Guru
Introduction
Artificial Intelligence (AI) has progressed from science fiction to a reality that is revolutionizing various aspects of our world. This cutting-edge technology has the potential to transform industries, reshape our daily lives, and create new opportunities. In this article, we will explore the ways in which AI is shaping our future, from autonomous vehicles to personalized healthcare. Join us on this exciting journey as we delve into the limitless possibilities of artificial intelligence.
Understanding Artificial Intelligence
AI has faced false starts partly because people misunderstand what it is and should accomplish. Movies and books create false hopes, and we tend to anthropomorphize technology, expecting too much from AI. It’s best to start by defining what AI is, what it isn’t, and how it relates to computers today.
Your expectations of AI depend on how you define it, the technology available, and your goals. Everyone sees AI differently, beyond the hype of supporters or the negativity of critics. You may have different expectations, which is fine, but it’s essential to focus on what AI can actually do rather than expect the impossible.
Discovering four ways to define AI
First, it’s important to understand that AI is not the same as human intelligence. Some AI simulates human thinking, but it remains a simulation. AI involves goal-seeking, data processing to reach goals, and data acquisition to understand them better. It uses algorithms to achieve results that may not align with human goals or methods. With this in mind, AI can be categorized in four ways:
»Acting humanly: When a computer acts like a human, it aligns with the Turing test, where the computer succeeds if it cannot be distinguished from a human. This is the image of AI often portrayed by the media. It involves technologies like natural language processing, knowledge representation, automated reasoning, and machine learning, all needed to pass the test. The original Turing test did not include physical contact.
The newer Total Turing Test includes physical contact through perceptual interrogation, requiring computer vision and robotics. Modern AI focuses on achieving goals rather than fully mimicking humans. For example, the Wright Brothers didn’t copy birds exactly but used the idea of flight to develop aerodynamics. Both birds and humans fly but use different methods.
- Introspection: Detecting and documenting the techniques used to achieve goals by monitoring one’s own thought processes.
- Psychological testing: Observing a person’s behavior and adding it to a database of similar behaviors from other persons given a similar set of circumstances, goals, resources, and environmental conditions (among other things).
- Brain imaging: Researchers study brain activity using methods like CAT, PET, MRI, and MEG, then create models to simulate these processes in programs. However, human thought varies greatly, making accurate simulation difficult, so results remain experimental. Psychology often uses this “thinking humanly” approach to model human thought for realistic simulations.
»Thinking rationally: Studying how humans think helps create guidelines for typical behaviors, and a person is seen as rational when following them within limits. A computer that thinks rationally uses these behaviors to interact with its environment based on available data.
This approach aims to solve problems logically, providing a baseline you can modify for practical use. Solving a problem in principle often differs from practice, but a starting point is essential.
History of Artificial Intelligence
Philosophers like Aristotle and Descartes explored core questions related to AI, but researchers could only begin testing whether they could build AI systems when computers became available in the twentieth century.
A key AI question is when a system can be called intelligent. In 1950, Turing proposed the imitation game, where an interrogator guesses whether responses are from a human or a computer.
Turing held that if an interrogator can't distinguish a computer from a human, their intelligence is equal. The Turing test equates intelligence with linguistic competence, as seen in some AI models.
In 1959, Simon, Newell, and Shaw developed the General Problem Solver (GPS), which could tackle formal problems like symbolic integration, the Königsberg bridges, and the Towers of Hanoi. Their work also defined the cognitive simulation paradigm, stating that AI systems should model general human problem-solving methods.
Encouraged by symbolic AI successes, Newell and Simon proposed the physical symbol system hypothesis in 1976, a core view of Strong AI. A physical symbol system uses symbols to build expressions and includes processes to modify, reproduce, and destroy them—in essence, it transforms expressions.