|Marvin Lee Minsky|
American cognitive scientist Marvin Lee Minsky once wrote: “Will robots inherit the earth? Yes, but they will be our children.”
Robots are machines run by artificial intelligence. Artificial intelligence is a product of human intelligence.
But, what is intelligence?
Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.
Computation and calculation
Computation is a type of calculation. A calculation is a process for transforming one or more inputs into one or more results, with variable change.
The term is used in a variety of senses, from the very definite arithmetical calculation of using an algorithm to logically calculating a strategy in a competition or the chance of a successful relationship between two people.
For example, multiplying 7 by 8 is a simple algorithmic calculation. Estimating the fair price for financial instruments using the Black–Scholes model is a complex
To calculate means to ascertain by computing. The English word derives from the Latin calculus, which originally meant a small stone in the gall-bladder (from Latin calx). It also meant a pebble used for calculating, or a small stone used as a counter in an abacus. The abacus was an instrument used by Greeks and Romans for arithmetic calculations, preceding the slide-rule and the electronic calculator. It consisted of perforated pebbles sliding on an iron bars.
What is artificial intelligence?
Artificial intelligence (AI) is the intelligence of machines. Generally, the term is applied to the branch of computer science that aims to create AI. The text books dealing with the subject define the field as "the study and design of intelligent agents" where an intelligent agent is a system or machine that perceives its environment and takes actions that maximize its chances of success. John McCarthy was an American computer scientist and cognitive scientist. He coined the term "artificial intelligence". He defines AI as "the science and engineering of making intelligent machines." AI research is highly technical and specialized, divided into subfields. Machine implementation of human cognitive ability is an ambitious and challenging objective. Its ultimate goal includes total integration, understanding, and representation of animal behavours and cognitive processes of humans—namely, thinking, feeling, speaking, symbolic processing, remembering, learning, knowing, consciousness, problem solving, planning, and decision making. These processes compose a broad cognitive and behavioural spectrum of living systems.
Human mind consists of modules
Many cognitive scientists depict the mind as “modular”—consisting of different parts that interact to produce both external behaviors and internal phenomena such as introspection. Such compartmental paradigms make it possible to create models of mind and build machines that based on such modular concepts. This paradigm provides for emulating the human brain. Such forms of emulation are the very essence of artificial intelligence. Naturally the AI has different branches or subspecialties performing different functions.
Subspecialties or programs of artificial intelligence:
This program decides what to do by inferring that certain actions are appropriate for achieving its goals. Cognitive scientist Robert C. Moore distinguishes three uses of logic in AI; as a tool of analysis, as a basis for knowledge representation, and as a programming language.
AI programs often examine large numbers of possibilities, e.g. moves in a chess game or inferences by a theorem-proving program.
When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face.
Facts about the world have to be represented in some way. Usually languages of mathematical logic are used.
From some facts, others can be inferred. This is emulation of human reasoning. The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default, but the conclusion can be withdrawn if there is evidence to the contrary. For example, when we hear of a bird, we may infer that it can fly, but this conclusion can be reversed when we hear that it is a penguin. It is the possibility that a conclusion may have to be withdrawn that constitutes the non-monotonic character of the reasoning.
Commonsense knowledge and reasoning
This is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s.
Learning from experience
Programs do that. Such programs are made imitating the neural networks of human brain.
Planning programs start with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal. From these, they generate a strategy for achieving the goal. In the most common cases, the strategy is just a sequence of actions.
The list of programs is incomplete. Some of these may be regarded as concepts or topics rather than full branches.
Chinese room argument
Cognitive scientists Stuart Russell and Peter Norvig wrote in the third edition (2009) of the book Artificial Intelligence: A Modern Approach: “Once we have a complete, comprehensive theory of mind, it becomes possible to express the model in machine form.” But some philosophers think this is over confidence and they believe the “Chinese-room-argument” is valid for ever.
The Chinese Room argument was put forward by American philosopher John Rogers Searle. It is an argument against the possibility of artificial intelligence which is true replica of human mind. The argument centers on a thought experiment in which someone who knows only English sits alone in a room following English instructions for manipulating strings of Chinese characters. To those outside the room watching the performer it appears as if someone in the room understands Chinese. Searle summarized the Chinese Room argument concisely:
Imagine a native English speaker who knows no Chinese locked in a room full of boxes of Chinese symbols (a data base) together with a book of instructions for manipulating the symbols (the program). Imagine that people outside the room send in other Chinese symbols which, unknown to the person in the room, are questions in Chinese (the input). And imagine that by following the instructions in the program the man in the room is able to pass out Chinese symbols which are correct answers to the questions (the output). The program enables the person in the room to pass the Turing Test for understanding Chinese without understand a word of Chinese.
Searle goes on to say,
The point of the argument is this: if the man in the room does not understand Chinese on the basis of implementing the appropriate program for understanding Chinese then neither does any other digital computer solely on that basis because no computer, qua computer, has anything the man does not have.
|John Rogers Searle|
Searle develops broader implications of his argument. Searle also aims to refute the functionalist approach to understanding minds, especially that form of functionalism known as the Computational Theory of Mind that treats minds as information processing systems. As a result of its scope, as well as Searle's clear and forceful writing style, the Chinese Room argument has probably been the most widely discussed philosophical argument in cognitive science to appear in the in the past 25 years. By 1991 computer scientist Pat Hayes had defined Cognitive Science as the ongoing research project of refuting Searle's argument.