What Is Artificial Intelligence (AI)? Definition, Types and Use Cases

Basics · Artificial intelligence · Definition & use cases

Whether chatbots, self-driving cars or personalized recommendations: artificial intelligence (AI) has long been part of our daily lives, often without us noticing. But what exactly is artificial intelligence?

The term describes technologies that imitate human-like abilities such as thinking, learning or problem-solving. And the topic is rapidly gaining importance: according to forecasts, the number of users worldwide will grow by around 826 million between 2025 and 2031. That is an increase of more than 238 percent. By 2031, the number of users is expected to reach a new high of around 1.2 billion.

In this article you will learn what artificial intelligence is and which opportunities and risks it brings for companies and our society.

What is artificial intelligence: the key points in brief

What is artificial intelligence?

Artificial intelligence refers to the ability of machines or computers to take on tasks that normally require human intelligence. In doing so, it can imitate a range of cognitive abilities:

• Logical thinking and problem-solving
• Language understanding and processing
• Visual perception and image recognition
• Ability to learn and adapt to new situations

AI systems work with large amounts of data and complex algorithms. They analyze information, recognize relationships and make statements or decisions on that basis.

Two aspects are particularly central here:

Machine learning

In machine learning, algorithms improve their performance through experience, similar to humans who learn from mistakes.

The ability to be creative

The creative component shows in the ability of AI systems to generate new ideas, for example in the form of texts, images or pieces of music.

The history of AI

The history of artificial intelligence goes back further than many assume. As early as the 1950s, Alan Turing developed a test that raises the fundamental question: when can we call a machine intelligent?

The Turing test

In the Turing test, a person communicates in writing with a computer and with another person, without knowing who is who. If the examiner cannot reliably tell after several conversations whether they are communicating with a human or a machine, the test is considered passed. The test is controversial because it equates a machine’s language ability with its ability to think, but it is nevertheless considered a milestone of research.

The perceptron

The perceptron, developed by Frank Rosenblatt in 1958, is considered a precursor of today’s neural networks. It is a simple artificial neuron that weights and sums input signals and produces an output signal when a threshold is exceeded. Even though the perceptron could initially only solve simple tasks, it laid the foundation for more complex machine learning models.

The first chatbot: ELIZA

In 1966, Joseph Weizenbaum developed the program ELIZA at MIT, which is regarded as the first chatbot in history. Although ELIZA was relatively simple in design and had no real language understanding, the program often managed to convince people of a seemingly intelligent conversation.

Basics of artificial intelligence: how does AI work?

When it comes to how it works, there are primarily the following four fundamentals:

1. Machine learning
2. Deep learning
3. Supervised learning
4. Unsupervised learning

Machine learning

In machine learning, algorithms learn from data and experience. Concretely, this means: algorithms improve through repeated task completion; artificial intelligence independently identifies relationships and patterns in datasets; and instead of detailed step-by-step instructions, the AI receives the specification of the “what”, not the “how”, and finds suitable solution paths itself.

Deep learning

Deep learning is an advanced form of machine learning. It is based on artificial neural networks modeled on the human brain. Multiple layers of data nodes process information, the design is oriented toward the connections of nerve cells, and the constant processing of new data improves performance.

Supervised learning

In supervised learning, developers train AI models with pre-classified data; each input corresponds to a known, correct output (label). The model learns from labeled datasets, recognizes patterns between inputs and outputs and uses these insights to correctly classify new, unknown data. Typical areas of use are image recognition, fraud prevention and language processing.

Unsupervised learning

In unsupervised learning, AI models work with unlabeled data and independently discover patterns and structures. The systems group data without predefined categories, recognize relationships and anomalies and are particularly adaptable with unknown or changing datasets. Areas of use are found, for example, in marketing (segmentation), in cybersecurity (detecting unusual activity) and in text analysis (clustering similar documents).

