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Artificial and Computational Intelligence

Artificial intelligence is both the intelligence of machines and the branch of computer science which aims to create it. The term artificial intelligence is also used to describe a property of machines or programs: the intelligence that the system demonstrates. AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, operations research, economics, control theory, probability, optimization and logic.

Subjects in computational intelligence as defined by IEEE Computational Intelligence Society mainly include: Neural networks: trainable systems with very strong pattern recognition capabilities. Fuzzy systems: techniques for reasoning under uncertainty, have been widely used in modern industrial and consumer product control systems; capable of working with concepts such as ‘hot’, ‘cold’, ‘warm’ and ‘boiling’. Evolutionary computation: applies biologically inspired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem

AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others. Computational intelligence Computational intelligence involves iterative development or learning (e.g., parameter tuning in connectionist systems).

Major AI textbooks define artificial intelligence as “the study and design of intelligent agents, where an intelligent agent is a system that perceives its environment and takes actions which maximize its chances of success. John McCarthy, who coined the term in 1956, defines it as “the science and engineering of making intelligent machines.

Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or “strong AI”) has not yet been achieved and is a long-term goal of some AI research.

AI research uses tools and insights from many fields, including computer science, psychology, philosophy, neuroscience, cognitive science, linguistics, ontology, operations research, economics, control theory, probability, optimization and logic. AI research also overlaps with tasks such as robotics, control systems, scheduling, data mining, logistics, speech recognition, facial recognition and many others.

“Artificial intelligence is the next stage in evolution,” Edward Fredkin said in the 1980s, expressing an idea first proposed by Samuel Butler’s Darwin Among the Machines (1863), and expanded upon by George Dyson in his book of the same name (1998). Several futurists and science fiction writers have predicted that human beings and machines will merge in the future into cyborgs that are more capable and powerful than either. This idea, called transhumanism, has roots in Aldous Huxley and Robert Ettinger, is now associated with robot designer Hans Moravec, cyberneticist Kevin Warwick and Ray Kurzweil.

 

Retrieved 20th August 2008 at 18:29

*Science daily, http://www.sciencedaily.com/articles/a/artificial_intelligence.htm

*http://en.wikipedia.org/wiki/Artificial_intelligence

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agosto 20, 2008 Posted by | Human Language Technologies | | Deja un comentario

Machine Aided Translation

Machine translation is an autonomous operating system with strategies and approaches that can be classified as follows:

  • the direct strategy
  • the transfer strategy
  • the pivot language strategy

The direct strategy, the first to be used in machine translation systems, involves a minimum of linguistic theory. This approach is based on a predefined source language-target language binomial in which each word of the source language syntagm is directly linked to a corresponding unit in the target language with a unidirectional correlation, for example from English to Spanish but not the other way round. The best-known representative of this approach is the system created by the University of Georgetown, tested for the first time in 1964 on translations from Russian to English. The Georgetown system, like all existing systems, is based on a direct approach with a strong lexical component. The mechanisms for morphological analysis are highly developed and the dictionaries extremely complex, but the processes of syntactical analysis and disambiguation are limited, so that texts need a second stage of translation by human translators.

In practice, computer-assisted translation is a complex process involving specific tools and technology adaptable to the needs of the translator, who is involved in the whole process and not just in the editing stage. The computer becomes a workstation where the translator has access to a variety of texts, tools and programs: for example, monolingual and bilingual dictionaries, parallel texts, translated texts in a variety of source and target languages, and terminology databases. Each translator can create a personal work environment and transform it according to the needs of the specific task. Thus computer-assisted translation gives the translator on-the-spot flexibility and freedom of movement, together with immediate access to an astonishing range of up-to-date information. The result is an enormous saving of time.

There have been basically two overall strategies which researchers have adoptedin the design of MT systems. In the first, the system is designed in all its details specifically for a particular pair of languages, e.g. Russian as the language of the originaltexts (the source language) and English as the language of the translated texts (the target language). Translation is direct from source language (SL) text to target language (TL) text; the vocabulary and syntax of the source language is analysed as little as necessary for acceptable target language output. For example, if a Russian word can be translated in only one way in English it does not matter that the English word may have other meanings or that the Russian might have two or more possible translations in another language. Likewise, if the original Russian word order can be retained in   English and give acceptable translated sentences, there is no need for syntactic analysis. In other words, analysis of the source language is determined strictly by the requirements of the target language. By contrast, in the second strategy, analysis of SL texts is pursued independently of the TL in question. Translation is indirect via some kind of ‘intermediary language’ or via a transfer component operating upon ‘deep syntactic’ or semantic representations of SL texts and producing equivalent representations from which TL texts can be generated. For example, a Russian passive sentence might be analysed as a deep syntactic form which allows for translation in English as either an active or a passive according to circumstances (e.g. the demands of idiomaticity, constraints on English verb forms, etc.) Likewise the various Russian expressions for ‘large’, ‘great’, ‘extreme’, etc. which differ in their distribution according to the nouns and verbs with which they occur, might all be represented as (say) Magn and translated in English by whichever is the most appropriate idiomatic form for the corresponding English noun or verb.

It has long been a subject of discussion whether machine translation and computer-assisted translation could convert translators into mere editors, making them less important than the computer programs. The fear of this happening has led to a certain rejection of the new technologies on the part of translators, not only because of a possible loss of work and professional prestige, but also because of concern about a decline in the quality of production. Some translators totally reject machine translation because they associate it with the point of view that translation is merely one more marketable product based on a calculation of investment versus profits. They define translation as an art that possesses its own aesthetic criteria that have nothing to do with profit and loss, but are rather related to creativity and the power of the imagination.

 

Retrieved: 17-07-2008, 18:28

http://www.hutchinsweb.me.uk/JDoc-1978.pdf

http://accurapid.com/journal/29computers.htm

http://en.wikipedia.org/wiki/Computer-assisted_translation

 

 

julio 17, 2008 Posted by | Human Language Technologies | | Deja un comentario