Simply Explained: What Is Artificial Intelligence?
Artificial intelligence (AI) describes computers or machines that imitate human thinking and learning in order to solve tasks independently. AI systems recognize patterns, process language and learn from data in order to make better and better decisions. Today, AI is already helping in many different areas, from voice assistants and AI chatbots to data analysis.
And Here in More Detail
Artificial intelligence is a branch of computer science that deals with the automation of intelligent behavior. To date, there is no standardized definition. However, it is usually understood to mean systems that perform tasks that typically require human intelligence, such as learning, understanding, analyzing or problem solving.
Early AI models, such as artificial neural networks, were based on the structure of biological nerve cells in the brain. Today, AI systems are much more complex and are primarily based on mathematical and statistical processes.
However, the goal of reproducing human thinking - such as recognizing, deciding or learning - remains the same. Modern applications such as language models or AI chatbots use machine learning to continuously improve: they learn from new data, optimize their responses and adapt to contexts. In areas such as speech recognition or the analysis of large amounts of data, they often achieve greater speed and precision than humans.
AI systems are already firmly integrated into our everyday lives and use innovative methods to solve a wide variety of problems. Examples of this include AI chatbots:
- Translation programs,
- automated image optimization in cameras and smartphones
- or automatic recommendation systems such as those used by Netflix.
The importance of AI for all areas of technology can be seen not least from the fact that there are hardly any new computers/smartphone processors that are not designed to massively support or accelerate AI.
What Are Important Terms Relating to Artificial Intelligence?
Anyone researching artificial intelligence will quickly come across terms such as “machine learning” or “generative AI”. To avoid confusion, here is a compact overview of key terms - simply explained:
Machine learning: Process in which AI systems recognize patterns from sample data and learn from them. The more data is available, the better correlations can be identified and predictions made.
Large Language Models (LLMs): Language models such as GPT-4 or Claude 4 that have been trained with huge amounts of text. This enables them to understand and process complex language input and generate suitable answers.
More on this in the wiki article: 'What Is a Large Language Model?'
Generative AI (GenAI): Systems that independently generate new content, such as text, images, music, videos or program code, based on learned patterns. Well-known examples are ChatGPT or DALL-E. moinAI also uses generative AI.
Natural language processing (NLP): a branch of AI that deals with the analysis, understanding and generation of human language. Examples of applications include language assistants, chatbots and automatic text suggestions.
More on this in the wiki article: 'Natural Language Processing: Examples & Potential (Simply Explained)'.
What Is the History of Artificial Intelligence?
In order to better understand the potential of today's AI, it is worth taking a look back at its beginnings. As early as 1950, the British mathematician Alan Turing posed the question in his famous essay 'Computing Machinery and Intelligence': Can machines think? His so-called Turing Test was intended to test whether a computer could convincingly imitate human behavior.
Shortly afterwards, Arthur Samuel developed an adaptive program for the strategy game Checkers - an early example of machine learning. However, the Dartmouth Workshop in 1956 is considered the official starting point of AI research: not only was the term “artificial intelligence” coined there, but the vision was also formulated that machines would be able to learn, reason and solve problems in the future.
In the following decades, the first applications were developed, such as the text dialog bot ELIZA and programs for pattern recognition. A breakthrough came in 1997 when IBM's chess computer Deep Blue defeated the then world champion Garry Kasparov.
AI has experienced a huge boom since the 2010s: IBM Watson won the quiz show Jeopardy! and showed just how powerful language processing systems can be. The final boost to development came at the end of 2022 with the release of ChatGPT, which brought the general public into direct contact with artificial intelligence for the first time. Since then, artificial intelligence has finally arrived in everyday life - from chatbots and image generators to recommendation systems and autonomous vehicles.
However, the future of AI is not only determined by technology. Regulatory frameworks such as the EU AI Act, ethical guidelines and open source initiatives such as Mistral or DeepSeek will determine how AI can be used responsibly and transparently in the coming years.
👉 More on this topic in the wiki article: “EU AI Act in focus: New regulations for AI in Europe”.
How Does Artificial Intelligence Work?
AI is based on large amounts of data from which the systems use machine learning (ML) to recognize patterns and make predictions without having to program each individual task manually.
The learning process can be simplified as follows:
1. Collection of large, often unstructured amounts of data (e.g. text, images)
2. Training of a model in which it learns to recognize correlations and patterns
3. Adjustment of internal parameters to improve accuracy
4. Application of the trained model to new data to make decisions or predictions
5. Continuous improvement through feedback and new data
In practice, this process is usually much more complex. Today, many companies use pre-trained AI models that they adapt specifically to their own areas of application with the help of transfer learning. This saves resources and time, as large basic models are extremely time-consuming to develop.
Areas of application for artificial intelligence
Artificial intelligence (AI) is being used in more and more areas of life and work - often quite naturally in the background. It takes over monotonous or complex tasks, helps with decisions and opens up new opportunities to make processes more efficient. Here is an overview of key areas of application, including exciting examples:
Mobility & autonomous driving
Whether assistance systems or self-driving vehicles. AI is the key to the mobility of the future. Cameras, sensors and control units record the environment in real time. The AI processes this data, recognizes traffic signs, vehicles or pedestrians - and reacts accordingly.
Examples:
- Mercedes-Benz was the first car manufacturer to introduce an AI-based system with German road approval for semi-autonomous driving (level 3) on freeways. The driver can even take their eyes off the traffic for a short time.
- Household robots and drones also use AI to find their way around and learn, for example when recognizing objects or navigating through unknown spaces.
Industry & Production (Industry 4.0)
In Industry 4.0, AI optimizes production processes, reduces downtime and detects faults before they occur:
- Predictive maintenance: AI analyzes machine data and detects maintenance requirements at an early stage.
