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Artificial intelligence

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

The field is founded upon a claim that a central property of humans, intelligence, the brains of homo sapiens, can be so precisely described This is simulated by a machine. This raises philosophical issues about the nature of mind and limits of scientific hubris, issues which are addressed by of myth, fiction and philosophy since the first time. Artificial intelligence has been the subject of optimism notably, has suffered stunning setbacks and, now, has become a critical part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.

AI research are highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how to do AI and applications tools of many differing. The central problem of the AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and use objects. General intelligence (or "strong AI") is still a long-term goal of (some) research.

History

Thinking machines and artificial beings appear in Greek myths, such as knows of Crete, the golden robots of Hephaestus and Pygmalion's Galatea. Human likenesses believe that intelligence was built in every major civilization, animated statues were worshiped in Egypt and Greece and humanoid automatons are developed by Shi Yan, Hero of Alexandria, Al-Jazari and Wolfgang von Kempelen. It is also widely believed that artificial beings created by J? BIR ibn forever? n, Judah Loew and Paracelsus. By the 19th and 20th century, artificial beings has become a common feature in fiction, as in Mary Shelley's Frankenstein or Karel? apek's RUR (Rossum's Universal Robots). McCorduck Pamela argues that all of these are examples of an ancient urge, as he describes it, "to forge the gods". Mythical creatures and their fates discuss many of the same hopes, fears and ethical concerns presented by artificial intelligence.

Mechanical or "formal" reasoning was developed by philosophers and mathematicians since the beginning of time. The study of logic that led directly to the development of programmable electronic digital computer, based on the work of mathematician Alan Turing and others. Turing's theory of computation suggested that a machine, by shuffling symbols as simple as "0" and "1", can simulate any conceivable act of mathematical deduction. This, plus the latest discoveries in neurology, information theory and cybernetics, inspired a small group of researchers to begin to seriously consider the possibility of building an electronic brain.

AI research field was established at a conference on the campus of Dartmouth College in the summer of 1956. The attendees, including John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, became the leaders of AI research for many decades. They and their students wrote programs, the majority of people, just wonderful: the computer is solving word problems in algebra, proving logical theorems and speaking English. By the middle of the 1960s, research in the U.S. is heavily funded by the Department of Defense and Laboratories will be established throughout the world. AI's founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that "machines are capable, within twenty years, of doing any work a man can do "and Marvin Minsky agreed, writing that" within a generation ... the problem of making of 'artificial intelligence' will substantially be solved.

They failed to recognize the difficulty of some of the problems they face. In 1974, in response to criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more productive projects, the U.S. and British government cut off all undirected, testing research in AI. The next few years, when funding for the project is hard to find, was later called an "AI winter".

In the early 1980s, AI research has revived the commercial success of expert system, a form of AI program that simulated the knowledge and analytical skills of one or more people experts. By 1985 the market for AI has reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S. and British government to restore funding for academic research in the field. However, since the collapse of the market stutter Machine in 1987, AI once again fell into disrepute, and a second, with no lasting AI winter began.

The 1990s and early 21st century AI achieved its greatest success, though somewhat behind landscape. Artificial intelligence is used for logistics, data mining, medical diagnosis and many other places throughout the technology industry. The success is due on several factors: the incredible power of computers today (see Moore's law), a greater emphasis on solving specific subproblems, the development of new relationships between AI and other fields working on similar problems, and above all a new commitment by researchers at the solid mathematical methods and rigorous scientific standards.

Problem

The problem of simulating (or creating) intelligence is broken down into a number of specific sub-problems. These consist of particular traits or abilities that the researchers wanted to show an intelligent system. The traits described below are received the most attention.

Inference, reasoning, problem solving

Early AI researchers developed algorithms that imitated the step-by-step reasoning to use the humans when they solve puzzles, play board games or make logical deductions. In the late 1980s and '90s, AI research has also developed highly successful procedures for dealing with uncertain or incomplete information, employing concepts from probability and economics.

For that problem, most algorithms may require enormous computational resources - most experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical if the problem goes beyond a certain size. The search for better problem solving algorithm is a high priority for AI research.

Most human beings to solve their problems using the quick, intuitive judgments rather than more, step-by-step argument that early AI research is able to model. AI has made some progress to copy this kind of "Sub-symbolic" solve the problem, embodied approaches emphasize the importance of sensorimotor skills in higher reasoning, neural net research attempts to mimic the structure within human and animal think that gives rise to this skill.

