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Artificial Intelligence

Artificial Intelligence (AI) is the intelligence of machines and the branch of computer science that aims to create. Textbooks define the field as the study and design of intelligent agents "where an intelligent agent is a system that perceives its environment and takes actions which maximize their chances of success. John McCarthy, who coined the term in 1956, defines it as "science and engineering of making intelligent machines ".

The camp was founded on the assertion that a fundamental property of human beings, intelligence, wisdom of Homo sapiens can be described so precisely that can be simulated by a machine. This raises philosophical questions about the nature of the mind and the limits of scientific arrogance, questions have been addressed by the myth, fiction and philosophy since antiquity. The artificial intelligence was impressive optimism has suffered dramatic setbacks and is now a essential element of the technology industry, a heavy burden for many of the most difficult problems in computer science.

AI research is highly qualified, deeply divided into sub-fields, which often fail to communicate. Sub-fields have developed around particular institutions, the work of individual researchers, the solution of specific problems, the long-standing differences of opinion on how the AI should be done and implementing the wide variety of tools. The central problem of AI include features such as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. The general intelligence (or "strong AI ") is always long term (investigate).

History

Thinking machines and artificial beings appear in Greek myths, such that Talos Crete, the golden robots of Hephaestus Pygmalion and Galatea. Similarities of man believes that intelligence is integrated in all major civilizations: animated statues are worshiped in Egypt and Greece and humanoid robots have been built by Shi Yan, Heron of Alexandria Al-Jazari and Wolfgang von Kempelen. It is also believed that artificial beings had been created by J? Bir al-Hayy ibn? No, Judah Loew and Paracelsus. In the 19th and 20th centuries artificial beings has become a common feature in fiction, as in Mary Shelley's Frankenstein or Karel? Universal APEK RUR (Rossum's robots). McCorduck Pamela contends that these are all examples of an old movement, as she describes it, "Forging the gods." The stories of these creatures and their fates to examine many of the same hopes, fears and concerns presented by ethical intelligence artificial.

Mechanical or "formal" reasoning has been developed by philosophers and mathematicians since antiquity. The study of logic led directly to the invention of the programmable electronic digital computer, based on the work of mathematician Alan Turing and others. The theory Turing suggested that a calculating machine, shuffle through symbols as simple as "0" and "1" could simulate an act conceivable mathematical deduction. This, combined with recent findings of neuroscience, information theory and cybernetics, inspired by a few researchers to begin to consider seriously the possibility of building an electronic brain.

The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. The guests, including John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, became the leaders of AI research for many decades. They and their students have written programs that for most people, simply amazing: computers are solving word problems in algebra, logic and prove theorems English language. In the mid-1960s, research in the United States has been largely funded by the Department of Defense and the laboratories have been established in the world. AI Founders were deeply optimistic about the future of the new field: Herbert Simon predicted that "machines will be capable in the twenty years to make any job a man can do "and agreed Marvin Minsky writes that" within a generation ... the problem of the creation of 'artificial intelligence' will substantially resolved. "

They do not recognize the difficulty some of the problems they face. In 1974, responding to criticism of Sir James Lighthill England and the constant pressure Congress to fund more productive projects, the United States and British governments to cut all non-oriented, exploratory research in AI. In future years, when Project financing has been difficult to find, later to be called "AI winter".

In the 1980s, AI was impetus to the search by the commercial success of expert systems, a form of AI program that simulates the knowledge and skills necessary to analyze one or more experts to man. In 1985, the market Amnesty International had reached more than one billion dollars. Meanwhile, IT project fifth of Japan has inspired a generation of U.S. and British governments to restore funding for university research in the field. However, the collapse the Lisp machine market in 1987, Amnesty International has again fallen into disrepute, and a second, longer duration of winter, Amnesty International began.

In the 1990s and the 21st century AI achieved its greatest success, although a little behind the scenes. Intelligence artificial is used for logistics, data mining, medical diagnosis and many other areas throughout the technology industry. The success was due to several factors: the incredible power of today's computers (see Moore's Law), the emphasis on solving subproblems More specifically, the creation of links between Amnesty International and other areas working on similar problems, especially a new commitment researchers in mathematical methods of sound and rigorous scientific standards.

