By Jayne Schultheis – Machine learning is a subset of artificial intelligence (AI) that involves giving computers the ability to learn by analyzing data and past experiences without explicit programming. Computers use algorithms to find patterns within and from data to solve problems and make predictions.

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‍In a typical day, the average person might use several different machine learning systems. These could be simple, utility-based applications, like turning on a Roomba vacuum to clean a room. Or you could use machine learning for more complex tasks, like asking ChatGPT, OpenAI's NLP chatbot, to give you a gluten-free recipe for chocolate chip cookies in the pirate vernacular. Yes, we tried that prompt today.

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You also can use machine learning to drive a content marketing campaign — from analyzing your competitors' content and your own to finding the best topics for content to crafting that content to rank well in online searches.

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Even though machine learning seems new and modern, it’s based on the work of scientists from as far back as the 1700s.

When did machine learning start?

The first instances of machine learning go back to 1763, when an essay was published on the work of Thomas Bayes, a British statistician and minister. Bayes showed a way to update the probability of something based on new data, laying the groundwork for machine learning.

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In the years after WWII, Alan Turing, considered the Father of Computer Science, applied this principle within the context of machines. He gave a 1947 lecture describing a “machine that can learn from experience.” Three years later, he developed his own learning machine, an ancestor of the modern computer. In 1952, Arthur Samuel wrote the first learning program for IBM. It involved a checkers game, and he later coined the term “machine learning” in 1959.

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Today, with advances like neural networks, machines can function similarly to the human brain. Machine learning models have evolved to the point where they can predict patterns in human behavior and recognize voices and faces.

What’s the difference between AI and machine learning?

The father of artificial intelligence, John McCarthy, defined AI as “the science and engineering of making intelligent machines, especially intelligent computer programs.” For McCarthy, AI could also be considered a way that computers assist humans in reaching a specific goal.

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Traditional programming involves coding rules and instructions for a computer to follow explicitly to perform a specific task. It relies on a programmer's understanding of the problem to craft algorithms and logic that dictate how the program behaves.

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Machine learning takes this concept a bit deeper. It’s a subset of AI that allows computers to learn from data and patterns without being directly programmed to do so. The machines can make predictions by observing datasets and teach themselves to get better at doing so. As a result of this automation, companies that use machine learning notice an uptick in human productivity.

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What are some examples of machine learning?

The Google algorithms are some of the most widely-used applications of machine learning techniques. Google trained its machines to scour all of the massive amounts of websites online, and recognize patterns. Its machine learning algorithms use natural language processing to break down the context of larger articles. That way, when you search for a term, Google provides relevant data placed in the proper context, rather than random pages stuffed with keywords.

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Some of its algorithms offer personalization with location tracking. For example, if you search for “Pizza near me,” the input from the algorithm has been trained to identify map patterns and locations. That way, it can predict the right place to send users.

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But that type of machine learning is pretty self-evident and has been around for decades now. What's notable is how the rapid proliferation of machine learning has brought it into almost every sector of commerce, science, and technology.

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In genomics, machine learning techniques aid in gene sequencing, identifying genetic mutations, and predicting disease susceptibility. In climate science, it is used to analyze climate data and predict weather patterns.

Rellify's unique use of machine learning

In content marketing, machine learning has created a revolution. Nearly every SaaS tool mentions that it uses AI technology to perform article topic selection, write articles, and carry out other functions. But while AI can help to make decisions, human intelligence still matters, as does the quality of data.

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That’s why Rellify’s machine learning process is so transformative. Rather than using generic website data from the entire Internet, Rellify has a new weapon: a custom Relliverse™. This in-house subject matter expert can research your business, industry, and competitors' offerings.

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Rellify then applies deep learning tools to this data, which is unique to your business and target audience. We build exclusive neural networks and deliver clusters of topics and keywords that provide a unique visualization of what's most relevant to your business and customers.

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Rellify's content marketing platform then enables writers and editors to craft engaging content, using AI tools every step of the way. Or, if you prefer, you can create an online marketing campaign with a few clicks and generate content automatically. What used to take weeks can be done, thanks to AI, in minutes.

Are there different types of machine learning?

Machine learning is only as good as your data set. So depending on the type of data you’re using, as well as your goals, you can break machine learning down into three general categories: supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning

Supervised machine learning uses data sets that are well-labeled with clear input and output variables. The machines apply this data to predict the output of new data, often with the help of support vector machines for classification, regression analysis, and identifying outliers. In this model, you give machines input and output variables since you have a sense of what results to expect from the data.

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‍Facial recognition technology is a popular application of supervised learning. Companies like Apple or Facebook have trained machines to identify faces. When you take a new picture, thus adding to a database of millions of faces, the machines can predict the identity with accuracy.