Knowledge Representation

Knowledge representation and knowledge engineering is central to AI research. Many of the problems machines are expected to solve require extensive knowledge about the world. Among the things that AI needs to represent are: objects, properties, categories and relationships between things; situations, events, states and time, cause and effect, knowledge about knowledge (what we know about what other people know) and many other, better researched domain. A complete representation of "what exists" is an ontology (borrowing words from the traditional philosophy), where the most general called top Ontologies.

Among the most difficult problems in knowledge representation are:

Default reasoning and the qualification problemMany the things people know take the form of "working assumptions." For example, if a bird comes up in conversation, people are usually pictures of animals fist size, sings, and flies. None of these things are true about all of the birds. John McCarthy acknowledged this problem in 1969 as the qualification problem: for any commonsense rule that AI researchers care stands, there will often be a large number of exceptions. Almost nothing is true or false only way to abstract logic requires. AI research has explored a number of solutions to this problem. The breadth of commonsense knowledgeThe number of atomic facts that the average person are aware of the astronomical. Research project that attempts to develop a comprehensive knowledge base of commonsense knowledge (eg, Cyc) require large amounts of laborious ontological engineering - they must be built, by hand, a complex concept at a time. The main goal is to have enough computers to understand concepts that can learn by reading from sources such as internet, and therefore can add its own ontology. The subsymbolic form some commonsense knowledgeMuch of what people know that it does not represent "reality" or "statement" that they could actually say out loud. For example, a chess master will avoid a particular chess position because it "feels too exposed" or an art critic can take a look a statue and immediately realize that this is a fake. These intuitions or tendencies represented in the brain non-consciously and sub-symbolically. Knowledge it informs, supports and provides a context for the symbolic, meant more. Number of related problems of sub-symbolic reasoning, it is expected to be situated AI or computational intelligence gives way to represent this kind of knowledge.

Planning

Intelligent agent should be able to set goals and achieve them. they need a way to illustrate the future (they must have a representation of the condition of the world and can make predictions about how their actions will change it) and can make the choice to maximize utility (or "value") of the available options.

Primary problems in planning, agents can assume that it is the only thing moving in the world and this is precisely what the consequences of its actions may be. However, if it is not true, you should check periodically if the world matches its predictions and it must change its plans as it becomes necessary, requiring the agent to reason under uncertainty.

Multi-agent planning uses of cooperation and competition of agents to achieve a given objective. Behavior appears as it is used by of evolutionary algorithms and swarm intelligence.

Study

Machine learning is central to AI research from the beginning. Unsupervised learning is ability to find patterns in a stream of input. Supervised learning with the same classification and as a return. Classification is used to determine what category something belongs to, after seeing a number of examples of things from several categories. Regression takes a set of numbers input / output samples and attempts to discover a continuous function that would generate outputs from inputs. Extra equipment to study the agent is rewarded for good response and punished for bad ones. These will be analyzed in terms of decision theory, using concepts such as utility. The mathematical analysis machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory.

Natural language processing of

Natural language processing gives machines the ability to read and understand the language people speak. Many researchers hope that a strong enough natural language processing system can acquire knowledge on its own, by reading the existing text available on the internet. Some straightforward applications natural language processing include information retrieval (or text mining) and machine translation.

Motion and manipulation

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

The field of robotics is closely related to AI. Intelligence is necessary for robots that can handle tasks such as object manipulation and navigation, with the sub-problem of localization (knowing where you are), mapping (learning what is around you) and actions planning (figuring out how to get there).

Perception

Machine perception is the ability to use input from sensors (such as cameras, microphones, sonar and more exotic) to deduce aspects of the world. Computer vision is the ability to analyze visual input. Some selected subproblems is speech recognition, facial recognition and object recognition.

Social intelligence

Fortunately, a robot with rudimentary social skills

Feeling and social skills to play two roles for an intelligent agent. First, it must be able to predict the actions of others, by understanding their motives and emotional state. (It involves elements of game theory, decision theory, as well as the ability to model human emotions and the perceptual skills to detect emotions.) Also, for good human-computer interaction, an intelligent machine also needs to show emotions. At the very least it must appear respectful and sensitive to the people it interacts with. At best, it must have normal emotions itself.

Creativity

TOPIO, a robot that can play table tennis, developed TOSY.

A sub-field of AI address creativity both theoretically (From a philosophical and psychological perspective) and true (by specific implementation of the system to generate outputs that can be considered creative).