Problems

The problem of simulation (or create) Intelligence has been divided into a series sub-groups of problems. They consist of special features or capabilities that researchers want an intelligent system to display. Functions described below have received the most attention.

Deduction, reasoning, problem solving

Early investigators developed AI algorithms that imitate the step reasoning that humans use when they solve puzzles, board games or make logical deductions. At the end of 1980 and 90, the AI research has also developed highly effective methods for dealing with uncertain or incomplete information, using concepts of probability and economics.

For difficult problems, most of these algorithms May require enormous computing resources - more experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for the solution of problems more efficient algorithms is a priority for AI research.

Human beings solve most problems with using fast Test intuitive rather than conscious step by step deduction that early AI research could model. Amnesty International has made some progress in imitation of these "sub-symbolic" Diagnosis: integrated approaches emphasize the importance of consensus than reasoning skills; neural net research attempts to simulate the internal structures of human and animal brain that leads to this ability.

Performance Knowledge

Knowledge Representation and technical knowledge are essential to the research of AI. Many machines are expected to solving problems requires a thorough knowledge of the world. Among the things that the AI should represent are: objects, properties, classes and relationships between objects, situations, events, states and time, the causes and effects, the knowledge of knowledge (that we know what people know the others), and many other areas in the study. A complete representation of "what there is an ontology (to use a word of the traditional philosophy), more than general ontologies are convened.

Among the most difficult problems of knowledge representation are:

Default reasoning and the qualification problemMany things that people know to take the form of "working hypothesis". For example, if a bird comes into the conversation, it often represents an animal about the size of a fist, singing and flies. None of these things are true of all birds. John McCarthy identified this problem in 1969 as a qualification problem: for any rule of common sense AI researchers care to represent, tends to be a large number of exceptions. Almost nothing is simply true or false in the way the abstract logical demands. AI research has examined a series solutions to this problem. The extent of common number knowledgeThe sense of atomic facts that the average citizen knows is astronomical. Research projects trying to build a knowledge base full of common sense knowledge (eg, Cyc) require huge amounts Ontological Engineering laborious - to build, by hand, a complex concept at a time. An important goal is to have equipment sufficient to understand the concepts of power learn through reading sources such as the Internet, and thus be able to add their own ontology. The shape of knowledgeMuch subsymbolic little common sense this we know that is not represented as "facts" or "statements" which actually could be said aloud. For example, a master Chess Chess will avoid particular position because it "feels very exposed" or an art critic to take a look the statue and immediately realized it was a fake. These are ideas or trends that are represented in the brain non-conscious and sub-symbolic. Knowledge This information supports and provides a context for symbolic knowledge, conscious. As with the related problem of sub-symbolic reasoning, it is expected that places computer AI or intelligence describes how to represent such knowledge.

Planning

Intelligent agents must be able to set goals and achieve them. You need a way to visualize the future (which should have a representation of the state of the world and be able to make predictions about how their actions change) and be able to make decisions that maximize utility (or "value") options available.

In the classic problems of planning, staff may assume that this is the only thing acting on the world and be safe what the consequences of their actions may be. However, if this is not true, you should check periodically the world conforms to their predictions and must change your plan if necessary, which requires the agent to reason under uncertainty.

The Planning Officer uses cooperation and more competition from many players to achieve a certain goal. Emergent behavior like this is used by evolutionary algorithms and swarm intelligence.

Learning

Machine learning has been the focus of AI research since the beginning. Learning unsupervised, is the ability to find patterns in an input sequence. Supervised learning classification includes both digital and regression. The classification is used to determine that something belongs to the class after seeing a series examples of things in several categories. Regression assumes a serial digital input or output examples and tries to find a continuous function Outputs generated inputs. In reinforcement learning the agent is rewarded for correct answers and penalized for bad. These can be analyzed in terms of decision theory, using concepts such as utility. The mathematical analysis of algorithms automatic learning and their performance is a branch of information theory called the theory of machine learning.

Natural Language Processing

Natural language processing engine provides the ability to read and understand the languages spoken by humans. Many researchers hope that the system sufficiently powerful natural language processing would be able to learn on their own, reading the text available on existing Internet. Some direct applications of natural language processing include information retrieval (or retrieval of text) and translation Automatic.

