Information, Knowledge, Text

Text, writing, words, and human communication in general have been studied for many years. In this book there are references to Aristotle (323BC), Plutarch (100BC) and to newcomers like Rousseau (1755). Philosophical writing about these subjects does not get out of date like IT or library science. Older books about the subjects are still very useful. Some years ago I became interested in it and accumulated several which have not dated much.

These topics seem to be referred to as communication science rather than information science – one of them (Burger and Chaffee, 1987) has over 300 references, of which only a handful are common to both of these subject areas. A shortened version of the definition of communication science, as given in Burger and Chaffee, is “Communication Science seeks to understand the production, processing, and effects of symbol and signal systems”.

Communication science is not usually taught as part of IT – perhaps it should be. Cherry (1966) considers that: “at the time of writing, the various aspects of communication, as they are studied under the different disciplines, by no means form a unified study; there is a certain common ground which shows promise of fertility, nothing more”. This classic book about human communications (first edition 1957, second edition 1966) has been cited nearly 500 times (Web of Science). Bar‐Hillel (1964) contains some advanced mathematics, while MacKay (1969) brings a different viewpoint to some of the ground covered by Cherry.

Warner describes the objectives of his scholarly book in the preface. “Computing and other modern information technologies had been partly further assimilated to historical and still continuing technological forms … and at the theoretical and epistemological level, understanding still needs deliberately to be developed. The focus must be on social, not individual, and epistemology, on collective, not personal, ways of knowing … considering their broader intellectual context”. The author pursues these ideas in a number of interesting chapters.

In chapter 1 he reviews studies of writing, while chapter 2 covers redundancy and Shannon’s communication concepts. Bacon’s well known aphorism “Reading maketh a full man; conference a ready man and and writing an exact man”, quoted in chapter 3, aptly summarises the theme of the chapter. In chapter 4 the book continues with a discussion covering logic, automata theory, notational models and Turing machines. Chapter 5 deals with copyright from UK and US viewpoints, and chapter 6 is about graphic communication. The book concludes with several short reviews of other books about some of the subjects covered in this book, followed by a good bibliography

Both Turing and Shannon – men of Nobel prize stature – receive considerable attention in Warner’s book, but a little more information about them is in order. Turing was awarded the OBE in 1946 and was appointed a Fellow of the Royal Society in 1951. An original thinker, he worked at Bletchley Park on the Bombe and the Colossus computer for deciphering data from the German Enigma and other machines. Later he moved to the National Physical Laboratory to work on the Ace machine. In Turing’s (1936) paper “On computable numbers … ” written while he was a graduate student at Princeton University, he proposed a “Universal machine”. It would solve any problem once it had been loaded with an appropriate program. This seminal paper has since been cited hundreds of time.

A later paper in Mind (Turning, 1950) – a quarterly review of psychology and philosophy – is equally enduring. It contains Turin’s paper “Computing machinery and intelligence” – an article which is regularly cited today. In it, a game between a recipient – really a computer program – and a human interrogator is proposed. The interrogator, in a separate room but connected to the recipient via a teleprinter, is required to decide whether the replies come from a human or from the computer. The interrogator poses questions designed to find out if the responses require “thought”.

In 1991 the first annual Loebner contest was held in order to find out whether a computer program could be developed to survive the Turing test. A number of computer “subjects” were assessed in a specially devised test. Judges were asked to rank “subjects” from most human‐like to least human‐like. But if any computer was adjudged to be indistinguishable from a human, the computer program writer would receive a grand prize of $100,000 and a gold medal. A prize of $2,000 and a bronze medal would be awarded annually to the most human‐like program. So far a winner has not emerged.

Claude Shannon, who died in 2001, was a scientist, musician, juggler and expert chess player (Cawkell, 2001). Shannon had a puckish sense of humor. While making a speech at a Brighton conference he observed that the audience was not paying attention. He took some balls out of his pocket and started juggling. The audience woke up and loudly applauded.

Vannevar Bush, dean of engineering at MIT, had built a huge mechanical computer using rods, gears and axles called The Differential Analyser. Shannon assisted Bush in setting up the machine. Experience with it prompted his master’s thesis about using relay and switching circuits for performing complex equations. Later he joined the Bell Telephone Laboratories and presented what is arguably the most important paper ever conceived in the field of information transmission – about the information capacity of a noisy channel (Shannon, 1948).

It is unfortunate that the word “information”, as I and many others have used it in this context, has crept in, because the word is virtually synonymous in the minds of most people with “meaning”. Shannon is usually credited with the founding of “information theory”. In chapter 2 of this book by Warner, the author attempts to deal with certain difficulties, not entirely successfully, which have risen since the phrase was first used. As Cherry says about information theory (page 51) “…it is a pity that the mathematical concepts … have been called ‘information’ at all”. The subject is about “the statistical rarity or ‘surprise value’ of a source of message‐signs”. Many of the discussions about information theory are based on Shannon’s paper describing work for which it is not applicable.

Fairthorne (1968), another remarkable man, for whom I sadly wrote an obituary (Cawkell, 2000), said: “how easily we can slip from the narrow concept of ‘information’ based on the observed statistical habit of symbols, to the far wider concepts of ordinary discourse which involve also the content of symbols and the reactions of their users”.

This is partly because sections of Shannon’s paper – sections in which symbol probabilities are discussed – are applicable in information science. Later, Shannon (1951) published another paper. This piece was about estimating the entropy (the aforementioned “statistical rarity”) and redundancy of the English language. It includes a curve plotted on logarithmic axes showing word frequency against rank for English words. The idea was to test people’s knowledge of the statistics of language. The curve, which is almost a straight line, shows high frequency words like “the” at one end of it and words like “quality” at the other. The predictability of English was tested in a guessing game. Having been shown the first part of a line of text, subjects are invited to guess the remainder letter by letter, and if wrong have to try again. The number of guesses per letter are recorded. This paper is well worth reading as it shows that Shannon’s work verged on applications in which collections of symbols do have meanings.