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Artificial intelligence

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

The camp was founded on the assertion that a central property of human beings, intelligence-the wisdom of Homo sapiens can be so precisely described that can be simulated by a machine. This raises philosophical questions about the nature of the mind and the limits of scientific arrogance, issues have been addressed by the myth, fiction and philosophy since antiquity. The artificial intelligence of optimism has been impressive, has suffered setbacks spectacular and today has become an essential part of the technology industry, providing the heavy lifting to many of the most difficult problems in science computer.

AI research is highly technical and specialized, deeply divided into subfields that often do not communicate with each other. Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, long-standing differences of opinion about of how IA should be done and the implementation of a comprehensive different tools. The central problems of AI includes features such as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or "strong AI") remains a long-term (some research).

History

Thinking machines and artificial beings appear in Greek myths as Talos of Crete, the golden robots of Hephaestus and Pygmalion's Galatea. Human similarities believes that intelligence is built into all major civilizations: animated statues were worshiped in Egypt and Greece and humanoid robots were built by Yan Shi, Heron of Alexandria, Al-Jazari and Wolfgang von Kempelen. There was also a widespread belief that artificial beings have been created by J? Bir ibn Hayy? No, Judah Loew and Paracelsus. 19 A and 20 centuries, the artificial beings had become a common feature fiction, as in Mary Shelley's Frankenstein or Karel? APEK of RUR (Rossum's Universal Robots). McCorduck Pamela argues that these are all examples of an ancient need, as she describes it, "to forge the gods." The stories of these creatures and their fates discuss many of the same hopes, fears and ethical concerns that are presented by artificial intelligence.

Mechanical or formal "reasoning has been developed by philosophers and mathematicians from antiquity. The study of logic led directly to the invention of digital programmable computer, thanks to the work of mathematician Alan Turing and others. the theory of computation that Turing proposed a machine, the exchange of symbols as simple as "0" and "1" could simulate any act conceivable mathematical deduction. This, together with recent discoveries in neuroscience, information theory and cybernetics, inspired in a small group of 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 participants, including John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, became leaders of AI research for many decades. They and their students wrote programs that, for most, simply amazing: computers were solving problems of words in algebra, logic theorem proving and speaks English. In the mid 1960s, research in the U.S. was funded largely by the Defense Department and the laboratories had been established around the world. AI's founders were deeply optimistic about the future of the new field: Herbert Simon predicted "The machines will be capable, within twenty years, performing any work that a man can do" and Marvin Minsky agreed, writing that " within a generation … the problem of creating 'artificial intelligence' will be resolved significantly. "

They did not recognize the difficulty some of the problems they face. In 1974, responding to criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, U.S. and Britain cut all government without direction, exploratory research in AI. The next few years, when the project funding was difficult to find, later became a winter "AI."

In the 1980s, AI research was revived by the success commercial expert systems, a form of artificial intelligence program that simulates the knowledge and analytical skills of one or more human experts. In 1985 the market Avian influenza had reached more than one billion dollars. At the same time, Japan's fifth-generation project inspired by the U.S. computer and governments Britain to restore funding for academic research in the field. However, from the market collapse in 1987 Machine Lisp, AI, again fell into disrepute, and a second, more lasting winter began AI.

In the 1990s and the 21st century AI achieved its greatest successes, but somewhat behind the scenes. The artificial intelligence is used for logistics, data mining, medical diagnosis and many other areas across the industry technology. The success was due to several factors: the incredible power of today's computers (see Moore's Law), a greater emphasis on solving of specific subproblems, the creation of new ties between AI and other fields working on similar problems, and above all a new commitment by researchers to the methods sound and rigorous mathematical scientific standards.

Problems

The problem of simulating (or create) the intelligence has been disclosed in a series of specific sub-problems. These consist of particular features or capabilities that researchers would like an intelligent system to display. Features described below have received the most attention.

Deduction, reasoning, problem solving

The first researchers developed AI algorithms that mimic the reasoning step that humans use when they solve puzzles, board games or make logical deductions. In the late 1980s and '90s, AI research has also developed highly successful methods to deal with uncertain or incomplete information, employing concepts of probability and economics.