The movement and handling

ASIMO uses sensors and intelligent algorithms to avoid obstacles and navigate the stairs.

The field of robotics is closely related to avian influenza. The information is necessary for robots to be able to handle tasks such as object manipulation and navigation, with sub-problems of localization (knowing where he is), mapping (learning what is around you) and planning motion (see how to get there).

Perception

The perception of machine capacity utilization of inputs from sensors (as cameras, microphones, sonar and other more exotic) to deduce aspects of the world. Computer vision is the ability to analyze data visual. A small number of sub-problems are speech recognition, facial recognition and object recognition.

Social intelligence

Kismet, a robot with rudimentary social skills

Emotion and social skills play two roles in an intelligent agent. Prime Instead, you should be able to predict the actions of others, understanding their motivations and emotional states. (These are elements of game theory, theory of decision and the ability to model human emotions and perceptual skills to detect emotions.) Also, for good interaction human-computer, an intelligent machine must also show emotions. At least in appearance and friendly interaction with humans. At best, he should have normal emotions himself.

Creativity

Topio, a robot that can play ping-pong, developed by TOSY.

A sub-domain addresses AI creativity both in theory (from a philosophical standpoint and psychological) and practice (via implementations specific systems that generate products that can be considered creative).

The general intelligence

Most researchers hope their work will eventually be integrated on a machine with general intelligence (known as strong AI), combining all the skills above and beyond the human capacity in most or all of them. Some believe that anthropomorphic features like artificial consciousness or an artificial brain may be necessary for this project.

Many AI problems above are considered-complete: to solve a problem we must solve them all. For example, even a single specific task, such as translation machine requires the machine to follow the reasoning of the author (reason), know what you are talking about (knowledge), and the faithful reproduction of the author's intention (social intelligence). Machine translation, therefore, is believed to be AI-complete: it requires a strong AI in May to do and humans can do.

Approaches

There is no unifying theory or a paradigm that guides AI research. Researchers agree on many issues. Some of the oldest unanswered questions are: artificial intelligence to simulate intelligence naturally through the study of psychology or neurology? Or human biology is irrelevant to AI research as the biology of birds is aeronautical engineering? Intelligent behavior can be described using principles simple and elegant (like logic or optimization)? Or is it necessarily the solution of many problems unrelated? Can be reproduced using symbols of high intelligence, as words and ideas? Or a need to "sub-symbolic" processing?

Cybernetics and simulation of the brain

There no consensus on how closely the brain must be simulated.

In the 1940s and 1950s, a number of researchers have explored the relationship between neurology, information theory and cybernetics. Some of them built machines they use electronic networks to display rudimentary intelligence, such as turtles W. Walter Gray & the Beast to Johns Hopkins. Many of these researchers met meetings of the Society of University Princeton and teleological Ratio Club in England. In 1960, this approach has been largely abandoned, although the elements that would be revived in the 1980s.

Symbolic

When access to digital computers has been possible in the mid-1950s, Amnesty International has research begun to explore the possibility that human intelligence can be reduced to the manipulation of symbols. The investigation focused on three institutions: CMU, Stanford and MIT, and each has developed its own style of research. John Haugeland called these approaches to IA "good old IA "or" BAIA ".