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If you’re using unlabeled data, unsupervised machine learning is a better tool for validation of your data set. Unsupervised learning uses algorithms to identify unlabeled clusters of data.

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One practical use of unsupervised learning would be the recommendation engines for online shopping or music services. Algorithms can identify big patterns in the data and implement segmentation and categorization. For example, people who like watching "Star Wars" movies might also like "The Mandalorian," versus a Jane Austen period piece. (Although it's true that many people might enjoy both.) Unsupervised learning is used in your social media feeds and to generate personalized product recommendations when you shop online.

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Reinforcement learning

Reinforcement learning is a dynamic process that incorporates a trial-and-error approach to train machines. Engineers and data scientists use it when they need to make a decision point and have several options to choose from. A room-cleaning robot uses reinforcement learning because once it bumps into one obstacle, it can choose several different directions based on the environment. The data set (the room layout) might constantly change, causing the machine to constantly adjust its trajectory.

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The Learning to Run project from deepsense.ai in collaboration with Stanford University is a more complex example of reinforcement learning. They created a computer model that had to learn all of the nuanced motions of an active runner. The machine learning tools had to follow a sequence of dynamic events to create movement like jumping. The successful project could lead to the design of better prosthetic devices to help real people walk.

Neural networks and deep learning

Deep learning is one of the most talked-about machine learning applications. We won’t dive deep into the details here, but we'll cover a few basics so you can get started.

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‍Deep learning is considered scalable because it is not as dependent on human intervention and can tackle large data sets. It’s a type of supervised machine-learning algorithm that consists of neural networks designed to mimic the human brain.  The “deep” part of deep machine learning comes from the multiple layers of neural networks.

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These artificial neural networks are a means for reinforcement learning. Like other forms of machine learning, they receive data, recognize patterns, and predict the outputs for similar data. They have a layer that receives data, a layer for output data, and several other connected layers where computation occurs.

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The ethical implications of machine learning

The use of machine learning (ML) raises several ethical implications, including issues related to bias, privacy, transparency, accountability, and fairness. Addressing these concerns will further the responsible development and use of ML systems.

Bias

ML models can inherit biases present in the data they are trained on. This, unfortunately, can lead to unfair or discriminatory outcomes against certain demographic groups. To address bias:

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• Consider the potential impacts of ML systems on different demographic groups.

• Conduct thorough bias assessments on training data and model outputs.

• Employ techniques such as fairness-aware learning algorithms to counter biases.

• Promote diversity and inclusivity in the teams developing and validating ML systems.

• Engage with marginalized communities and stakeholders to understand their concerns and perspectives.

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Privacy

ML systems often process large amounts of sensitive data, raising concerns about privacy infringement. Techniques like these can help protect individuals' privacy:

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• Use robust data protection measures, including encryption and access controls.

• Restrict data collection and retention to only what is necessary for the ML task.

• Obtain informed consent from individuals before using their data.

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Transparency, explainability, and accountability

Many ML algorithms, such as deep neural networks, are often complicated. This makes it challenging to understand their decision-making process. Lack of transparency can lead to distrust and hinder accountability. To combat this:

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• Use interpretable ML models when possible, such as decision trees or linear models.

• Provide explanations for model predictions.

• Document the entire ML pipeline, including data sources, preprocessing steps, and model architecture.

• Define clear roles and responsibilities for stakeholders involved in development and deployment.

• Conduct regular audits and evaluations of ML systems to monitor performance and compliance with ethical guidelines.

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Rellify and machine learning

Rellify uses deep learning to comb through large sets of data from around the Internet. Then, it provides a custom content roadmap, generative AI tools, and monitoring capabilities so you can seamlessly create and refine content that will resonate with your customer base.

With Rellify's unique AI capabilities, you can create and implement a content marketing plan that will boost your SEO. That means you'll get more (relevant) web traffic, leads, and loyal customers. Still curious about how a custom Relliverse™ works? We'd be happy to give you a demo, free of charge! Contact a Rellify expert today to find out how our groundbreaking platform expertly uses machine learning to maximize the returns on your marketing efforts.

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About the author

Jayne Schultheis has been in the business of crafting and optimizing articles for five years and has seen Rellify change the game since its inception. With strategic research, a strong voice, and a sharp eye for detail, she's helped many Rellify customers connect with their target audiences. The evergreen content she writes helps companies achieve long-term gains in search results Her subject expertise and experience covers a wide range of topics, including tech, finance, food, family, travel, psychology, human resources, health, business, retail products, and education. If you're looking for a Rellify expert to wield a mighty pen (well, keyboard) and craft real, optimized content that will get great results, Jayne's your person.