For difficult problems, most of these algorithms may require huge computing resources – most 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 solution efficient algorithms is a high priority problem for AI research.

Human beings solve most problems with the quick use, instead deduction of intuitive judgments conscious, step by step at the beginning of research in AI was able to model. AI has made some progress to mimic this type of "Sub-symbolic" Troubleshooting: embedded approaches emphasize the importance of sensorimotor skills to a higher reasoning, neural research attempts network to simulate the internal structures of human and animal brain that gives rise to this ability.

Knowledge Representation

The representation knowledge and engineering knowledge are fundamental to AI research. Many of the problems machines are expected to solve will require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relationships between objects, situations, events, states and time, the causes and effects, knowledge about knowledge (what we know about what people know), and many other less investigated domains. A complete representation of "what exists" is an ontology (to borrow a word from the traditional philosophy), of which the most general are called upper ontologies.

Among the most difficult problems in knowledge representation are:

By default problemMany and reasoning skills of the things that people know take the form of "working hypothesis." For example, if a bird comes into the conversation, people tend to picture an animal that is the fist-sized, singing, and flies. None of these things are true about all birds. John McCarthy in 1969 identified this problem as the problem of classification: for any rule of common sense that AI researchers to represent the attention tends to be a large number of exceptions. Almost nothing is simply true or false in the way that abstract logic required. IA research has explored a number of solutions to this problem. The breadth of common sense knowledgeThe number of states of affairs that the average person knows is astronomical. Research projects that attempt to build a comprehensive knowledge base of common sense knowledge (eg, Cyc) require huge amounts of laborious ontological engineering – must be built, by hand, a complicated concept at a time. An important goal is to have the team to understand concepts to be able to learn by reading sources such as the internet, and thus be able to add their own ontology. The shape of some subsymbolic knowledgeMuch sense of what people knows that it is represented as "facts" or "statements" that actually could be said aloud. For example, a chess master to avoid a position particular chess because "it feels very exposed" or an art critic can take a look at a statue and instantly realize that it is a forgery. It is intuitions or tendencies are represented in the brain does not consciously and sub-symbolically. Knowledge of this information, supports and provides a context for understanding symbolic conscious. As with the related problem of the sub-symbolic reasoning, it is expected that places computer AI or intelligence describes how to represent this kind of knowledge.

Planning

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

In classical planning problems, the agent can assume that is the only act upon the world and you can be sure what the consequences their actions may be. However, if this is not true, you should check periodically if the world agrees with their predictions and that must change your plan as necessary, requires the agent to reason under uncertainty.

planning using multi-agent cooperation and competition of many actors to achieve a certain goal. Emergent behavior like this is used by evolutionary algorithms and swarm intelligence.

Learning

Machine learning has been central to AI research from the beginning. Unsupervised learning is the ability to find patterns in an input current. Supervised learning includes both numerical classification and regression. The classification is used to determine what something belongs to the class after seeing a number of examples of things several categories. Regression takes a set of numerical data / output examples and attempts to find a continuous function that generate the outputs from the inputs. In reinforcement learning the agent is rewarded for a good response and punished for the bad. These can be analyzed in terms of decision theory, using concepts as public services. The mathematical analysis of learning algorithms and their performance is a branch of theoretical computer science theory known as computational learning.

Natural language processing

natural language processing engine gives the ability to read and understand the languages humans speak. Many researchers hope that sufficiently powerful system of natural language processing could acquire knowledge on their own, reading the existing text available through the Internet. Some direct applications include natural language processing, information retrieval (or text mining) and translation automatically.

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. Intelligence is necessary for robots capable of handling tasks such as manipulating objects and Navigation, with sub-location problems (know where), mapping (learning what's around you) and the planning of movement (check directions).

Perception

perception of the machine is the ability to use input from sensors (such as cameras, microphones, sonar and other more exotic) to deduce aspects of the world. Computer vision is the ability to analyze visual information. A selected subproblems are recognition voice, facial recognition and object recognition.