Cognitive simulationEconomist Herbert Simon and Alan Newell has studied the ability to solve human problems and attempted formalize, and his work laid the foundations for the field of artificial intelligence and cognitive science, operations research and science management. His research team has conducted psychological experiments to demonstrate the similarities between the solution of human problems and programs (such as their "General Problem Solver ") that were developing. This tradition, focusing on Carnegie Mellon University eventually lead to the development of architecture Soar in the mid 80s. Logic basedUnlike Newell and Simon, John McCarthy said that the machine was not necessary to simulate human thought, but he should try to find the essence of abstract reasoning and problem solving, regardless of whether people use the same algorithms. His laboratory Stanford (SAIL) has focused on the use of formal logic to solve a wide range of issues, including knowledge representation, planning and learning. The logic also focus the work of the University of Edinburgh and other parts of Europe, which led to the development Prolog programming language and the science of logic programming. Anti-sense "or" disorderly "Researchers MIT (like Marvin Minsky and Seymour Papert) found that difficult problems in vision and natural language processing requires ad hoc solutions - which supported there was no simple and general principle (like logic) that reflects all aspects of intelligent behavior. Roger Schank describes his "anti-logic" approaches as "dirty" (as opposed to paradigms "own" the CMU and Stanford). Knowledge bases of common sense (as Doug Lenat Cyc) is an example of "scruffy" AI because they must be built by hand, a complex concept at a time. Computer skills with great memories basedWhen became available around 1970, researchers from the three traditions have begun to construct knowledge in applications of AI. This revolution knowledge, led the development and deployment of expert systems (introduced by Edward Feigenbaum), the first real success as a software artificial intelligence. The knowledge revolution has also been motivated by the belief that vast amounts of knowledge are necessary for several simple applications of AI.

Sub-symbolic

During the 1960s, symbolic approaches have achieved great success in simulation of reflection high-level programs in small demonstration. The cyber-based approaches or neural networks have been abandoned or relegated secondary. In the 1980s, however, progress in symbolic AI appeared and since many believe that the symbolic systems would never be able to reproduce all processes of human cognition, including perception, robotics, learning and pattern recognition. A number of researchers have begun to watch "sub-symbolic" approaches to specific problems of avian flu.

AIResearchers From bottom to top, embodied, situated, based behavior on the field or related new robotics, as Rodney Brooks, rejected the symbolic and IA-based engineering problems base that will allow robots to move and survive. His work is not resumed the symbolic point of view of researchers in cybernetics in the 50s and re-introduced the use of control theory of AI. These approaches are conceptually related to the embodied mind thesis. Computational IntelligenceInterest neural networks and "connection" has been revived by David Rumelhart and others in the 1980 East. These and other sub-approaches symbolic, such as fuzzy systems and evolutionary computing, are now studied together for the new discipline of computational intelligence.

Statistics

In the 1990s, Amnesty International researchers developed sophisticated mathematical tools to solve the subproblems specific. These tools are truly scientific in the sense that their results are quantifiable and verifiable, and were responsible for much of the recent success of AI. Shared mathematical language has also allowed a high level of collaboration with more established areas (math, science economic or research). Stuart Russell and Peter Norvig describe this movement as nothing less than a "revolution" and "the victory of NEATS.

Integration methods

Paradigm intelligent agent intelligent agent is a system that perceives its environment and takes actions which maximizes its chances of success. Intelligent agents are simple programs that solve specific problems. Intelligent agents are more complicated and rational thinking human beings. The paradigm of the license provides researchers to study problems and find solutions that are verifiable and useful, without agreeing on an approach unique. An agent who solves a specific problem, you can use whatever method works - some agents are symbolic and logical, others are sub-symbolic neural networks and others can use new approaches. The paradigm also provides researchers with a common language to communicate with other areas such as decision theory and economics, which also use the concepts of abstract agents. The paradigm of the Smart Agent been widely accepted during the 1990s. ArchitecturesResearchers agent architectures and cognitive systems have been designed to build systems intelligent agents that interact in a multi-agent system. A system with both symbolic and sub-symbolic component is a hybrid intelligent system, and studying these systems is the integration of artificial intelligence systems. A hierarchical control system is a bridge between sub-symbolic AI at their lowest levels reactive traditional symbolic AI and higher levels, where the constraints time to allow flexible planning and modeling world. Rodney Brooks subsumption architecture is a proposal to the top of the hierarchy.

Tools

During the 50 years of research, Amnesty International has developed a number of tools to solve the toughest problems computer. Some of the most general of these methods are discussed below.

Search and optimization

Many AI problems can be solved in theory intelligent search through many possible solutions: the reasoning can be reduced to search. By example, the test logic can be considered as seeking a path that leads to conclusions from premises, where each step is applying an inference rule. Algorithms research planning through the trees of goals and sub-goals, trying find a path to a goal target, a process called means-ends analysis. Algorithms move Robotics members and grasp objects using search local in configuration space. Many learning algorithms use search algorithms based on optimization.

