How does machine learning work and what can it do?

Machine learning is a branch of artificial intelligence where computer models learn from data to make predictions or decisions without explicit programming. It’s not only interesting for science and IT companies like Google or Microsoft. The world of online marketing can also change through developments in artificial intelligence.

What is machine learning?

Machines, computers, and programs traditionally follow predefined instructions: “If A happens, then do B.” But expectations for modern systems are rising, and developers can’t anticipate every possible scenario or pre-program every solution. That’s why today’s software needs to make independent decisions and react appropriately to unfamiliar situations. To achieve this, algorithms are used that allow programs to learn. First, they are provided with data; then, they analyze it to recognize patterns and form connections. This process is at the heart of machine learning.

In the context of self-learning systems, related terms often appear that should be understood to gain a better understanding of machine learning.

Artificial Intelligence

Research into artificial intelligence (AI) focuses on creating machines that can act in ways similar to humans. Computers and robots are designed to analyze their environment and make the best possible decisions. By our standards, this would mean behaving intelligently—but that raises a challenge: we don’t even have a clear definition for measuring our own intelligence. At present, AI—at least as it currently exists—cannot replicate a complete human being, including emotional intelligence. Instead, it focuses on specific capabilities to solve targeted tasks, a concept known as weak artificial intelligence.

Since 2022, systems with generative AI like the AI Assistant, such as ChatGPT, have gained significant importance. These are based on transformer models that are capable of generating text, images, or code from massive amounts of data. However, they remain specialized systems that do not possess true general intelligence.

Neural networks

A branch of artificial intelligence research, neuroinformatics, seeks further to design computers modeled after brains. It views nervous systems in an abstract way—stripped of their biological properties and limited purely to functionality. Artificial neural networks are primarily not an actual manifestation, but rather mathematical, abstract procedures. A network of neurons (mathematical functions or algorithms) is formed that can handle complex tasks like a human brain. The connections between neurons vary in strength and can adapt to problems.

The advancement of neural networks has led to the rise of deep learning. These are complex neural networks with many layers, which dominate today.

Big data

The term “Big Datainitially simply describes the massive volumes of data. However, there is no defined point at which we stop referring to data as just data and start calling it big data. This phenomenon has gained increased media attention in recent years due to the source of this data: In many cases, the deluge of information comes from user data (interests, movement profiles, vital data) collected by companies like Google, Amazon, or Facebook to better tailor their offerings to customers.

Such large data volumes can no longer be satisfactorily analyzed by traditional computer systems: Conventional software can only find what users look for. Therefore, self-learning systems are needed to uncover previously unknown relationships.

Data mining

Data mining refers to the analysis of big data. Simply collecting data doesn’t hold much value on its own. The information becomes valuable when you extract and analyze relevant features—similar to gold prospecting. Data mining differs from machine learning in that the former focuses on finding new patterns, while the latter emphasizes applying recognized patterns. Methods in data mining include cluster analyses, decision trees, regression methods, and association analyses. Today, data mining is often part of business intelligence systems or used in predictive analytics to predict customer behavior or market trends.

Comparison of machine learning methods

In general, developers distinguish between supervised learning, unsupervised learning, and deep learning. The algorithms used in these methods vary significantly.

Supervised learning

In supervised learning, the system is supplied with examples. Developers specify the value that each piece of information should have, such as whether it belongs in category A or B. The self-learning system then draws conclusions, recognizes patterns, and can handle unknown data better. The goal is to continually minimize the error rate.

A well-known example of supervised learning is spam filters: Based on certain characteristics, the system decides whether the email lands in the inbox or is moved to the spam folder. If the system makes a mistake, you can manually adjust it, which will influence the filter’s future calculations. This way, the software delivers increasingly better results.

Unsupervised learning

In unsupervised learning, the program tries to identify patterns on its own. For example, it can use clustering where an element is selected from the data set, analyzed for its features, and then compared to those already examined. If equivalent elements have been examined, the current object is added to them. If not, it is stored separately.

Systems based on unsupervised learning are implemented in neural networks, among other things. Examples can be found in network security: A self-learning system identifies abnormal behavior. Since, for instance, a cyberattack cannot be assigned to any known group, the program can detect the threat and raise an alarm.