Types of artificial intelligence

Weak vs. strong AI

Weak AI, also called “narrow AI”, is specialized in specific tasks. It powers many practical applications today such as voice assistants, chess computers or recommendation systems and is currently the predominant form of artificial intelligence. Strong AI, often referred to as “artificial general intelligence” (AGI), aims at human-like cognitive abilities: it should be able to think, learn and solve problems independently in a wide variety of contexts. AGI so far exists only in theory and not in practice.

Symbolic vs. subsymbolic AI

Symbolic AI is based on logical rules and symbols, works with explicit knowledge and enables comprehensible decision paths; a classic example is expert systems in medicine. Subsymbolic AI, by contrast, uses mathematical models and statistical methods, learns from data without explicit rules and is often harder to interpret in its decisions; typical examples are neural networks in image recognition.

Software vs. embedded AI

Software AI runs on standard computer systems, can be used flexibly and is easy to update. Examples are chatbots or recommendation systems in online shops. Embedded AI is integrated directly into hardware, optimized for specific tasks and environments and is found, for example, in smartwatches, autonomous vehicles or smart household appliances.

What is generative AI?

Generative AI independently creates new content that previously only humans could create through their own creativity. Such models learn the underlying patterns and rules from large amounts of data and use this understanding to produce new, original content that resembles the training data but is not identical to it.

Generative AI can write texts, create images from text descriptions, compose new melodies and musical arrangements, develop program code and generate videos and animated scenes.

What is artificial intelligence used for?

Artificial intelligence is applied in numerous areas of our daily lives. Important areas of use are digital assistants such as Siri, Alexa or ChatGPT; transport and navigation with route optimization and autonomous driving; e-commerce through personalized recommendations; language processing for translation and speech recognition; healthcare with early detection of diseases and analysis of medical images; cybersecurity for detecting and defending against digital threats; smart living with control of heating, lighting and security systems; image analysis from facial recognition to detecting deepfakes; and industry with process optimization and robotics.

Opportunities and risks of AI

Artificial intelligence promises to improve and simplify many areas of our lives, but at the same time raises ethical and societal questions. For responsible use, both sides should be considered.

Opportunities: AI automates processes and speeds up workflows in many industries. Personalized offers improve customer experiences. Systems support prevention and early detection of risks, for example in cybersecurity. In addition, AI drives innovation in science and technology.

Risks: AI systems can amplify existing biases and lead to discrimination. Automation can displace certain occupations. Deepfakes and AI-generated content make it harder to distinguish fact from fiction. Processing large amounts of data raises data protection questions, and complex systems often act as a “black box” and are hard to trace.

The EU AI regulation: EU AI Act

The EU AI Act regulates the use of artificial intelligence in the European Union. The regulation classifies AI systems by their risk, from unacceptable to minimal. Strict controls apply to high-risk applications; at the same time, all AI systems must meet a minimum level of transparency. The goals are to promote innovation, protect fundamental rights and uphold ethical principles. Companies are called on to build competencies so that employees can use AI properly and control it at any time.

What does artificial intelligence look like in the future?

The development of artificial intelligence is advancing rapidly and will shape daily life and the working world lastingly. AI systems will increasingly be integrated invisibly and be omnipresent, from smart household appliances to applications in healthcare. For companies, AI is becoming a central competitive factor: it opens up new room for maneuver where classic processes reach their limits. Those who want to use the opportunities should act now; investments in technologies and competencies will be decisive for future success.

Using artificial intelligence in a targeted way in your company

In general, AI takes over repetitive tasks quickly and precisely. For lasting success in the company, the right use matters: identify concrete areas with real added value, because not every problem needs an AI solution. Plan enough time for implementation, because successes often come with a delay. Take the EU AI Act into account in your AI strategy from the start. Above all, though: well-trained employees are the key to success with AI.

What is artificial intelligence: conclusion

The key to successful and responsible use of AI lies in competence. Companies that can understand, evaluate and deploy AI in a targeted way will have an edge in the future. Ultimately, success in the age of AI depends not on technology alone, but on the people who develop, implement and use it. It is about using AI as a tool to create value, always in line with the legal situation.