Search comprehensive rarely simple enough for most real-world problems: the search for (the space of places to search) grows rapidly to astronomical figures. The result is a search that is too slow or never complete. The solution to many problems is to use "heuristic" or "golden rules" to eliminate options that are unlikely to lead to the target (called the search tree pruning). Heuristics provide the program to guess a "better" than the path of the solution comes.

A very different type of research, became famous in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to start the search with any response, then gradually improve the proposal until improvements can be made. These algorithms can be considered blind people climbing the hill, we started looking at a random point in the landscape, then jumps or steps, we move our conjecture climb to the summit. Other algorithms, simulated annealing optimization, search and route optimization random.

Evolutionary computation uses a form of search optimization. For example, one can start with a population of organisms (assumptions), then they can to mutate and recombine in selecting only the fittest survive each generation (refining assumptions). Forms of evolutionary computation algorithms including swarm intelligence (eg ant colonies and particle swarm optimization) and evolutionary algorithms (genetic algorithms [103] and genetic programming [104] [105]).

Logic

Logic has been introduced in the survey by AI John McCarthy in his proposal of 1958 Taker Council. The logic is used for knowledge representation and problem solving, but can be applied to other problems. For example, the algorithm uses SATPLAN planning logic and inductive logic programming is a method of learning.

Many different forms of logic used in AI research. The logic is declarative or propositional logic statements can be true or false. First order logic also allows the use of quantifiers and predicates, and can express facts about objects, their properties and relationships with others. Fuzzy logic is a version of the first-order logic that allows the truth of a statement to be represented as a value between 0 and 1, instead of simply true (1) or False (0). Fuzzy Systems can be used for reasoning uncertainties have been widely used in industry modern control and consumer products. Default logic, nonmonotonic logic and definition are forms of logic to help with the reasoning defect and the qualification problem. Several extensions of the logic has been designed to handle specific areas of knowledge such as: description logics, load situation, the calculation of events and calculation of fluid (for the representation of events and time), the calculation of causation, calculation of beliefs, and modal logic.

In 1963, J. Alan Robinson has discovered a simple logical deduction, complete and fully algorithms that can be done easily by digital computers. However, a naive implementation of the algorithm quickly leads to a combinatorial explosion or an infinite loop. In 1974, Robert Kowalski has offered to represent logical expressions, such as Horn clauses (statements in the form of rules: "if P then Q "), which reduced the logical deduction of forward chaining or backward chaining. This very relieved (not eliminate) the problem.

Probabilistic reasoning methods uncertain

Many of the problems of AI (in reasoning, planning, learning, perception and robotics) require the agent to operate with incomplete or uncertain. In the late 80s and 90s, Judea Pearl and others, has defended the use of methods borrowed from probability theory and economics to develop a set of powerful tools for solving these problems.

Bayesian networks are a general tool that can be used for many problems, reasoning (using the Bayesian inference algorithm), trained (with the maximization algorithm pending), planning (using decision networks) and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used for filtering, prediction, smoothing and searching for explanations of the data stream by helping collection systems for analyzing processes that occur over time (eg, Hidden Markov Models or Kalman filters).

A key concept of economics is "useful": a measure of how valuable a thing is an intelligent agent. Precise mathematical tools were developed to analyze how an agent can make decisions and plan, using decision theory, decision analysis, the theory of value of information. These tools include models such as the decision process Markov decision networks dynamics, game theory and mechanism design.

Classifiers and statistical learning methods

The simplest form of AI applications can be divided into two types: classifiers ( "If shiny then Diamond") and controllers ( "if shiny then pick up"). Pilots, however, also classified the conditions before deduction actions, and therefore classification is central to many AI systems. Classifiers are functions that match the way to use to determine a closest match. You can adjust if necessary, make them very attractive for use in AI. These examples are known as observations or models. The supervised learning, each pattern belongs to a predetermined class. A class can be considered as a decision must be made. All the observations combined with class labels is known as a data set. When a new observation, that observation is classified according to the experience Prev.

