Information, complexity and computer science

An interesting article appeared on NY Times today about information and computer science. The article questions whether or not computer science has become the metaphor for grasping and comprehending knowledge. A bunch of CS folks got together to discuss whether this is true or not. The article corectly concludes, that indeed, computer science today cannot successfully interpret the complexity of our world's knowledge. In some fields of knowledge (especially physical science) it may be possible to extract some meaning by reducing data to consistent patterns (such as is being attempted with with the human genome). But a problem lies in the fundamental concept that all messages are processable and can be understood universally by a computer. The computer scientists participating in the discussion mentioned in the article hoped to arrive at a "'unified language' in which to talk about physics, biology, neuroscience and other realms of thought." What they found instead was that it is difficult to even define the terms that describe that with which they work. The problem of representing knowledge is the crux of knowledge representation work (indexing, ontological creation). The world's messages do not consistently mean the same thing to all people at different times. In fact, the same message might mean something different to the same person at different times. The fundamental problem with the computer science approach to information is that they view the raw data as the message. The term information comes from the latin "in forma", meaning the shape within. Information implies meaning -- knowledge that is the result of some study or analysis. Extracting the meaning of messages does not produce foolproof and universal results with today's computer science applications. At least not yet. As an example, could a computer science algorithm consistently and universally produce the same results that a human does when looking at a Rorschach inblot test and describe some message? To think that one can create an algorithm that can is the height of hubris. Maybe some day the results of human and computer message extraction will get closer to each other, but today, the knowledge representation and comprehension is very slippery territory, which is why many IAs recommend, when organizing and representing information on large Web projects, a combination of automated and human methods. From the article, "Time of Growing Pains for Information Age": This is the information age, in which, we are told, biology is defined by a three-billion- letter instruction manual called the genome and human thoughts are analogous to digital bits flowing through a computer. Jaron Lanier is the lead scientist of the National Tele-Immersion Initiative. He and six other scientists were sitting under a maple tree one recent afternoon worrying whether this headiness was justified. They found instead that they could not even agree on useful definitions of their field's most common terms, like "information" and "complexity," let alone the meaning and future of this revolution. ... Scientifically, the information age can be said to have begun in 1948 when Dr. Claude E. Shannon, a researcher at Bell Laboratories, proposed that information could be defined as the number of ones and zeros bits that it took to encode a message in binary language. ... The assembled scientists, however, argued that Dr. Shannon's definition of information, based on counting bits, did not give a meaningful result in every situation. For example, if you have two copies of a book, you have twice as many bits and thus twice as much information, but you are not necessarily better informed.