Understanding Artificial Intelligence: Definitions, Foundations, and Approaches
Artificial Intelligence, or more commonly known as AI, is now present in almost every aspect of our lives. From movie recommendations on streaming apps, customer service chatbots, to self-driving cars — all are the result of decades of research into how to make machines “intelligent” like humans.
But what exactly is AI? Where did the idea come from? Let’s break it down in simple terms.
What Is AI According to Experts?
Since the 1970s, many scholars have tried to define AI. The results vary depending on their perspective.
- Bellman (1970s): AI is the automation of activities usually carried out by the human brain, such as decision-making and problem-solving.
- Haugeland (1985): AI is an effort to make computers truly capable of thinking, not just following commands.
- Charniak (1985): AI is the study of mental faculties through computational models.
- Kurzweil (1990): AI is the art of creating machines that perform functions requiring intelligence when done by humans.
- Rich & Knight (1991): AI is about making computers perform tasks at which humans are still better.
- Winston (1992): AI is the study of computations that enable perception, reasoning, and action.
- Luger (1993): AI is a branch of computer science concerned with the automation of intelligent behavior.
- Dean (1995): AI is the design of computer programs that can react intelligently to diverse situations.
- Nilsson (1998): AI is concerned with intelligent behavior in artifacts.
If we look closer, there are two main themes:
- Some emphasize “thinking” like humans (cognition, logic).
- Others focus on “acting” like humans (behavior in practice).
The Three Pillars of AI
To be intelligent, AI relies on three essential foundations:
Knowledge AI needs to “know something.” This knowledge can be facts, rules, or patterns. For example, in NLP (Natural Language Processing), AI learns the meaning of words, grammar, and context so it can understand human language.
Computation The brain of AI is its algorithms. Algorithms tell machines how to make decisions, search for solutions, or recognize patterns. Examples include Google’s search algorithm or YouTube’s recommendation system.
Learning AI becomes smart because it learns from large amounts of data. For example, autonomous cars learn from millions of images of roads, signs, and traffic situations so they can recognize their surroundings and make decisions.
Four Approaches to AI
AI can also be understood through four perspectives, based on whether it thinks or acts, and whether it is human-like or rational:
| Approach | Humanly Oriented | Rationally Oriented |
|---|---|---|
| Thinking | Cognitive modeling — imitating how humans think | Logical reasoning — following formal rules of logic |
| Acting | Human-like behavior (e.g., Turing Test) | Rational agents — acting optimally to achieve goals |
Examples:
- ChatGPT is closer to acting humanly (communicating naturally in human language).
- AlphaGo (the AI that defeated the world champion in Go) represents acting rationally (choosing the best possible move).
The Foundations of AI: A Blend of Many Fields
AI did not emerge out of nowhere. It is a mixture of insights from many disciplines:
- Philosophy: laid the groundwork by asking questions about mind, logic, and knowledge.
- Mathematics: provided the formal tools like logic, probability, and algebra, which underpin algorithms.
- Economics: contributed decision theory and optimization, helping AI choose the best options.
- Psychology: inspired models of how humans learn and think.
- Neuroscience: influenced the design of neural networks, inspired by the human brain.
- Linguistics: enabled AI to process and understand language, from grammar to meaning.
Conclusion
Artificial Intelligence is not just about smart machines, but the result of a long journey combining philosophy, mathematics, economics, psychology, neuroscience, and linguistics. The definitions may vary, but the essence is the same: AI is the effort to make machines capable of thinking and acting intelligently.
Today, AI is all around us and continues to evolve. The challenge is how we use it wisely — so that it becomes a powerful assistant for humans, rather than a replacement.