A classifier can be trained in various forms, there are many statistical methods machine learning. The algorithms most commonly used are neural networks, kernel methods like support vector machine, k-plus nearest neighbors, Gaussian mixture model, naive Bayes classifier and decision tree. The performance of these classifiers were compared a series of tasks. Classifier performance depends greatly on the characteristics data to classify. There is no single classification that works best on all given problems, which is also known as the "no free lunch theorem". The determination of a classifier appropriate for a given problem is more an art than a science.

Neural networks

A neural network is a group of nodes interconnected, similar to a vast network of neurons in the human brain.

The study of artificial neural networks began in the decade before the search AI field was founded in the work of Walter Pitts and Warren McCullough. Another prominent researchers to Frank Rosenblatt, who invented the perceptron and Paul Werbos who developed the backpropagation algorithm.

The main categories of networks are acyclic or non-recurring neural networks (when the signal goes in one direction) and recurrent networks (which allow feedback). Among the most popular networks are perceptrons proactive, multi-layer perceptrons and radial basis networks. Among the recurrent networks, the most famous is the Hopfield network, as network attraction, which was first described by John Hopfield in 1982. The neural networks can be applied to the problem of intelligent control (for robotics) or learning, using techniques such as learning and competitive Hebbian learning.

Jeff Hawkins argues that research on neural networks has stalled because the model lacks the essential properties of the neocortex, and proposed a model (hierarchical temporal memory) based on neurological research.

Control theory

Control theory, the little son-of cybernetics, has many important applications, including robotics.

Languages

Researchers from Amnesty International have developed several specialized languages for AI research such as Lisp and Prolog.

Assessing progress

How can you determine if an agent is intelligent? In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent is now known as the test Turing. This procedure allows almost all the major problems of artificial intelligence test. However, it is a difficult challenge and not currently all agents.

Artificial intelligence can also be evaluated on specific problems of small problems such as chemistry, handwriting recognition and role playing. These tests were called skilled in the Turing test. Problems offer little more achievable goals and there is a growing number of positive results.

The general classes of test results for avian influenza are:

• Optimus: You can not do better
• Strong super-human: performs better than all humans
• Super-human: performs better than most human
• Sub-human: performs worse than most humans

For example, optimal performance in projects, performance chess is super-human and super-human strength, and performance in many daily tasks performed by man is subhuman.

A very different approach to intelligence measures across the test machine was developed from mathematical definitions intelligence. Examples of such tests in the early nineties the development of intelligence tests using the concepts of complexity Kolmogorov and data compression. Definitions are like artificial intelligence presented by Marcus Hutter in his book Universal Artificial Intelligence (Springer 2005), an idea developed by Legg and Hutter. Two major advantages of definitions in mathematics is its applicability to non-intelligence rights and its lack of a requirement for assessors of man.

Applications

Artificial intelligence has been used successfully in a wide range of fields including medical diagnosis, stock transactions, control robots, the right to discovery science, video games, toys and Web search engines. Often, when there is a technique for general use, is more artificial intelligence is considered, sometimes described as the effect of AI. Also be integrated into artificial life.

Contests and Awards

There are a series of competitions and prizes to encourage research in artificial intelligence. The main areas of promotion are: intelligence machine in general, behavior, conversation mining, driverless cars, robots and soccer games.

Platforms

A platform (or "computing platform") is defined by Wikipedia as "a sort of hardware architecture or a software framework (including application frameworks), which allows software to run. "Rodney Brooks, as pointed out several years ago, not only artificial intelligence software, which defines the characteristics of the IA platform, but rather the same platform which affects the IA results, ie we must solve the problems of AI in the platforms of the real world and not in isolation.

A wide variety of platforms has enabled the various aspects of AI development, ranging from expert systems, although based on PC, but being around a real system platforms diverse global robot, the Roomba as widely available open interface.

Philosophy

Intelligence artificial, claiming to be able to recreate the capabilities of the human mind is both a challenge and an inspiration for philosophy. Are there limits to how intelligent machines can be? Is there an essential difference between intelligence and human intelligence artificial? A machine can have a mind and consciousness? Some of the most influential answers to these questions below.