In addition to these two main directions, there are also semi-supervised learning, reinforcement learning, active learning, and self-supervised learning. These methods belong to supervised learning and differ in the type and extent of user involvement. Particularly relevant today is self-supervised learning, where systems generate learning tasks themselves—without user involvement.

Deep learning

Unlike classical machine learning algorithms such as decision trees or support vector machines, deep learning uses multi-layered neural networks to process more complex data sets. These are complex because they involve natural information—for example, those found in speech, handwriting, or face recognition. Natural data is easy for humans to process but challenging for a machine, as it is difficult to quantify mathematically.

Deep learning and artificial neural networks are closely related. The way neural networks are trained can be described as deep learning. It is called “deep” because the network of neurons is organized in multiple hierarchical layers. It starts at the first layer with a set of input neurons. These neurons receive the data, begin analyzing it, and send their results to the next neural node. In the end, the increasingly refined information reaches the output layer, and the network produces a value. The often numerous layers between input and output are called hidden layers.

How does machine learning work for marketing?

Machine learning already plays an important role in marketing today. However, currently, it is primarily large companies that use these technologies internally, with Google leading the way. Self-learning systems are still so new that they cannot be purchased as off-the-shelf solutions. Instead, major internet providers develop their own systems and thus act as pioneers in this field. Since some, despite commercial interests, pursue an open-source approach and collaborate with independent research, developments in this area are accelerating.

Data analysis and forecasting

Marketing, alongside its creative side, also has a strong analytical dimension. Statistics on customer behavior are a key factor in determining which advertising measures to take. In general, the larger the data set, the more valuable insights can be drawn from it. Self-learning computer programs can detect patterns and generate reliable predictions—something humans, who naturally approach data with bias, can do only to a limited extent.

Analysts typically approach measurement data with certain expectations. These biases are nearly unavoidable for humans and often cause distortions. The larger the data sets, the more significant the deviations might be. Although intelligent machines can also have biases because they were unintentionally trained by humans, they tend to be more objective with hard facts. As a result, machines usually provide more meaningful analyses.

Visualization

Self-learning systems also improve and simplify the presentation of analysis results: Automated Data Visualization is the technique where the computer autonomously selects the appropriate representation of data and information. This is particularly important for people to understand what the machine has discovered and forecasted. In the vast data flood, it becomes challenging to present measurement results manually. Therefore, visualization also needs to rely on the computer’s calculations.

Personalization and generative design

Machine learning can also impact content creation—keyword: generative design. Instead of designing the same customer journey for everyone, dynamic systems based on machine learning can create individualized experiences. The content displayed on a website is still provided by writers and designers, but the system assembles the components specifically for the user. Self-learning systems are now also used to design or write content themselves: With Project Dreamcatcher, for example, it’s possible to have machines design components. LLMs like ChatGPT can also create website texts tailored to user groups.

Intelligent chatbots and language processing

Machine learning can also be used to optimize chatbots. Many companies already use programs that handle part of the customer support via a chatbot. However, in many cases, users quickly become annoyed with conventional bots. A chatbot based on a self-learning system with good speech recognition (Natural Language Processing), on the other hand, can give customers the feeling of actually communicating with a person—and thus pass the Turing Test.

Personalized recommendations

Amazon and Netflix demonstrate another important development in machine learning for marketers: recommendations. A major factor in these companies’ success is predicting what a user might want next. Based on collected data, self-learning systems can recommend additional products to the user. What used to be possible only on a large scale (“Our customers liked Product A, so most will probably like Product B too.”) is now also possible on a smaller scale with modern programs (“Customer X enjoyed Products A, B, and C, so she’ll likely also enjoy Product D.”).

In summary, it can be stated that self-learning systems will influence online marketing in four important areas:

• Volume: Programs that operate with machine learning and have been well-trained can process massive amounts of data and thus make predictions for the future.
• Speed: Analyses take time—if they have to be done manually. Self-learning systems increase work speed, allowing for quicker reactions to changes.
• Automation: With machine learning, it’s easier to automate processes. Since modern systems can independently adapt to new situations using machine learning, even complex automation processes are possible.
• Individuality: Computer programs can manage countless customers. Because self-learning systems capture and process data from individual users, they can also provide personalized advice.

