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
Artificial intelligence (AI) has had several false starts and stops over the years,partly because people don’t really understand what AI is all about,or even what it should accomplish.A major part of the problem is that movies,television shows and books have all conspired to give false hopes as to what AI will accomplish.In addition the human tendency to anthropomorphize (give human characteristics to) technology makes it seem as if AI must do more than it can hope to accomplish.So,the best way to start this is to define what AI actually is, what it isn’t, and how it connects to computers today.
Of course, the basis for what you expect from AI is a combination of how you define AI, the technology you have for implementing AI, and the goals you have for AI.Consequently, everyone sees AI differently.It doesn’t buy into the hype offered by proponents, nor does it indulge in the negativity espoused by detractors, so that you get the best possible view of AI as a technology. As a result, you may find that you have somewhat different expectations than those you encounter in this book, which is fine, but it’s essential to consider what the technology can actually do for you, rather than expect something it can’t.
Discovering four ways to define AI
As we discussed earlier that,the first concept that’s important to understand is that AI doesn’t really have anything to do with human intelligence. Yes,some AI is modeled to simulate human intelligence, but that’s what it is a simulation. When thinking about AI, notice an interplay between goal seeking, data processing used to achieve that goal, and data acquisition used to better understand the goal. AI relies on algorithms to achieve a result that may or may not have anything to do with human goals or methods of achieving those goals. With this in mind, you can categorize AI in four ways:
»Acting humanly: When a computer acts like a human, it best reflects the Turing test, in which the computer succeeds when differentiation between the computer and a human isn’t possible.This category also reflects what the media would have you believe AI is all about. You see it employed for technologies such as natural language processing, knowledge representation, automated reasoning, and machine learning (all four of which must be present to pass the test).The original Turing Test didn’t include any physical contact.
The newer, Total Turing Test does include physical contact in the form of perceptual ability interrogation, which means that the computer must also employ both computer vision and robotics to succeed. Modern techniques include the idea of achieving the goal rather than mimicking humans completely. For example,the Wright Brothers didn’t succeed in creating an airplane by precisely copying the flight of birds; rather, the birds provided ideas that led to aerodynamics that eventually led to human flight. The goal is to fly. Both birds and humans achieve this goal, but they use different approaches.
- 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: Monitoring brain activity directly through various mechanical means, such as Computerized Axial Tomography (CAT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI), and Magnetoencephalography (MEG).After creating a model, you can write a program that simulates the model.Given the amount of variability among human thought processes and the difficulty of accurately representing these thought processes as part of a program, the results are experimental at best. This category of thinking humanly is often used in psychology and other fields in which modeling the human thought process to create realistic simulations is essential.
»Thinking rationally: Studying how humans think using some standard enables the creation of guidelines that describe typical human behaviors. A person is considered rational when following these behaviors within certain levels of deviation. A computer that thinks rationally relies on the recorded behaviors to create a guide as to how to interact with an environment based on the data at hand.
The goal of this approach is to solve problems logically,when possible. In many cases, this approach would enable the creation of a baseline technique for solving a problem, which would then be modified to actually solve the problem. In other words, the solving of a problem in principle is often different from solving it in practice, but you still need a starting point.
History of Artificial Intelligence
Many fundamental methodological issues of Artificial Intelligence have been of great importance in philosophy since ancient times. Such philosophers as Aristotle,St. Thomas Aquinas, William of Ockham, René Descartes, Thomas Hobbes, and Gottfried W. Leibniz have asked the questions: “What are basic cognitive operations?”, “What necessary conditions should a (formal) language fulfill in order to be an adequate tool for describing the world in a precise and unambiguous way?”,“Can reasoning be automatized?”. However, the first experiments that would help us to answer the fundamental question: “Is it possible to construct an artificial intelligence system?” could not be performed until the twentieth century, when the first computers were constructed.
Certainly, the question: “When can we say that a system constructed by a human designer is intelligent?” is a key problem in the AI field. In 1950 Alan M. Turing1 proposed a solution of this problem with the help of the so-called imitation game [307].This conversation is performed with the help of a device which makes the simple identification of an interlocutor impossible. (For example, both interlocutors send their statements to a computer monitor.) The human interrogator, after some time,should guess which statements are sent by the human being and which ones are sent by the computer. According to Turing, if the interrogator cannot make such a distinction, then the (artificial) intelligence of the computer is the same as the intelligence of the human being. Let us note that intelligence is, somehow, considered equivalent to linguistic competence in the Turing test. As we will see further on, such an equivalence between intelligence and linguistic competence occurs in some AI models.
The further research of Simon, Newell, and Shaw into constructing systems possessing mental abilities resulted in the implementation of General Problem Solver,GPS in 1959. The system solved a variety of formal problems, for example: symbolic integration, finding paths in Euler’s problem of the Königsberg bridges, playing the Towers of Hanoi puzzle, etc. Defining the paradigm of cognitive simulation,6 which says that in AI systems general schemes of human ways of problem solving should be simulated, was a methodological result of their research.
Successes in constructing symbolic AI systems encouraged Newell and Simon to formulate in 1976 a fundamental view of Strong Artificial Intelligence, namely the physical symbol system hypothesis.Aphysical symbol system consists of a set of elements called symbols that are used by the system to construct symbolic structures called expressions and a set of processes for their modification, reproduction, and destruction. In other words, the system transforms a certain set of expressions.