I’ve had some success with. It’s only [number] months old but already ranks for [number] keywords and gets an estimated [number] monthly search visits according to Ahrefs.

[Ahrefs screenshot]

I checked your careers page and didn’t see any junior positions there, but I was hoping you might consider me for any upcoming positions? I’m super enthusiastic, hard-working, and eager to learn.

Let me know.

[Name]

You can pull all the numbers and screenshots you need by creating a free Ahrefs Webmaster Tools account and verifying your website.

4. Specialize and hone your skills

SEO is a broad industry. It’s impossible to be an expert at every aspect of it, so you should niche down and hone your skills in the area that interests you the most. You should have a reasonable idea of what this is from working on your own site and in an agency.

For example, link building was the area that interested me the most, so that’s where I focused on deepening my knowledge. As a result, I became what’s known as a “t-shaped SEO”—someone with broad skills across all things SEO but deep knowledge in one area.

T-shaped SEOT-shaped SEO
What a t-shaped SEO looks like

Marie Haynes is another great example of a t-shaped SEO. She specializes in Google penalty recovery. She doesn’t build links or do on-page SEO. She audits websites with traffic drops and helps their owners recover.

In terms of how to build your knowledge in your chosen area, here are a few ideas:

Here are a few SEOs I’d recommend following and their (rough) specialties:

Final thoughts

K Anders Ericsson famously theorized that it takes 10,000 hours of practice to master a new skill. Can it take less? Possibly. But the point is this: becoming an SEO expert is not an overnight process.

I’d even argue that it’s a somewhat unattainable goal because no matter how much you know, there’s always more to learn. That’s part of the fun, though. SEO is a fast-moving industry that keeps you on your toes, but it’s a very rewarding one, too. 

Here are a few stats to prove it:

  • 74.1% of SEOs charge clients upwards of $500 per month for their services (source)
  • $49,211 median annual salary (source)
  • ~$74k average salary for self-employed SEOs (source)

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A Year Of AI Developments From OpenAI

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A Year Of AI Developments From OpenAI

Today, ChatGPT celebrates one year since its launch in research preview.

From its humble beginnings, ChatGPT has continually pushed the boundaries of what we perceive as possible with generative AI for almost any task.

In this article, we take a journey through the past year, highlighting the significant milestones and updates that have shaped ChatGPT into the versatile and powerful tool it is today.

ChatGPT: From Research Preview To Customizable GPTs

This story unfolds over the course of nearly a year, beginning on November 30, when OpenAI announced the launch of its research preview of ChatGPT.

As users began to offer feedback, improvements began to arrive.

Before the holiday, on December 15, 2022, ChatGPT received general performance enhancements and new features for managing conversation history.

Screenshot from ChatGPT, December 2022ChatGPT At One: A Year Of AI Developments From OpenAI

As the calendar turned to January 9, 2023, ChatGPT saw improvements in factuality, and a notable feature was added to halt response generation mid-conversation, addressing user feedback and enhancing control.

Just a few weeks later, on January 30, the model was further upgraded for enhanced factuality and mathematical capabilities, broadening its scope of expertise.

February 2023 was a landmark month. On February 9, ChatGPT Plus was introduced, bringing new features and a faster ‘Turbo’ version to Plus users.

This was followed closely on February 13 with updates to the free plan’s performance and the international availability of ChatGPT Plus, featuring a faster version for Plus users.

March 14, 2023, marked a pivotal moment with the introduction of GPT-4 to ChatGPT Plus subscribers.

ChatGPT At One: A Year Of AI Developments From OpenAIScreenshot from ChatGPT, March 2023ChatGPT At One: A Year Of AI Developments From OpenAI

This new model featured advanced reasoning, complex instruction handling, and increased creativity.

Less than ten days later, on March 23, experimental AI plugins, including browsing and Code Interpreter capabilities, were made available to selected users.

On May 3, users gained the ability to turn off chat history and export data.

Plus users received early access to experimental web browsing and third-party plugins on May 12.

On May 24, the iOS app expanded to more countries with new features like shared links, Bing web browsing, and the option to turn off chat history on iOS.

June and July 2023 were filled with updates enhancing mobile app experiences and introducing new features.

The mobile app was updated with browsing features on June 22, and the browsing feature itself underwent temporary removal for improvements on July 3.