"Turing Convention politeness" If an intelligent machine that acts as a human being, then he is as intelligent as humans theory. Alan Turing, that finally we can judge the intelligence of a machine based in behavior. This theory is the basis of the proposal test.The Turing Dartmouth "Each aspect of learning or any other characteristic of intelligence can be described with such precision that the machine can To simulate this. "This statement was printed in the proposal for the Dartmouth Conference in 1956, and represents the position of most work insemination of researchers.Newell Simon and physical symbol system hypothesis "a physical symbol system has the necessary and sufficient to act with intelligence in general. "Newell and Simon argue that intelligence consists of formal operations symbols. Hubert Dreyfus argued that, contrary to human experience depends on instinct more unconscious than conscious manipulation of symbols and a "feel" of the situation rather than explicit symbolic knowledge. (See Dreyfus criticism of AI.) Incompleteness theorem of Gödel An officer system (like a computer program) can not prove all true statements. Roger Penrose is one of those who claim that the theorem Gödel limit what machines can do. (See The Emperor's New Mind). Searle strong AI hypothesis "The appropriately programmed computer with entrances and exits to the right, which had a mind in exactly the same sense of human beings have a spirit. "Searle counters this assertion with the Chinese room argument, which asks us to look inside the computer and try to find where the spirit "argument may be.The artificial brain, the brain can be simulated. Hans Moravec, Ray Kurzweil and others have argued that it is technically possible to copy the brain directly in hardware and software, and that this simulation will be essentially identical to the original.

Speculation and fiction

AI is a common theme in both science fiction and projections about the future of technology and society. The existence artificial intelligence that rivals human intelligence raises difficult ethical issues and the potential power of technology inspires both hope and fear.

Frankenstein by Mary Shelley is considered a key issue in the ethics of intelligence artificial: if a machine can be created with intelligence, you feel too? If you can not experience, has the same rights as human being? The idea also appears in modern science fiction: the film Artificial Intelligence: AI considers a machine in as a small child has been given the ability to feel human emotions, including, unfortunately, the capacity to suffer. This problem now known as "human robot", is currently studying, for example, the California Institute for the future, although many critics believe that the debate is premature.

Another issue explored both by science fiction writers and futurists is the impact of artificial intelligence in society. In fiction, Amnesty International has published several roles, among them;

• As official (R2D2 from Star Wars)
• As a representative of the law (KITT "Knight Rider")
• Already a colleague (Lieutenant Commander Data in Star Trek)
• As a conqueror / Overlord (Matrix)
• As a dictator (With hands folded)
• As a murderer (Terminator)
• As a race sentias Battlestar Galactica)
• As an extension of human abilities (Ghost in the Shell)
• El Salvador as the human race (R. Daneel Olivaw in the series Foundation).

Academic sources have considered the consequences such as lower demand for human labor, improved human capacity or experience, and the need for a redefinition of identity and basic human values.

Several futurists argue that artificial intelligence goes beyond the limits of progress and fundamentally transform humanity. Ray Kurzweil has used Moore's Law (which describes the relentless exponential improvement in digital technology amazing precision) to calculate what computers office have the same processing power as human brains by the year 2029, and in 2045 artificial intelligence will reach a point where it can improve at a rate that far exceeds anything imaginable in the past, a writer Vernor Vinge on science fiction called the "technological singularity". Edward Fredkin argued that "artificial intelligence is the next step in evolution", an idea advanced by Darwin, Samuel Butler's Among the Machines "(1863), and enlarged by George Dyson in his book of the same name in 1998. Several writers of futuristic science-fiction writers have predicted that humans and machines will merge in the future into cyborgs that are more capable and more powerful than either. This idea, called transhumanism, which has roots in Aldous Huxley and Robert Ettinger, is now associated with robot designer Hans Moravec, cybernetics Kevin Warwick and inventor Ray Kurzweil. Transhumanism has been illustrated in fiction, for example in the manga Ghost in the Shell series of science fiction Dune. McCorduck Pamela wrote that these scenarios are expressions of human will to the former, as she calls it, "forge of the gods."

About the Author

S. Rajkumar belongs to Madurai, Tamil nadu, India. He is a post graduate in Computer Science and Information Technology. Now he is working as a web designer and PHP programmer in AJ Square Inc. Vilacherry, Madurai.