General intelligence

Most researchers hope that their work will eventually be incorporated into a machine with general intelligence (known as strong AI), took all the skills above and beyond ability of people in most or all of them. Some believe that anthropomorphic features like artificial consciousness or an artificial brain will be required for such a project.

Many of the problems above are considered AI-complete: to solve a problem, you should solve all of them. For example, even a honest, specific tasks such as machine translation engine requires to follow the argument the author of (reasons), knew what was being talked about (knowledge) and faithfully copy the author's intention (social intelligence). Machine translation, therefore, believe that AI-complete: it may require strong AI do as well as humans can do it.

Approaches

No established unifying theory or paradigm that guides AI research. Researchers disagree about the issue. Some of the most long-standing questions have remained unanswered are: artificial intelligence should simulate natural intelligence, by learning in psychology or neurology? Or is human biology as those not related to AI research as a bird biology is in aeronautical engineering? Be intelligent behavior can be described using The simple, elegant principles (such as logic or optimization)? Or is it always requires solving a large number of totally unrelated problems? Can Intelligence is reproduced with high levels of symbols, like words and ideas? Or is it requires a "sub-symbolic" processing?

Cybernetics and brain simulation

No consensus on how closely to brain simulation.

In the 1940s and 1950s, a number of researchers explored the connection between of Neurology, information theory and cybernetics. Some of them built machines used electronic networks exhibit rudimentary intelligence, such as W. Grey's Walter Love and the Johns Hopkins Beast. Many of the researchers who gathered for meetings of the teleological Society and Princeton University and the Ratio Club in England. By 1960, this strategy This is largely abandoned, although these elements are revived in the 1980s.

Symbolical

When access to the digital computer was possible in the middle 1950s, AI research began to explore the possibility that human intelligence can fall in symbol manipulation. The research is centered on three institutions: CMU, Stanford and MIT, and each developed his own style of research. John Haugeland named approaches to AI "good old fashioned AI" or "GOFAI.

Cognitive simulationEconomist Herbert Simon and Alan Newell studied human problem solving skills and attempted to formalize them, and their work laid the foundation of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team performed psychological experiments to show the similarities between human problem solving and the program (such as their "General Problem Solver") they are developing. Tradition This, centered at Carnegie Mellon University will lead eventually to the development of architectural soar in the middle 80s. Login basedUnlike Newell and Simon, John McCarthy felt that machines are not needed to simulate human thought, but rather should try to find the essence of abstract reasoning and problem solving, regardless of whether people used the same algorithm. His lab at Stanford (sail) focused on the use of formal logic to solve a wide variety of problems, including the knowledge representation, planning and learning. Logic will also focus work at the University of Edinburgh and elsewhere in Europe which led to the development of programming Prolog language and science of programming logic. "Anti-Logic" or "disorder" Researchers at MIT (such as Marvin Minsky and Seymour Papert) found solving problems with vision and natural language processing requires ad-only solution - they argued that no simple and general principles (like logic) to get all aspects of intelligent behavior. Roger Schank described their "anti-logic" approach as "disorder" (as opposed to "clean" paradigms sa CMU and Stanford). Commonsense knowledge bases (such as Doug Lenat's Cyc) is an example of "disorder" AI, because they should be developed by hand, a complex concept at a time. Knowledge basedWhen computers with large memories became available around 1970, researchers from all three traditions began to build knowledge in AI applications. The "knowledge revolution" led to development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software. Knowledge driven revolution is also realization that large amounts of knowledge is required by many simple AI applications.

Sub-symbolic

During the 1960s, symbolic approaches have achieved great success in simulating high-level thinking in small programs in presentation. Cybernetics or approaches based on neural network was abandoned or pushed in the background. By the 1980s, however, progress in symbolic AI seems to stall and many believe that symbolic systems are never able to duplicate all of the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look at "sub-symbolic" approaches to specific problems AI.

Bottom-up, represent, situated, behavior-based or nouvelle AIResearchers from related fields of robotics, such as Rodney Brooks, AI denied meaningful and focused on basic engineering problems that would allow the robot to move and survive. Their work revived the non-symbolic commentary cybernetics researchers early 50s and reintroduced the use of control theory in AI. These approaches are also conceptually related to symbolize the mind thesis. Computational IntelligenceInterest the neural network and "connectionism" is revived by David Rumelhart and the other in the middle 1980s. These and other sub-symbolic approaches, such as fuzzy systems and evolutionary computation, now collectively studied of the emerging discipline of computational intelligence.