The Code Interpreter feature rolled out in beta to Plus users on July 6.

Plus customers enjoyed increased message limits for GPT-4 from July 19, and custom instructions became available in beta to Plus users the next day.

July 25 saw the Android version of the ChatGPT app launch in selected countries.

As summer progressed, August 3 brought several small updates enhancing the user experience.

Custom instructions were extended to free users in most regions by August 21.

The month concluded with the launch of ChatGPT Enterprise on August 28, offering advanced features and security for enterprise users.

Entering autumn, September 11 witnessed limited language support in the web interface.

Voice and image input capabilities in beta were introduced on September 25, further expanding ChatGPT’s interactive abilities.

An updated version of web browsing rolled out to Plus users on September 27.

The fourth quarter of 2023 began with integrating DALL·E 3 in beta on October 16, allowing for image generation from text prompts.

The browsing feature moved out of beta for Plus and Enterprise users on October 17.

Customizable versions of ChatGPT, called GPTs, were introduced for specific tasks on November 6 at OpenAI’s DevDay.

ChatGPT At One: A Year Of AI Developments From OpenAIScreenshot from ChatGPT, November 2023ChatGPT At One: A Year Of AI Developments From OpenAI

On November 21, the voice feature in ChatGPT was made available to all users, rounding off a year of significant advancements and broadening the horizons of AI interaction.

And here, we have ChatGPT today, with a sidebar full of GPTs.

ChatGPT At One: A Year Of AI Developments From OpenAIScreenshot from ChatGPT, November 2023ChatGPT At One: A Year Of AI Developments From OpenAI

Looking Ahead: What’s Next For ChatGPT

The past year has been a testament to continuous innovation, but it is merely the prologue to a future rich with potential.

The upcoming year promises incremental improvements and leaps in AI capabilities, user experience, and integrative technologies that could redefine our interaction with digital assistants.

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Is AI Going To E-E-A-T Your Experience For Breakfast? The LinkedIn Example

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Is AI Going To E-E-A-T Your Experience For Breakfast? The LinkedIn Example

Are LinkedIn’s collaborative articles part of SEO strategies nowadays?

More to the point, should they be?

The search landscape has changed dramatically in recent years, blurring the lines between search engines and where searches occur.

Following the explosive adoption of AI in content marketing and the most recent Google HCU, core, and spam updates, we’re looking at a very different picture now in search versus 12 months ago.

User-generated and community-led content seems to be met with renewed favourability by the algorithm (theoretically, mirroring what people reward, too).

LinkedIn’s freshly launched “collaborative articles” seem to be a perfect sign of our times: content that combines authority (thanks to LinkedIn’s authority), AI-generated content, and user-generated content.

What could go wrong?

In this article, we’ll cover:

  • What are “collaborative articles” on LinkedIn?
  • Why am I discussing them in the context of SEO?
  • The main issues with collaborative articles.
  • How is Google treating them?
  • How they can impact your organic performance.

What Are LinkedIn Collaborative Articles?

First launched in March 2023, LinkedIn says about collaborative articles:

“These articles begin as AI-powered conversation starters, developed with our editorial team, but they aren’t complete without insights from our members. A select group of experts have been invited to contribute their own ideas, examples and experiences within the articles.“

Essentially, each of these articles starts as a collection of AI-generated answers to FAQs/prompts around any given topic. Under each of these sections, community members can add their own perspectives, insights, and advice.

What’s in it for contributors? To earn, ultimately, a “Top Voice” badge on their profile.

The articles are indexable and are all placed under the same folder (https://www.linkedin.com/advice/).

They look like this:

Screenshot from LinkedIn, November 2023LinkedIn content

On the left-hand side, there are always FAQs relevant to the topic answered by AI.

On the right-hand side is where the contributions by community members get posted. Users can react to each contribution in the same way as to any LinkedIn post on their feed.

How Easy Is It To Contribute And Earn A Badge For Your Insights?

Pretty easy.

I first got invited to contribute on September 19, 2023 – though I had already found a way to contribute a few weeks before this.

Exclusive LinkedIn group of expertsScreenshot from LinkedIn, November 2023Exclusive LinkedIn group of experts

My notifications included updates from connections who had contributed to an article.

By clicking on these, I was transferred to the article and was able to contribute to it, too (as well as additional articles, linked at the bottom).

