Artificial intelligence (AI) basically refers to computer-based systems that perform tasks traditionally associated with human intelligence: Perception, learning, decision-making, problem-solving, judgment or even creativity.
Formally, AI is a branch of computer science that deals with the research and construction of such systems. The machine should not just stubbornly execute predefined instructions - as is the case with classic programs - but also draw conclusions independently, recognize patterns or adapt to new situations.
Where does the term come from and how has the field developed?
The term „artificial intelligence“ was coined in the mid-1950s as part of the Dartmouth Summer Research Project on Artificial Intelligence (1956), which is widely regarded as the birth of AI research.
Since then, AI research has gone through several phases - phases of great optimism, but also phases of disillusionment when expectations were not met.
| Year | Milestone |
|---|---|
| 1936 | Alan Turing develops the Turing machine |
| 1956 | John McCarthy coins the term „artificial intelligence“ at the Dartmouth Conference |
| 1966 | ELIZA: first chatbot that simulates conversations |
| 1997 | Deep Blue defeats world chess champion Garry Kasparov |
| 2000s | Advances in computing power, big data and algorithms enable modern AI systems |
| 2010-today | Voice assistants, generative AI (ChatGPT, DALL-E), self-driving cars |
With the advent of powerful computers and large amounts of data (e.g. thanks to the Internet, sensor technology, Big Data) and technical advances - particularly in the field of artificial neural networks and deep learning - AI has experienced a significant upswing. As a result, applications that seemed unimaginable just a few decades ago are now possible.
How does AI work - central methods and approaches
AI can be realized in different ways. Two central approaches are
- Symbolic / rule-based AI: Here, knowledge and decision rules are explicitly modeled - the system draws logical conclusions based on these rules, like a classic expert system.
- Statistical / data-driven AI (in particular neural networks and deep learning): Here, the system learns from large amounts of data - e.g. from images, texts or measured values - in order to recognize patterns and make predictions or decisions. This approach can also deal with probabilistic information and uncertainty.
Particularly widespread today are methods of Machine learning (machine learning, ML) and deep learning. These enable AI systems to learn from experience and improve their performance in a similar way to how humans grow through learning.
Weak vs. strong AI - the difference between everyday applications and visions
In practice, experts usually speak of weak (narrow) AISystems that are optimized for very specific tasks - such as speech recognition, image recognition, recommendation systems, autonomous vehicles or chatbots. They imitate certain aspects of human intelligence, but not general intelligence.
This contrasts with the idea of strong (general) AI or Artificial General Intelligence (AGI): a hypothetical system that can think and learn flexibly, creatively and with a similar breadth as a human - across many different tasks. Such a system does not yet exist in the real sense.
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What is AI used for today - fields of application and benefits
The range of applications for AI is astonishingly wide:
| Field of application | Examples |
|---|---|
| 🗣️ Speech recognition, translation & NLP | Voice assistants, chatbots, automatic translation programs |
| 🖼️ Image & pattern recognition | Facial recognition, medical image analysis, industrial quality control |
| ⚙️ Automation & optimization | Use in production, logistics, administration, data processing; automation of repetitive or data-intensive processes |
| Generative AI | Text generation, image generation, music composition, design, program code |
The main benefit of AI lies in this, Automate processes, The ability to analyze large amounts of data and derive patterns or predictions from it - often faster, more accurately and more scalable than human work alone.
Opportunities and challenges
The use of AI offers many opportunities - but also challenges:
- Advantages: Relief for routine work, increased efficiency, automation of complex processes, support in medicine, research, industry and administration.
- Risks and limits: AI systems require large amounts of data; they can make mistakes, are susceptible to bias or data distortion, and their decisions are often not transparent („Black box“ problem).
- Ethical and social issues: Data protection, responsibility, transparency, possible effects on the labor market and power relations - these are all key issues when dealing with AI.
Conclusion
Today, artificial intelligence is much more than an abstract scientific term; it is shaping our everyday lives, our working world and our society. AI can support us, relieve us and open up new opportunities - at the same time, it challenges us to use this technology responsibly.
While many of today's systems (weak AI) are already impressive and deliver real added value, the vision of a general, human-like intelligence (strong AI) remains in the realm of theory and speculation for the time being.
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