Perception and physical reality

Information theory

From Wikipedia, the free encyclopedia

Not to be confused with information technology, information science, or informatics.

Information theory is a branch of applied mathematics and electrical engineering involving the quantification of information. Information theory was developed by Claude E. Shannon to find fundamental limits on signal processing operations such as compressing data and on reliably storing and communicating data. Since its inception it has broadened to find applications in many other areas, including statistical inference, natural language processing, cryptography generally, networks other than communication networks — as in neurobiology,[1] the evolution[2] and function[3] of molecular codes, model selection[4] in ecology, thermal physics,[5] quantum computing, plagiarism detection[6] and other forms of data analysis.[7]

A key measure of information in the theory is known as entropy, which is usually expressed by the average number of bits needed for storage or communication. Intuitively, entropy quantifies the uncertainty involved when encountering a random variable. For example, a fair coin flip (2 equally likely outcomes) will have less entropy than a roll of a die (6 equally likely outcomes).

Applications of fundamental topics of information theory include lossless data compression (e.g. ZIP files), lossy data compression (e.g. MP3s), and channel coding (e.g. for DSL lines). The field is at the intersection of mathematics, statistics, computer science, physics, neurobiology, and electrical engineering. Its impact has been crucial to the success of the Voyager missions to deep space, the invention of the compact disc, the feasibility of mobile phones, the development of the Internet, the study of linguistics and of human perception, the understanding of black holes, and numerous other fields[citation needed]. Important sub-fields of information theory are source coding, channel coding, algorithmic complexity theory, algorithmic information theory, information-theoretic security, and measures of information.

I was laughing at your attempt to ridicule fresco. Reminds me of a child who has just learned 2+2 trying to teach a mathematician how to do his job.

For the rest, I would advise you to take a good look at the assumptions you put at the foundation of your logic.

Computing and philosophy

Recent creative advances and efforts in computing, such as semantic web, ontology engineering, knowledge engineering, and modern artificial intelligence provide philosophy with fertile notions, new and evolving subject matters, methodologies, and models for philosophical inquiry. While computer science brings new opportunities and challenges to traditional philosophical studies, and changes the ways philosophers understand foundational concepts in philosophy, further major progress in computer science would only be feasible when philosophy provides sound foundations for areas such as bioinformatics, software engineering, knowledge engineering, and ontologies.

Classical topics in philosophy, namely, mind, consciousness, experience, reasoning, knowledge, truth, morality and creativity are rapidly becoming common concerns and foci of investigation in computer science, e.g., in areas such as agent computing, software agents, and intelligent mobile agent technologies.

According to L. Floridi "[3] one can think of several ways for applying computational methods towards philosophical matters:

1. Conceptual experiments in silico: As an innovative extension of an ancient tradition of thought experiment, a trend has begun in philosophy to apply computational modeling schemes to questions in logic, epistemology, philosophy of science, philosophy of biology, philosophy of mind, and so on.
2. Pancomputationalism: By this view, computational and informational concepts are considered to be so powerful that given the right Level of abstraction, anything in the world could be modeled and represented as a computational system, and any process could be simulated computationally. Then, however, pancomputationalists have the hard task of providing credible answers to the following two questions:
1. how can one avoid blurring all differences among systems?
2. what would it mean for the system under investigation not to be an informational system (or a computational system, if computation = information processing)?

The self-dual, self-interacting property of informational self-transduction has also been metaphorically described as infocognition and protocomputing.