Social intelligence

Kismet, a robot with rudimentary social skills

The excitement and social skills play two roles in an intelligent agent. First, it must be able to predict the actions of others, to understand their motives and emotional states. (These are elements of game theory, decision theory, as well as the ability to model human emotions and perceptual skills to detect emotions.) In addition, for the good human-computer interaction, an intelligent machine also needs to show emotions. At least to appear gentle and sensitive human beings interacting. At best, should have normal emotions himself.

Creativity

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

A sub-field of AI addresses creativity in theory (from a philosophical point of view and psychological) and practically (through implementations specific systems that generate products that can be considered creative).

General intelligence

Most researchers expect His final work will join a team with general intelligence (known as strong AI), combining all previous powers higher than human capacity in most or all of them. Some believe that anthropomorphic features like artificial consciousness or an artificial brain that may be required for project.

Many of the problems above are considered AI-complete: to solve a problem you must solve them all. For example, even a simple as a particular task machine translation requires the machine to follow the author's argument (reason), know what you're talking about (knowledge), and faithfully reproduce the author's intention (social intelligence). Machine translation, therefore, is believed to be AI-complete: it may require strong AI to be done, and beings humans can do.

Approaches

There is no unifying theory or paradigm that guides AI research. Researchers are agree on many issues. Some of the longest standing questions that remain unanswered are: artificial intelligence to simulate natural intelligence through the study of psychology and neuroscience? Or human biology have no interest in AI research as the biology of birds is to engineering aeronautics? behavior can be described using simple smart, elegant principles (eg, logic or optimization)? Or is necessarily required the solution of a large number of completely unrelated problems? Can be reproduced with high-level intelligence symbols, similar to the words and ideas? Or do they require "sub-symbolic" treatment?

Cybernetics and the brain simulation

There is no consensus on how about the brain must be simulated.

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

Symbolic

When access to computers digital was made possible in the 1950s average, AI research began 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 developed his own style of research. John Haugeland name to the approaches to AI "good old-fashioned AI" or "BAIA".

Cognitive simulationEconomist Herbert Simon and Alan Newell studied human problem-solving skills and tried to formalize, and his work laid the foundations of the field of artificial intelligence, science, and cognitive, operational research and management. His research team conducted psychological experiments to demonstrate the similarities between the solution of human problems and programs (such as their "General Problem Solver ") that were developed. This tradition, focusing on the Carnegie Mellon University finally culminate in the development of architecture in the early half Soar 80. Logic basedUnlike Newell and Simon, John McCarthy felt that equipment was not necessary to simulate human thought, but should try to find the essence of the argument abstract problem solving, regardless of whether people use the same algorithms. His lab at Stanford (SAIL) focused on the use of formal logic solve a wide variety of problems, including knowledge representation, planning and learning. The logic also became the focus of the work of the University of Edinburgh and other parts of Europe, which led to the development of Prolog programming language and the science of logic programming. "Anti-logic" or "sloppy" Researchers at MIT (eg, Marvin Minsky and Seymour Papert) found that the solution of difficult problems in vision and natural language processing requires ad-hoc solutions – which held that there was no simple and general principle (like logic) that captures all aspects of intelligent behavior. Roger Schank described its "Anti-logic 'approach as" sloppy "(as opposed to" clean "paradigms in CMU and Stanford). knowledge bases common sense (for example, Doug Lenat's Cyc) is an example of "sloppy" AI, since they must be built by hand, a complicated concept at a time. computers Knowledge basedWhen large memories were available around 1970, researchers from the three traditions began to build knowledge in AI applications. This "revolution of knowledge "led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of artificial intelligence software. The knowledge revolution was also fueled by the realization that vast amounts of knowledge would be necessary in many simple applications of AI.

Sub-symbolic

During the 1960s, symbolic approaches had achieved great success in the simulation of high-level thinking in programs small demonstration. The cyber-based approaches and neural networks were abandoned or pushed into the background. In the 1980s, however, progress seemed symbolic AI in place and many believed that the symbolic systems would never be able to mimic all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began looking at "sub-symbolic" approaches to specific problems avian influenza.

