For thousands of years, we have tried to understand how we think; that is; how a mere handful of matter can perceive, understand, predict and manipulate a world far larger and more complicated than itself. The field of Artificial Intelligence, or (AI), goes further, it not only just attempt to understand but also attempt to build intelligent entities. The term AI is coined by John McCarthy in 1956.
What is Artificial Intelligence?
Historically four approaches are adopted in the field of AI. These approaches can be organised along the two dimension:
- Human Centered Approach Vs Rationalist approach
- Thought process(Thinking) Vs Behavior(Acting)
1. Act Like Human: The Turing Test Approach
The Turing Test proposed by Alen Turing(1950), was designed to provide a satisfactory operational definition of intelligence.
Programming a computer to pass a rigorously applied test provides plenty to work on. The computer need to possess the following capabilities:
- Natural Language Processing: to enable it to communicate successfully in English or other language.
- Knowledge Representation: to store what it knows or hear.
- Automated Reasoning: to use the stored information to answer questions and to draw new conclusions.
- Machine Learning: to adapt to new circumstances and to detect and extrapolate patterns.
Turing’s test deliberately avoided direct physical interaction between the interrogator and the computer, because physical simulation of a person is unnecessary for intelligence.
However, the so-called Total Turing Test, includes a video signal so that the interrogator can test the subject’s perceptual abilities. To pass the Total Turing Test, the computer will need
- Computer Vision: to perceive objects, and
- Robotics: to manipulate objects and move about.
These six disciplines compose most of AI, and Turing deserve credit for the designing a test that remains relevant 60 years later. Yet AI researchers, have devoted little effort to passing the Turing Test, believing that, it is more important to study the underlying principles of intelligence than to duplicate the exemplar.
2. Thinking Like Human: Cognitive Modelling Approach
If we are going to say that a give program think like a human, we must have some way of determining how humans think. We need to get inside the actual working of the human mind which is the field of Cognitive Science. Cognitive science brings together computer models from AI and experimental techniques from psychology to construct precise and testable theories of the human mind.
The two field computer science and cognitive science continue to fertilize each other, most notably in Computer Vision, which incorporates neurophysiological evidences in computational model.
3. Thinking Rationally: The Laws of Thought Approach
The Greek philosopher Aristotle was one of the first to attempt to codify “right thinking”. His syllogisms provided pattern for argument structures that always yielded correct conclusion when given correct premises. for example, “Socrates is a man, all man are mortal; therefore, Socrates is mortal.” These laws of thought were supposed to govern the operation of the mind, their study initiated the field called logic.
Logicians in the 19th century developed a precise notation for statements about all kinds of objects in the world and relationship among them. By 1965, programs existed that could, in principle, solve any solvable problem described in logical notation. ( Although if no solution exists, the program might loop forever). The so called Logicist tradition within the AI hopes to build on such programs to create intelligent systems.
There are two main obstacles to this approach:
- It is not easy to take informal knowledge and state in the formal term required by logical notations particularly when knowledge is less than 100% certain.
- There is a big difference between solving a problem “in principle” and solving in practice. Even problem with just a few hundred facts can exhaust the computational resources of any computer unless it has some guidance as to which reasoning steps to try first.
4. Acting Rationally: The Rational Agent Approach
An agent is something that acts. Of course, all computer programs do something, but computer agents are expected to do more: Operate autonomously, perceive their environment, adapt to change and create and pursue the goals. A rational agent is one that act so as to achieve the best outcome, or where there is uncertainty, the best expected outcome.
The rational agent approach has two advantages over the other approaches:
- It is more general than the laws of thought approach, because, correct inference is just one of several possible mechanism for achieving rationality.
- It is more amenable to scientific development than are approaches based on human behavior or human thought.
Typical AI problems
Intelligent Agent need to be able to do both mundane task (such as planning route, activity planning, recognizing people and object, communicating through natural languages, navigating around obstacles on the street etc.) and expert task (such as medical diagnosis, mathematical problem solving, playing chess etc.).
It has been observed that it is easier to mechanize many of the expert level task we usually associated with “Intelligence” in people. These problem can easily be solved using AI to. Some problem where AI applied successfully are:
- Symbolic Integration.
- Proving Theorem.
- Playing Game like Chess.
- Medical Diagnosis.
But it has been very hard to mechanize some of the very basic task which even animal performs such as:
- Walking around without running into things
- Catching prey and avoiding predators
- Interpreting complex sensory information
- Modelling the internal states of living entity from their behavior.
- Working as team and collaborating.
These task are hard for intelligent agent too.
Source: Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
This article is contributed by Ram Kripal. If you like eLgo Academy and would like to contribute, you can mail your article to email@example.com. See your article appearing on the eLgo Academy page and help other Geeks. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.