Statistician

In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems. The tool is very scientific, in the sense that their results are both measurable and verifiable, and they are responsible for many of the AI is recent success. The shared mathematical language has also allowed a high degree of cooperation with more established fields (eg mathematics, economics or operations research). Stuart Russell and Peter Norvig describe this movement as less a "revolution" and "the success of neats."

The inclusion of approaches

ParadigmAn intelligent agent intelligent agent is a system that perceives its environment and takes actions which maximizes opportunities of success. Intelligent agent is the simplest programs to solve specific problems. The most complex intelligent agents are rational, thinking humans. The researchers prototype provides a license to study their problems and find solutions that are both verifiable and useful, not responsive to a single strategy. A agent that solves a specific problem may use any method that works - some agents are meaningful and logical, some sub-symbolic neural network and others may use new approaches. The paradigm also gives researchers a common language to communicate with other fields, such as decision theory and economics-that also use the concept of abstract agents. The intelligent agent paradigm became widely accepted during the 1990s. Agent architectures and cognitive systems have architecturesResearchers designed to build intelligent systems of interacting intelligent agents in a multi-agent system. A system with both symbolic and sub-symbolic components A hybrid intelligent system, and study such systems are artificial intelligence systems integration. A hierarchical control system provides a bridge between of sub-symbolic AI to its lowest, level of traditional reactive and symbolic AI at its highest level, where the relaxed time limits and planning permits modeling world. Rodney Brooks subsumption architecture 'is an early proposal for such a hierarchical system.

Fittings

In the course of 50 years of research, AI has developed a large number of tools to solve the most difficult problems in computer science. Some of the most common methods are discussed below.

Search and optimization

Many AI problems can be solved in theory by intelligently searching through many possible solutions, reasoning can be reduced to performing a search. For example, the logical evidence could be viewed as looking for a path that leads from the treatment area, where each step is the application of an inference rule. Planning algorithms search through the tree of goals and subgoals, attempting to find a path to a goal target, a process called means-ends analysis. Robotics algorithm for grasping objects and moving limbs use local search in configuration space. Many learning algorithms use search algorithms based on optimization.

Simple exhaustive search rarely enough for most real world problems: the search space (the number of places to search) grows quickly in astronomical numbers. The result is a search that is too slow or not completed. The solution, for many problems, is the use of "heuristics" or "rules of thumb" to eliminate choice may not lead to the goal (called "pruning the search tree"). Heuristics supply the program with a "best guess "for what the solution path lies.

A very different type of search came to prominence in the 1990s, based on mathematical theory of optimization. For many problems, it is possible to start the search with some form of a guess and then refine the guess incrementally until no further refinements can be made. The algorithm can be visualized as a blind hill climbing, we start the search at a random point landscape, then, by jumps or steps, we keep our guests moving uphill, until we reach the top. Other optimization algorithm simulated annealing, beam search and random optimization.

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

Logic

AI logic is introduced to research By John McCarthy in his 1958 proposal Advice taker. Logic is used for knowledge representation and problem solving, but it could be applied to other problems as well. For example, the satplan algorithm uses logic for planning and inductive logic programming is a method for learning.

Several other forms of logic used in AI research. Propositional or sentential logic is the logic of the statement be true or not. First-order logic also allows the use of quantifiers and predicates, and can express facts about objects, their properties, and their relationship to each other. Fuzzy logic, is a version of first-order logic that allows the truth of a statement represented as a value between 0 and 1, rather than just shooting (1) or False (0). These fuzzy system can be used for uncertain reasoning and is widely used in modern industrial and consumer product control systems. Default logics, non-monotonic logics and methods of logic setting is designed to help default reasoning and qualification problems. Many extensions of logic is designed to handle specific the domain of knowledge, such as: description logics, situation calculus, event calculus and fluent calculus (for representing events and time); causal calculus; belief calculus, and modal logics.

In 1963, J. Alan Robinson discovered a simple, complete and whole algorithmic methods for logical reasoning that can be easily is performed by digital computers. 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 as Horn clauses (statements in the form of rules: "if p then q "), Which reduced the logical reasoning backward chaining or forward chaining. It is much alleviated (but not eliminate) the problem.

Probabilistic methods for uncertain reasoning

Many problems in AI (the reasoning, planning, learning, perception and robotics) require the agent to operate with incomplete or vague information. Starting in the late 80s and early 90s, Judea Pearl and others championed the use of techniques drawn from probability theory and economics to think of a number of powerful tools to solve problems.