Other areas of application for self-learning systems

But it’s not just marketing that increasingly relies on machine learning today. Self-learning systems are entering many other areas of our lives. In some cases, they assist in science and technology by further advancing progress. In other instances, they are used simply in the form of various gadgets, big and small, to make our daily lives easier. The presented areas of application are merely examples. It’s expected that machine learning will influence our entire lives in the not-too-distant future.

Science

What applies to marketing holds even greater significance in the natural sciences. The intelligent processing of Big Data is tremendously helpful for empirically working scientists. Particle physicists, for instance, use self-learning systems to capture, process, and identify deviations in much more measurement data. But machine learning also aids in medicine: Some doctors are already using artificial intelligence for diagnosis and treatment. Furthermore, machine learning is used for prognosis, such as predicting diabetes or heart attacks.

Robotics

Robots are now ubiquitous, especially in factories. They assist with mass production to automate consistent work steps. However, they often have little to do with intelligent systems, as they are only programmed for the specific task they perform. When self-learning systems are used in robotics, these machines should also be able to tackle new tasks. These developments are, of course, very interesting for other areas as well: From space exploration to household use, robots with artificial intelligence will be employed in numerous fields.

Traffic

A major showcase for machine learning is self-driving cars. Vehicles can only navigate independently and safely through real traffic—not just test tracks—thanks to machine learning. It’s not feasible to program all possible situations. Therefore, it’s essential that cars designed for self-navigation rely on intelligent machines. Self-learning systems are also revolutionizing traffic beyond individual transportation methods: Intelligent algorithms, such as artificial neural networks, can analyze traffic and develop more effective traffic management systems, like intelligent traffic light controls.

Internet

Machine learning already plays a major role on the internet. One example is spam filters: By continuously learning, filters improve in detecting unwanted emails and more reliably keep spam out of the inbox. The same applies to the intelligent defense against viruses and malware, which better protects computers from harmful software. Also, ranking algorithms of search engines—most notably Google’s RankBrain—are self-learning systems. Even when the algorithm doesn’t understand the user’s input (because no one has searched for it before), it can make an educated guess about what might match the query.

Personal assistants

Even in our own homes, continuously learning computer systems are becoming increasingly important. This transforms simple homes into smart homes. For example, Moley Robotics is developing an intelligent kitchen equipped with robotic arms to prepare meals. Personal assistants like Google Home and Amazon Echo that can control parts of the home use machine learning technologies to understand users as best as possible. Meanwhile, many people carry their assistants with them at all times; with Siri, Cortana, or Google Assistant, users can use voice control to send commands and questions to their smartphones.

Games

Since the beginning of research surrounding artificial intelligence, the ability of machines to play games has been a major driver for researchers. In chess, checkers, or Go (perhaps the most complex board game in the world) from China, self-learning systems have competed against human opponents. Video game developers also use machine learning to make their games more engaging. Game designers can employ machine learning to create balanced gameplay and ensure that computer opponents intelligently adapt to the behavior of human players.

The history of self-learning systems

Robots and automatons have fascinated humanity for several centuries. The relationship between humans and thinking machines has always oscillated between fear and fascination. However, the actual efforts toward machine learning did not begin until the 1950s—a time when computers were still in their infancy and artificial intelligence was little more than a dream. Although theorists such as Thomas Bayes, Adrien-Marie Legendre, and Pierre-Simon Laplace had laid important foundations for later research in the two centuries before, it was only with the work of Alan Turing that the idea of machines capable of learning became concrete.

In 1950, Turing developed his now-famous Turing Test—a kind of game in which a computer tries to convince a human that it, too, is human. If the human can no longer tell that they are not speaking with a flesh-and-blood person, the machine has passed the test. That milestone was still far off at the time, but just two years later Arthur Samuel created a computer that could play checkers—and improved with every game. The program had the ability to learn. In 1957, Frank Rosenblatt developed the Perceptron, the first algorithm capable of supervised learning—a artificial neural network.

Today, major companies are among the driving forces in machine learning development: IBM has built Watson, a computer with an immense knowledge base that can answer questions posed in natural language. Google and Meta use machine learning to better understand their users and offer them more features.