- Round-the-clock quality control: image analysis using AI replaces time-consuming manual inspection processes.
- Collaborative robots (“cobots”) work directly and safely with humans, for example in assembly or packaging.
Example: BMW uses AI to automatically check images of vehicle parts for defects. This saves time and increases precision.
Medicine & healthcare
AI also supports diagnosis, therapy and research in the healthcare sector.
- Image analysis: AI recognizes the finest anomalies in X-ray or MRI images.
- Speech processing: Doctor's notes or doctor-patient conversations are automatically transcribed.
- Robot-assisted surgery: Operating systems work particularly precisely and without fatigue.
Example: In cancer diagnostics, systems such as “PathAI” are used to evaluate tissue samples faster and more reliably.
Customer service & communication
AI-based chatbots and virtual assistants are now an integral part of customer service in many companies. They answer queries around the clock, resolve support tickets and offer users direct, seamless communication.
Examples:
Blume2000: The florist uses an AI chatbot to answer queries efficiently, manage peak times and increase conversion rates.
You can find more insights on Blume2000 in the case study: 'AI Chatbot for Efficient Customer Service and Digital Transformation'.
Teleboy: The Swiss provider is using an AI-supported chatbot to relieve its customer support - especially for common questions about the internet, TV or telephone - and increase customer satisfaction.
BLS: BLS is another successful example of the use of AI chatbots in customer service: a single chatbot was turned into an entire system with over 25 AI agents.
Marketing, e-commerce & personalization
AI can identify preferences, analyse behaviour and make appropriate recommendations. Such personalized user experiences are now standard.
Examples:
- Netflix & Spotify: AI analyzes viewing and listening habits to optimize suggestions.
- Amazon: Product recommendations are based on an AI-supported prediction model.
- Email marketing: Tools such as HubSpot use AI to automatically optimize subject lines and send times.
Financial services & fraud detection
Banks and FinTechs use AI to analyze transactions, detect fraud attempts and offer individual financial advice.
Example: PayPal uses AI to filter suspicious activity in real time, protecting millions of users from fraud.
Human Resources & Recruiting
AI helps with the pre-selection of applications, compares profiles with requirements and conducts initial interviews in the form of video analyses.
Example: The 'HireVue' platform uses AI to analyze applicant videos. Factors such as body language, choice of words and facial expressions are included in the assessment.
Caution: (Still) heavily regulated in Europe
However, the use of AI to analyze applicant videos is highly sensitive from a legal perspective in Europe. Although such technology is possible in principle, it is subject to strict data protection regulations (GDPR) and may not be used without transparent consent or human control. Companies in Germany should therefore consider particularly carefully whether and how they use AI-supported video analysis in recruiting.
Language processing & generative content
AI is now able to independently create texts, images, music and even videos. Generative AI opens up new avenues for creative processes from marketing to software development.
- Text generation: Tools such as ChatGPT or alternatives provide support with texts, summaries or collections of ideas.
More information on alternative tools and their possible uses can be found in the article: '20 Alternatives to ChatGPT at a Glance'.
- Image generation: Midjourney or DALL-E generate visual concepts at the touch of a button.
- Code completion: GitHub Copilot suggests suitable lines of code when programming.
How Do Chatbots Use Artificial Intelligence?
AI chatbots are a vivid example of the practical use of artificial intelligence, or AI for short. They illustrate what artificial intelligence can actually achieve.
AI chatbots use modern natural language processing to recognize more than just the issue at hand. Many systems also detect the user's emotional mood and react more empathetically accordingly. The boundaries between a “real” human dialog and chatbot contact are becoming increasingly blurred.
Chatbots are also continuously expanding their knowledge base - usually through a combination of automatic learning and targeted human feedback. They are available and ready for use around the clock. In customer service, for example, chatbots use AI to deal with customers' problems, questions or complaints. This reduces the workload for both employees and customers: Inquiries are quickly received, forwarded or, in the best case, completely resolved by the chatbot itself.
Excursus: Does Artificial Intelligence Threaten Jobs?
What should artificial intelligence be able to do? This question quickly raises the concern that AI could lead to job losses. However, there is no general answer to this: AI does not fundamentally replace people, but it does change activities and work processes.
Chatbots, for example, take over standardized tasks and sequences, relieving staff in areas such as customer contact, completing inefficient tasks and pre-qualifying or answering repetitive questions. This creates new capacities for more complex, interpersonal or creative tasks.
As artificial intelligence cannot completely replace humans, it will still need close support from real people in the future - for quality assurance, further development or monitoring, for example. At the same time, AI is creating numerous new job profiles, for example in data analysis, system development or ethics consulting. The demand for technically qualified specialists is growing and with it the need for targeted further training and retraining. The use of AI in industry, business and services therefore not only harbors risks, but above all enormous potential for the further development of the world of work.
Conclusion: AI Has Firmly Established Itself
Artificial intelligence is becoming increasingly important - as shown by published strategy papers from the German government. It is already impossible to imagine many areas of life without AI. AI chatbots are a good example: they take over routine tasks, relieve people and create space for more demanding activities without replacing humans.
At the same time, it is important that AI continues to be closely supported by humans - be it in quality assurance or ethical issues. At the same time, technology is creating new professions and opportunities that require targeted further training.
The responsible and transparent use of AI is not only a technical challenge, but above all a social one. Regulations, ethical guidelines and open discussions are essential in order to make the most of the potential of AI and minimize risks.
All in all, it remains to be said: Artificial intelligence is a powerful tool with great potential for innovation and progress. The future of work and coexistence will be largely determined by how humans and AI work together harmoniously.