I wanted to test how hard it was to earn a Top SEO Voice badge. Eight article contributions later (around three to four hours of my time), I had earned three.

LinkedIn profileLinkedIn profile

Community top voice badgeScreenshots from LinkedIn, November 2023Community top voice badge

How? Apparently, simply by earning likes for my contributions.

A Mix Of Brilliance, Fuzzy Editorial Rules, And Weird Uncle Bob

Collaborative articles sound great in principle – a win-win for both sides.

  • LinkedIn struck a bullseye: creating and scaling content (theoretically) oozing with E-E-A-T, with minimal investment.
  • Users benefit from building their personal brand (and their company’s) for a fragment of the effort and cost this usually takes. The smartest ones complement their on-site content strategy with this off-site golden ticket.

What isn’t clear from LinkedIn’s Help Center is what this editorial mix of AI and human input looks like.

Things like:

  • How much involvement do the editors have before the topic is put to the community?
  • Are they only determining and refining the prompts?
  • Are they editing the AI-generated responses?
  • More importantly, what involvement (if any) do they have after they unleash the original AI-generated piece into the world?
  • And more.

I think of this content like weird Uncle Bob, always joining the family gatherings with his usual, unoriginal conversation starters. Only, this time, he’s come bearing gifts.

Do you engage? Or do you proceed to consume as many canapés as possible, pretending you haven’t seen him yet?

Why Am I Talking About LinkedIn Articles And SEO?

When I first posted about LinkedIn’s articles, it was the end of September. Semrush showed clear evidence of their impact and potential in Search. (Disclosure: I work for Semrush.)

Only six months after their launch, LinkedIn articles were on a visible, consistent upward trend.

  • They were already driving 792.5K organic visits a month. (This was a 75% jump in August.)
  • They ranked for 811,700 keywords.
  • Their pages were ranking in the top 10 for 78,000 of them.
  • For 123,700 of them, they appeared in a SERP feature, such as People Also Ask and Featured Snippets.
  • Almost 72% of the keywords had informational intent, followed by commercial keywords (22%).

Here’s a screenshot with some of the top keywords for which these pages ranked at the top:

Semrush US databaseScreenshot from Semrush US database, desktop, September 2023Semrush US database

Now, take the page that held the Featured Snippet for competitive queries like “how to enter bios” (monthly search volume of 5,400 and keyword difficulty of 84, based on Semrush data).

It came in ahead of pages on Tom’s Hardware, Hewlett-Packard, or Reddit.

LinkedIn computer hardware installation collaborative articleLinkedIn computer hardware installation collaborative article

collaborative article exampleScreenshots from LinkedIn, November 2023collaborative article example

See anything weird? Even at the time of writing this post, this collaborative article had precisely zero (0) contributions.

This means a page with 100% AI-generated content (and unclear interference of human editors) was rewarded with the Featured Snippet against highly authoritative and relevant domains and pages.

A Sea Of Opportunity Or A Storm Ready To Break Out?

Let’s consider these articles in the context of Google’s guidelines for creating helpful, reliable, people-first content and its Search Quality Rater Guidelines.

Of particular importance here, I believe, is the most recently added “E” in “E-E-A-T,” which takes experience into account, alongside expertise, authoritativeness, and trustworthiness.

For so many of these articles to have been ranking so well must mean that they were meeting the guidelines and proving helpful and reliable for content consumers.

After all, they rely on “a select group of experts to contribute their own ideas, examples and experiences within the articles,” so they must be worthy of strong organic performances, right?

Possibly. (I’ve yet to see such an example, but I want to believe somewhere in the thousands of pages these do exist).

But, based on what I’ve seen, there are too many examples of poor-quality content to justify such big rewards in the search engine results pages (SERPs).

The common issues I’ve spotted:

1. Misinformation

I can’t tell how much vetting or editing there is going on behind the scenes, but the amount of misinformation in some collaborative articles is alarming. This goes for AI-generated content and community contributions alike.

I don’t really envy the task of fact-checking what LinkedIn describes as “thousands of collaborative articles on 2,500+ skills.” Still, if it’s quality and helpfulness we’re concerned with here, I’d start brewing my coffee a little stronger if I were LinkedIn.

At the moment, it feels a little too much like a free-for-all.

