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Information as a concept has many meanings, from everyday usage to technical settings. The concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation. In its most restricted technical meaning, information is an ordered sequence of symbols.
The English word was apparently derived from the Latin accusative form (informationem) of the nominative (informatio): this noun is in its turn derived from the verb "informare" (to inform) in the sense of "to give form to the mind", "to discipline", "instruct", "teach": "Men so wise should go and inform their kings." (1330) Inform itself comes (via French) from the Latin verb informare, to give form to, to form an idea of. Furthermore, Latin itself already contained the word informatio meaning concept or idea, but the extent to which this may have influenced the development of the word information in English is unclear.
As a final note, the ancient Greek word for form was "μορφή" (morf -> morphe, Morph) and also είδος eidos (kind, idea, shape, set), the latter word was famously used in a technical philosophical sense by Plato (and later Aristotle) to denote the ideal identity or essence of something (see Theory of forms). "Eidos" can also be associated with thought, proposition or even concept.
Contents[hide]
1 As a message
1.1 Measuring information entropy
2 As sensory input
3 As an influence which leads to a transformation
4 As a property in physics
5 As records
6 Information and semiotics
7 See also
8 Notes
9 Further reading
10 External links
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[edit] As a message
Information is a term with many meanings depending on context, but is as a rule closely related to such concepts as meaning, knowledge, instruction, communication, representation, and mental stimulus. Simply stated, information is a message received and understood. In terms of data, it can be defined as a collection of facts from which conclusions may be drawn. There are many other aspects of information since it is the knowledge acquired through study or experience or instruction. But overall, information is the result of processing, manipulating and organizing data in a way that adds to the knowledge of the person receiving it.
/*Information means knowledge of an event in a space-time medium.*/
Information is the state of a system of interest. Message is the information materialized.
Information is a quality of a message from a sender to one or more receivers. Information is always about something (size of a parameter, occurrence of an event, value, ethics, etc). Viewed in this manner, information does not have to be accurate; it may be a truth or a lie, or just the sound of a falling tree. Even a disruptive noise used to inhibit the flow of communication and create misunderstanding would in this view be a form of information. However, generally speaking, if the amount of information in the received message increases, the message is more accurate.
This model assumes there is a definite sender and at least one receiver. Many refinements of the model assume the existence of a common language understood by the sender and at least one of the receivers. An important variation identifies information as that which would be communicated by a message if it were sent from a sender to a receiver capable of understanding the message. In another variation, it is not required that the sender be capable of understanding the message, or even cognizant that there is a message, making information something that can be extracted from an environment, e.g., through observation, reading or measurement.
Communication theory provides a numerical measure of the uncertainty of an outcome. For example, we can say that "the signal contained thousands of bits of information". Communication theory tends to use the concept of information entropy, generally attributed to Claude Shannon, see below.
Another form of information is Fisher information, a concept of R.A. Fisher. This is used in application of statistics to estimation theory and to science in general. Fisher information is thought of as the amount of information that a message carries about an unobservable parameter. It can be computed from knowledge of the likelihood function defining the system. For example, with a normal likelihood function, the Fisher information is the reciprocal of the variance of the law. In the absence of knowledge of the likelihood law, the Fisher information may be computed from normally distributed score data as the reciprocal of their second moment.
Even though information and data are often used interchangeably, they are actually very different. Data are sets of unrelated information, and as such are of no use until they are properly evaluated. Upon evaluation, once there is some significant relation between data, and they show some relevance, then they are converted into information. Now this same data can be used for different purposes. Thus, till the data convey some information, they are not useful and therefore not information.
[edit] Measuring information entropy
The view of information as a message came into prominence with the publication in 1948 of an influential paper by Claude Shannon, "A Mathematical Theory of Communication." This thesis provides the foundations of information theory and endows the word information not only with a technical meaning but also a measure. If the sending device is equally likely to send any one of a set of N messages, then the preferred measure of "the information produced when one message is chosen from the set" is the base two logarithm of N (This measure is called self-information). In this paper, Shannon continues:
The choice of a logarithmic base corresponds to the choice of a unit for measuring information. If the base 2 is used the resulting units may be called binary digits, or more briefly bits, a word suggested by J. W. Tukey. A device with two stable positions, such as a relay or a flip-flop circuit, can store one bit of information. N such devices can store N bits…[1]
A complementary way of measuring information is provided by algorithmic information theory. In brief, this measures the information content of a list of symbols based on how predictable they are, or more specifically how easy it is to compute the list through a program: the information content of a sequence is the number of bits of the shortest program that computes it. The sequence below would have a very low algorithmic information measurement since it is a very predictable pattern, and as the pattern continues the measurement would not change. Shannon information would give the same information measurement for each symbol, since they are statistically random, and each new symbol would increase the measurement.
123456789101112131415161718192021
It is important to recognize the limitations of traditional information theory and algorithmic information theory from the perspective of human meaning. For example, when referring to the meaning content of a message Shannon noted “Frequently the messages have meaning… these semantic aspects of communication are irrelevant to the engineering problem. The significant aspect is that the actual message is one selected from a set of possible messages” (emphasis in original).
In information theory signals are part of a process, not a substance; they do something, they do not contain any specific meaning. Combining algorithmic information theory and information theory we can conclude that the most random signal contains the most information as it can be interpreted in any way and cannot be compressed.[citation needed]
Michael Reddy noted that "'signals' of the mathematical theory are 'patterns that can be exchanged'. There is no message contained in the signal, the signals convey the ability to select from a set of possible messages." In information theory "the system must be designed