From bottom to top, embodied, situated, behavior-based AIResearchers or the nouvelle field of robotics, such as Rodney Brooks, rejected the symbolic AI and focused on basic engineering problems that allows robots that move and survive. His work revived the point of view non-symbolic Early researchers in cybernetics from the 50 and returned to introduce the use of control theory in AI. These approaches are also conceptually related the embodied mind thesis. IntelligenceInterest Computational neural networks and "connectionism" was revived by David Rumelhart and others in the 1980 half. These and other sub-symbolic approaches such as fuzzy systems and evolutionary computation, are studied together for the emerging discipline of computational intelligence.

Statistical

In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. These tools are truly scientific in the sense that its results are measurable, verifiable and have been responsible for many of the recent successes of Amnesty International. The shared mathematical language has also allowed a high level of collaboration with more established fields (like mathematics, economics or operations research). Stuart Russell and Norvig, Peter described the move as nothing less than a "revolution" and "the victory of the neats."

Integrating approaches

Intelligent agent Intelligent Agent paradigm is a system that perceives its environment and takes actions which maximizes its chances of success. The easiest way intelligent agents are programs that solve specific problems. Intelligent agents are more complicated rational, human thought. The paradigm License gives researchers to study isolated problems and find solutions that are verifiable and useful, however, agree on a single approach. An agent who decides a specific problem you can use any method that works – some agents are symbolic and logical, some are sub-symbolic neural networks and others can use new approaches. The paradigm also provides researchers with a common language to communicate with other fields such as decision theory and economics, which also use abstract concepts of agents. The intelligent agent paradigm was widely accepted during the 1990s. Agent architectures have cognitive architecturesResearchers intelligent systems designed to build intelligent agent systems that interact in a multi-agent system. A system with both symbolic components and sub-symbolic-is a hybrid intelligent system, and the study of these systems is integrating artificial intelligence systems. A hierarchical control system is a bridge between sub-IA symbolic in its lowest, the levels of reactive and traditional symbolic AI at its highest level, where time constraints relaxed enable planning and modeling world. architecture Rodney Brooks' subsumption was proposed earlier this hierarchical system.

Tools

In the course of 50 years research, Amnesty International has developed a number of tools to solve the most difficult problems in computer science. Some Generals more of these methods are discussed below.

Search and optimization

Many AI problems can be solved wisely, in theory, searching through many possible solutions: Reasoning can be reduced to a search. For example, a logical test can be seen as seeking a path that leads from premises to conclusions, where each step is the application of an inference rule. Planning search algorithms through trees of goals and sub-goals, trying to find a path towards a target goal, a process called means-ends analysis. Robotics algorithms to move the limbs, and grasp objects using local search in configuration space. Many learning algorithms use search algorithms based on optimization.

simple exhaustive search is rarely sufficient for most real world problems: the search space (the number of places to search) quickly grows to astronomical numbers. 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 the options that will probably not lead to the target (called "pruning the search tree). Heuristics supply the program with a "best estimate" of what the road is in the solution.

A very different type of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, you may start searching for some kind of answer and then Guess tune incrementally until no further improvements can be made. These algorithms can be viewed as blind people climbing the hill: the search began at a point random in the landscape and then by leaps or steps, our assumption we keep moving uphill, until you reach the top. Other annealing optimization algorithms simulated road search and random optimization.

Evolutionary computation uses a form of search optimization. For example, you can start with a population of organisms (guess) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining conjecture). Forms of evolutionary computation include swarm intelligence algorithms (such as ant colony and particle swarm optimization) and algorithms evolution (eg genetic algorithms [103] and genetic programming [104] [105]).

Logic

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

Various forms of logic used in AI research. Propositional or sentential logic is the logic of statements that 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 their relations with others. Fuzzy logic is a version of 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 uncertain reasoning have been widely used in modern industrial and consumer product control. Default logic, nonmonotonic logic and circumscription are forms of logic designed to help with default reasoning and the problem of classification. Several extensions of logic has been designed to handle specific areas of knowledge such as: description logics, situation calculus, calculus the event and the fluid calculation (for the representation of events and time), calculation of causality beliefs calculation and modal logic.

