Monday, 1 August 2011

The Curse of Knowledge

In this week's New Scientist (30 July) Richard Fisher writes:

There are many virtues in being ignorant. We all aspire to have the smarts, but it now seems knowing less can sometimes be an asset. It can make you a better teacher, a more perceptive student and a happier person overall.

The article includes details of a study by Nate Kornell, of Williams College, Williamstown, Massachusetts:

Kornell and Son staged a trivia quiz for mathematicians and historians. The pair asked these academics 90 questions about other experts in their field. Try two of the questions yourself: is Johannes de Groot a famous mathematician? What about Benoit Thoron? Would you answer yes, no, or don't know?

When asked about mathematicians, those in the same field were more likely to give a definite answer, yes or no. Yet while they might know their differential equations inside out, the mathematicians' confidence in naming members of their field was unfounded. They gave more wrong answers to these questions than the historians, who were more willing to confess their ignorance. But the historians weren't humble when it came to their own field - they made the same blunders over the questions about their peers. “They assumed their knowledge was great, but it wasn't in this case” says Kornell. “Experts should say 'don't know' in their own field of expertise more often.”

Are you honest about the limits of your knowledge? How did you answer the mathematician question? De Groot is a Dutch mathematician who died in 1972. Thoron is fictional.

My experience in developing CODIL is very relevant. My first job was as an information scientist working on research and development issues in a large international organisation. I was dealing with scientists and managers (up to board level) with very different backgrounds and specialities. The idea of one information worker having complete knowledge of what the organisation was doing was ridiculous. Everyone in such a large company, including the information workers, is ignorant of much of the detail of what was going on. What the information worker needs is the ability to identify sources of information when required – plus the “teaching skills” of being able to interpreting specialist reports in more widely understandable words.

I then moved to the data processing department of a very large commercial company and took my approach to knowledge and ignorance with me. I was aware that there were specialists in selling aviation fuel to the United States Air Force, in selling central heating oil to domestic houses, in selling tarmac to road builders, etc. I was also aware the the market place was dynamically changing. To me the idea that a group of systems analysts and programmers could produce an exact model which not only accurately reflected today's requirements but also allow flexibility to meet the commercial challenges that might appear tomorrow was something in cloud cuckoo land. Based on my experience of manual information process I took the idea of ignorance in my stride and suggested the answer was not to draw up an exact model of the invoicing process but rather to model the way that sales staff thought about invoicing. The aim was to allow each sales specialist within the company to directly control his area of the market without the need to conform to a globally pre-agreed approach which would always be incomplete and out-of-date.

How this idea later expanded the design of CODIL is describe in detail elsewhere on this blog. However the difficulty I had in getting the idea accepted is directly linked to the concept of ignorance. The computer establishment was, and still is, based on the idea that it is necessary to precisely predefine the application in advance. Ignorance of the application is automatically taken as a sign of incompetence. There is no doubt that much of the opposition to CODIL was that it was taken for granted that it is impossible to program a computer from a position of ignorance of the task – therefore I must have created the computer equivalent of the perpetual motion machine.

This blog is trying to take the CODL ideas further and say that in as far as you can model the human brain you can use the model to handle situations involving ignorance in a way that mimics the way that humans also tackle the problem .
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There is an example where too much knowledge has slowed down the progress in the “Brain Storms” on this blog. Over the last week or two I have been trying to complete the first stage of remapping CODIL onto a neural net. Most of the basic processes move relatively easily but one basic facility – that of asking a question of the knowledge base – would not go in a way that I felt was satisfactory if one wanted to move to parallel processing.. Basically CODIL uses the question as “criteria” (a little bit like a conventional program) and the knowledge base “file” (long term memory) was loaded statement by statement into the “facts” (equivalent to human short term memory). The problem was that in originally designing this part of CODIL I had too much knowledge about how conventional computers work and had incorporated this in the design.

Once I had realised the “block” existed I went right back to first principles. In fact it is obvious that the important thing, in terms of brain activity, is the question – so this should go into the Facts (short term memory). Once there the knowledge base file is asked to say what it contains which is relevant – rather as if it was acting as a kind of program, rather than as data. A new “Brain Storm” should appear in the next few days covering this point.

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