Bayesian networks is a very general tool that can used for a large number of problems: reasoning (using the Bayesian inference algorithm), study (with encouraging maximization algorithm), planning (using decision networks) and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used for filtering, prediction, smoothing and searching for explanations for medium data, helping systems sense to study processes that occur over time (eg, hidden Markov models or Kalman filter).

A key concept from the science of economics that "utility": a measure of how important something is an intelligent agent. Specific mathematical tools are developed to analyze how an agent can make choices and plans, using decision theory, decision analysis, information value theory. These tools include models such as Markov decision processes, dynamic decision networks, game theory and mechanism design.

Classifiers and statistical learning techniques

The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do however also classify conditions before inferring action, and therefore classification forms a central component of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to the samples, making them very attractive for use in AI. Examples are known as observations or patterns. Supervised learning, each pattern belongs to a certain predefined class. A class is seen as a decision with to do. All these observations combined with their class label is known as a data set. When a new observation is received, that observation is classified based on previous experience.

A classifier can be trained in different ways, many statistical and machine learning approaches. The most widely used classifiers are neural networks, kernel methods like support vector machines, k-nearest neighbor algorithm, Gaussian mixture model, naive Bayes classifier, and trees decision. The performance of the classifiers were compared over a wide range of activities. Classifier performance greatly depends on the nature of the data classified. No one classifier that works best with all the given problem, it is also referred to as the "no free lunch theorem". Determining a suitable classifier for a given problem is still more an art than science.

Neural networks

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

The study of artificial neural networks began in the decade before the AI research field is established, the work of Walter Pitts and Warren McCullough. Another important early researchers Frank Rosenblatt, who invented the perceptron and Paul Werbos developed the backpropagation algorithm.

The main categories networks are acyclic or feedforward neural networks (where the signal passes only one direction) and recurrent neural networks (which allow feedback). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis network. Among the recurrent network, the most famous is the Hopfield net, form an attractor network, which was first described by John Hopfield in 1982. Neural networks can be applied to problems of intelligent control (for robotics) or learning, using techniques such as Hebbian learning and competitive learning.

Jeff Hawkins argues that research on neural network is stalled because it had failed to model the essential properties of neocortex, and has a suggested model (hierarchical temporal Memory) based on neurological research.

Control Theory

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

Language

AI researchers have developed some special language for AI research, including LISP and Prolog.

Review progress

How can one determine whether an agent is smart? In 1950, Alan Turing proposed a general approach to testing an intelligence agent now known as the Turing test. This method allows almost all The main problem of artificial intelligence to assess. However, this is a difficult challenge and is currently all the agents fail.

Artificial intelligence can also be evaluated on specific problems such as small problems in chemistry, hand-writing recognition and game-play. Such analysis is termed subject matter expert Turing test. Smaller problems to provide better achieve goals and there is an ever-increasing number of positive results.

The broad class of outcomes for a test AI are:

  • Optimal: it is not possible to perform better
  • Strong super-human: performs better than all the staff man
  • Super-human: performs better than most people
  • Sub-human: performs worse than most people

For example, the performance is optimal drafts, chess performance is super-human and super strong approaching human, and performance in many daily activities performed of humans are sub-human.

A totally different way of proposals by machine intelligence tests which are developed from mathematical definition of intelligence. Examples of the types of tests at the beginning of the late nineties devising tests of intelligence with notions from the Kolmogorov complexity and data compression. Similar meaning machine intelligence was put forward by Marcus Hutter in his book Universal Artificial Intelligence (Springer 2005), an idea further developed by Legg and Hutter. Two main advantages of the mathematical definition is their applicability to nonhuman intelligences and their lack of a need for human testers.

Applications

Artificial intelligence has been successfully used in a wide range of fields including medical diagnosis, stock trading, robot control, legislation, scientific discovery, video games, toys, and Web search engines. Often, when a scheme reaches major use, it is not considered artificial intelligence, sometimes described as the AI effect. It may be also included in the artificial lives.

Competitions and prizes

There are a number of competitions and prizes to promote research in artificial intelligence. The main areas above are: general machine intelligence, cause behavior, data-mining, driverless car, robot soccer and games.

Platform

A platform (or "computing platform") is defined by Wikipedia as "some sort of hardware architecture or software framework (including application frameworks), which allows software to run. "As Rodney Brooks pointed out many years ago, it was not only artificial intelligence software AI sets of platform features, but rather the actual platform itself that affects AI results, ie, we will work on real problems in AI platform world rather than in isolation.