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

Probabilistic methods uncertain reasoning

Many of the problems of AI (in reasoning, planning, learning, perception and robotics) require agents to operate with incomplete or unclear information. From the late 80s and early 90s, Judea Pearl and others defended the use of methods derived from the theory probability and economics to develop a set of powerful tools to solve these problems.

Bayesian networks are a general tool that can be used for a large number of problems: reasoning (using the Bayesian inference algorithm), learning (using the algorithm of expectation-maximization), planning (Using decision networks) and perception (using dynamic Bayesian networks). probabilistic algorithms can also be used for filtering, prediction, smoothing and the search for explanations for the data flows, helping the perception systems to analyze the processes that occur over time (eg, models, hidden Markov and Kalman filters).

A key concept of the science of economics is the "utility": a measure of the value of something is an intelligent agent. precise mathematical tools have been 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 Markov decision processes, dynamic decision networks, game theory and mechanism design.

Classifiers and statistical learning methods

The easiest way AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). But drivers also classify conditions before deducting the shares, and therefore the classification is part of many AI systems. Classifiers are functions that use the coincidence patterns to determine a closest match. It can be adjusted according to the examples that make them very attractive for use in AI. These examples are known as the observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a decision has to be done. All combined observations with class labels is known as a data set. When a new observation is received, that observation is classified based on past experience.

A classifier can be trained in various forms, there are many statistical and machine learning methods. The most widely used classifiers are the neural network methods kernel, such as SVM, algorithm k-nearest neighbor, Gaussian mixture model, naive Bayes classifier and the decision tree. The performance of these classifiers have been compared in a wide range of tasks. Classifier performance depends greatly on the characteristics of the data are classified. 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 an appropriate classifier for a given problem is still more art than science.

Neural Networks

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

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

The main categories of acyclic networks or feedforward neural networks (when the signal goes in one direction) and recurrent networks (which allow feedback.) Among the most popular feedforward networks are perceptrons, multilayer perceptrons and radial basis networks. Among the recurrent networks, the most famous is the Hopfield network, a form of attractor network, which was first described by John Hopfield in 1982. Neural networks can be applied to the problem of intelligent control (The robot) or learning, using techniques such as Hebbian learning and competitive learning.

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

Control theory

Control theory, the grandson of cybernetics, has many important applications, especially in robotics.

Languages

AI researchers have developed several specialized languages for AI research, including Lisp and Prolog.

Evaluate progress

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

Artificial intelligence can also be evaluated on specific problems such as the small problems of chemistry recognition of handwriting and playing games. These tests have been known expert on the subject of Turing tests. Small problems and provide more attainable goals is an increasing number of positive results.

The general classes of test results for the IA are:

• Best: You can not have better performance
• Superhuman strength: it performs better than all human beings
• Super-human: performs better than most humans
• Sub-human: performs worse than most humans

For example, the performance is optimal for the ladies, the performance in is super-human chess and near super-human strengths, and performance in many daily tasks performed by humans is sub-human.

A very different approach measures machine intelligence through tests that are developed from mathematical definitions of intelligence. Examples of this type of testing will begin in the late nineties the development of intelligence tests with notions of Kolmogorov complexity and data compression. Similar definitions of artificial intelligence are presented by Marcus Hutter in his book Universal Artificial Intelligence (Springer 2005), an idea developed by Legg and Hutter. Two major advantages of the definitions mathematics is its applicability to non-human intelligence and the absence of a requirement for the human evaluators.

Applications

Artificial intelligence has been used successfully in a wide range of fields including medical diagnosis, stock trading, robot control, law, scientific discoveries, video games, toys, and Internet search engines. Often when you get to use a current technique, which is no longer considered artificial intelligence, sometimes described as the effect of avian influenza. It can also be integrated into artificial life.

Competitions and awards

There are a number of competitions and prizes promote research in artificial intelligence. The main areas promoted are: machine intelligence in general, conversational behavior, data-mining, cars driverless, robot football games.