Here are some examples of topics like SEO or content marketing.

misinformation example 1misinformation example 1

misinformation example 2misinformation example 2

misinformation example 3Screenshots from LinkedIn, November 2023misinformation example 3

2. Thin Content

To a degree, some contributions seem to do nothing more than mirror the points made in the original AI-generated piece.

For example, are these contributions enough to warrant a high level of “experience” in these articles?

thin content example 1thin content example 1

thin content example 2Screenshots from LinkedIn, November 2023thin content example 2

The irony to think that some of these contributions may have also been generated by AI…

3. Missing Information

While many examples don’t provide new or unique perspectives, some articles simply don’t provide…any perspectives at all.

This piece about analytical reasoning ranked in the top 10 for 128 keywords when I first looked into it last September (down to 80 in October).

Missing Information exampleScreenshot from LinkedIn, November 2023Missing Information example

It even held the Featured Snippet for competitive keywords like “inductive reasoning examples” for a while (5.4K monthly searches in the US), although it had no contributions on this subsection.

Most of its sections remain empty, so we’re talking about mainly AI-generated content.

Does this mean that Google really doesn’t care whether your content comes from humans or AI?

I’m not convinced.

How Have The Recent Google Updates Impacted This Content?

After August and October 2023 Google core updates (at the time of writing, the November 2023 Google core update is rolling out), the September 2023 helpful content update, and the October 2023 spam update, the performance of this section seems to be declining.

According to Semrush data:

Semrush data Screenshot from Semrush, November 2023Semrush data
  • Organic traffic to these pages was down to 453,000 (a 43% drop from September, bringing their performance close to August levels).
  • They ranked for 465,100 keywords (down by 43% MoM).
  • Keywords in the Top 10 dropped by 33% (51,900 vs 78,000 in September).
  • Keywords in the top 10 accounted for 161,800 visits (vs 287,200 in September, down by 44% MoM).

The LinkedIn domain doesn’t seem to have been impacted negatively overall.

Semrush dataScreenshot from Semrush, November 2023Semrush data

Is this a sign that Google has already picked up the weaknesses in this content and has started balancing actual usefulness versus the overall domain authority that might have propelled it originally?

Will we see it declining further in the coming months? Or are there better things to come for this feature?

Should You Already Be On The Bandwagon If You’re In SEO?

I was on the side of caution before the Google algorithm updates of the past couple of months.

Now, I’d be even more hesitant to invest a substantial part of my resources towards baking this content into my strategy.

As with any other new, third-party feature (or platform – does anyone remember Threads?), it’s always a case of balancing being an early adopter with avoiding over-investment. At least while being unclear on the benefits.

Collaborative articles are a relatively fresh, experimental, external feature you have minimal control over as part of your SEO strategy.

Now, we also have signs from Google that this content may not be as “cool” as we initially thought.

This Is What I’d Do

That’s not to say it’s not worth trying some small-scale experiments.

Or, maybe, use it as part of promoting your own personal brand (but I’ve yet to see any data around the impact of the “Top Voice” badges on perceived value).

Treat this content as you would any other owned content.

  • Follow Google’s guidelines.
  • Add genuine value for your audience.
  • Add your own unique perspective.
  • Highlight gaps and misinformation.

Experience shows us that when tactics get abused, and the user experience suffers, Google eventually steps in (from guest blogging to parasite SEO, most recently).

It might make algorithmic tweaks when launching updates, launch a new system, or hand out manual actions – the point is that you don’t know how things will progress. Only LinkedIn and Google have control over that.

As things stand, I can easily see any of the below potential outcomes:

  • This content becomes the AI equivalent of the content farms of the pre-Panda age, leading to Google clamping down on its search performance.
  • LinkedIn’s editors stepping in more for quality control (provided LinkedIn deems the investment worthwhile).
  • LinkedIn starts pushing its initiative much more to encourage participation and engagement. (This could be what makes the difference between a dead content farm and Reddit-like value.)

Anything could happen. I believe the next few months will give us a clearer picture.

What’s Next For AI And Its Role In SEO And Social Media?

When it comes to content creation, I think it’s safe to say that AI isn’t quite ready to E-E-A-T your experience for breakfast. Yet.

We can probably expect more of these kinds of movements from social media platforms and forums in the coming months, moving more toward mixing AI with human experience.

What do you think is next for LinkedIn’s collaborative articles? Let me know on LinkedIn!

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