Information vs Meaning: A False Dichotomy?
In a few previous posts I’ve stressed the difference between information and meaning (which I picked up from Claude Shannon, the father of information theory) and some of its implications. For example, in this post I pointed out that Shannon’s separation of meaning and information is compatible with structuralist and post-structuralist theories which maintain that there is no inherent meaning in the text. (I’ve also had to deal with it in the course of digitizing a book – see here). Work on Artificial Intelligence has tended to reinforce this distinction: computers are very good at processing information but not very good at understanding meaning.
But last week Bill Turkel wrote a post which turned my understanding of the meaning/information dichotomy on its head. This isn’t such a new development as it’s following on from a post he wrote in March 2006, and that was inspired by an article by Rudi Cilibrasi and Paul Vitányi published in 2005. There’s a lot of mathematical stuff about compression algorithms which I can’t claim to understand, but the schwerpunkt is that without understanding anything about meaning, computers can compare similarities in the information content of texts and cluster them accordingly. The result is patterns that make sense to humans who can understand the meaning of the text. Bill’s example used entries from the Canadian Dictionary of National Biography, finding geographical and chronological clusters of entries.
Despite the attention grabbing title of my post, the distinction between information and meaning isn’t a false one. However, these experiments show that in practice the relationship between information and meaning within the context of a particular linguistic/cultural system is not as arbitrary and unpredictable as theorizing might suggest. Does this mean that structuralism could make a comeback against post-structuralism? Or do we need to move beyond both of those things and find a new way to think about text? Whatever the implications for theory, this is an exciting development which promises to be very useful in practice.