Platforms

A platform (or "computing platform") is defined by Wikipedia as "a kind of hardware architecture or software framework (including application frameworks), which allows software to run." As noted Rodney Brooks For many years, not just artificial intelligence software that defines the AI of the platform, but rather the platform itself that affects IA results, ie we should be solving problems of AI in real-world platforms and not separately.

A wide variety of platforms has allowed that different aspects of AI development, ranging from expert systems, although based on PC, but being around a real world system for various robotic platforms Roomba widely available as the open interface.

Philosophy

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

Turing polite convention "is" If an intelligent machine acts as a human being, then it is as intelligent as a human being Alan Turing. Theory that ultimately, we can only judge the intelligence of a machine based on their behavior. This theory forms the basis Dartmouth's proposal Turing test.The "Every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it. "This statement was printed in the proposal for the Dartmouth Conference of 1956 and represents the position of most researchers.Newell IA and physical work of Simon symbol system hypothesis "A physical symbol system has the necessary and sufficient general intelligent action. "Newell and Simon argue that intelligence consists of formal operations on symbols. Hubert Dreyfus argued that on the contrary, human experience depends on unconscious instinct than conscious symbol manipulation and of having a "feel" of the situation symbolic knowledge rather than explicit. (Dreyfus' critique of AI Headquarters.) Gödel's incompleteness theorem officer system (Like a computer program) can not prove all true statements. Roger Penrose is one of those who say that the limits of what the machines can Gödel's theorem do. (See The New Mind.) Emperor Searle strong AI hypothesis "The computer programming with appropriate inputs and outputs on the right, which would have a mind in exactly the same human beings have felt the mind. "Searle counters this statement with his Chinese room argument, that invites us to look inside the computer and try to find where the "mind" could be.The artificial brain argument may be brain simulated. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly in hardware and software, and that the simulation will essentially identical to the original.

The speculation and fiction

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

Mary Shelley's Frankenstein considered a key issue in the ethics of artificial intelligence: if a machine can be created that has intelligence, you also feel? If you can feel, has the same rights as a human being? The idea also appears in the modern science fiction: the film Artificial Intelligence: AI considers a machine in the form of a small child who has the ability to feel human emotions, including, unfortunately, the capacity to suffer. This issue, now known as rights of "robot", is currently being studied, for example, California Institute for the Future, though many critics believe that the discussion is premature.

Another theme explored by both science fiction writers and futurists is the impact of artificial intelligence in society. In fiction, AI has appeared fulfilling many roles including;

• As a servant (R2D2 Star Wars)
• As a law enforcement officer (KITT "Knight Rider")
• As a partner (Lieutenant Commander Data in Star Trek)
• As a conqueror / M. (The Matrix)
• As a dictator (With Folded Hands)
• As a murderer (Terminator)
• As Battlestar Galactica career sentiant)
• As an extension human capabilities (Ghost in the Shell)
• As El Salvador of the human race (R. Daneel Olivaw the Foundation series.)

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

Several futurists argue that artificial intelligence beyond the limits of progress and, most importantly, transform humanity. Ray Kurzweil has used Moore's Law (which describes the relentless exponential improvement in digital technology with amazing precision) to calculate that desktop computers have the same power as human brains by the year 2029, 2045 and that artificial intelligence will reach a point where it is able to improve itself at a rate that far exceeds anything imaginable in the past, a scenario that science fiction writer Vernor Vinge name technological singularity. "says Edward Fredkin" artificial intelligence is the next stage in evolution, "an idea first proposed by Samuel Butler Darwin Among the Machines "(1863), and enlarged by George Dyson in his book of the same name in 1998. A number of futurists and science fiction writers have predicted that humans and machines will merge in the future into cyborgs that are more capable and powerful than anyone else. This idea, called transhumanism, which has its roots in Aldous Huxley and Robert Ettinger, is now associated with robot designer Hans Moravec, Kevin Warwick and inventor Ray Kurzweil cyberspace. Transhumanism has been illustrated in fiction, so for example in the Spirit in the Shell manga and science fiction series Dune. McCorduck Pamela writes that these scenarios are expressions of the will ancient human, as she says, "to forge 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.

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