A wide variety of platforms will allow various aspects of AI to develop, ranging from expert systems, although PC-based but still a whole system of real-world on various platforms such as robots Roomba widely available with open interfaces.

Philosophy

Artificial intelligence, by claiming to be able to review the ability of the human mind, is both a challenge and an inspiration for philosophy. Is there a limit on how intelligent machines can be? Is there an essential difference between human intelligence and artificial intelligence? Can a machine has a mind and consciousness? Some of the most influential answers to these questions are given below.

Turing's "polite convention" If a machine acts as intelligently as a human, then it is as intelligent as a person. Alan Turing theorized that, eventually, we can only judge the intelligence a machine based on its behavior. This theory forms the basis of Dartmouth Turing proposal test.The "Every aspect of learning or any other features intelligence can be so precisely described by a machine can do to reproduce it. "This statement is printed on the proposal for the Dartmouth Conference of 1956, and represents the position of most working AI researchers.Newell and Simon's physical symbol system hypothesis "A physical symbol system is necessary and adequate means of general intelligent action. "Newell and Simon argue that intelligences are composed of formal operations on symbols. Hubert Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather than conscious symbol manipulation and having a "feel" for the situation symbolic rather than explicit knowledge. (See Dreyfus' critique of AI.) Gödel's Incompleteness Theorem A formal system (such as a computer program) may not prove all true statements. Roger Penrose is among those who claim that Gödel's theorem limits what machines can do. (See the Emperor's New Mind.) Searle's strong AI hypothesis "The appropriately programmed computer with the right inputs and outputs so have a mind in exactly the same sense people have minds. "Searle counters this assertion with his Chinese room argument, asking us to look within the computer and try to find where the "mind" may be.The artificial brain brain argument may be simulated. Hans Moravec, Ray Kurzweil and others have argued that it is technologically feasible to copy the brain directly to the hardware and software, and that such a simulation is essentially similar to the original.

Speculation and fiction

AI is a common theme in both science fiction and projections about the future of technology and society. Having a 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 considers a key issue in ethics of artificial intelligence: if a machine could be created with intelligence, you may it also feel? If it can feel, is it the same rights as a person? The idea also appears in modern science fiction: the film Artificial Intelligence: AI considers a machine in the form of a little boy which is given the ability to feel human emotions, including, tragically, the capacity to suffer. This issue, now known as "robot rights", is under consideration by, for example, California's Institute for the Future, though many critics believe that the discussion is premature.

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

  • As a servant (R2D2 sa Star Wars)
  • As a law enforcer (Kitt "Knight Rider")
  • As a colleague (Lt. Commander Data in Star migrate)
  • As a conqueror / master (The Matrix)
  • As a dictator (in folded Hands)
  • As a murderer (Terminator)
  • As a race sentiant Battlestar Galactica)
  • As an extension to human abilities (Ghost in the Shell)
  • As the savior of humanity (R. Daneel Olivaw the Foundation Series).

Academic sources have considered such consequences as: a decreased demand for human labor, improving the ability of human experience, and a need for redefinition of identity and fundamental human values.

Many futurists argue that artificial intelligence will surpass the limits development and transformation fundamentally human. Ray Kurzweil has used Moore's Law (which describes the relentless exponential improvement in digital technology and strange accuracy) to calculate desktop computers will have the same processing power as human brains by the year 2029 and by artificial 2045 intelligence that will reach a point where it can improve itself at a rate that far exceeds anything conceivable in the past, a scenarios that science fiction writer Vernor Vinge named the "technological singularity". Edward Fredkin argues that "artificial intelligence is the next stage in evolution," an idea first proposed by Samuel Butler's "Darwin among the Machines" (1863), and expanded by George Dyson in his book of the same name in 1998. Many futurists and science fiction writers have predicted that humans and machines will merge in the future cyborgs that are more capable and powerful than either. The idea, called transhumanism, which has roots in Aldous Huxley and Robert Ettinger, is now associated with robot designer Hans Moravec, Kevin Warwick cyberneticist and inventor Ray Kurzweil. Transhumanism is illustrated in fiction as well, for example in the manga Ghost in the Shell and the science fiction series Dune. Pamela McCorduck writes that the events are expressions of ancient human desire, as he calls it, "forgot 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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Learn the basics of drawing Manga and Animated cartoon expressions with the comprehensive "How-To" learning guide included. 46 pieces of fun with 6 different art mediums including color pencils, sketch pencils, pastels, brush markers, markers and a fine tip marker. Set comes in a personalize-able Eco board carrying case....


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