Parallelism & Intelligence

About Intelligence

Until now, intelligence is defined vaguely as the things humans still can and computers not...

Intelligent Information System
examples, applications

To start my research I define following properties of information systems:
- Expressiveness (EXPR). An information system contains knowledge/information. Expressiveness is a measure for what kind of information can be put in the system. Especially the power of defining and applying general reasoning rules will be necessary for the parallelisator.
- Recognition (RECOG) of characteristics of the problem, like in pattern recognition. This relates to similarity detection (explained later).
- Understanding: for some requirements the information system should understand what is meant. However, this concept is not fully understood and still vague. A start of giving the concept a concrete form are the properies similarity detection (see recognition) and the notion of levels in a program (see later).
- Reasoning: one thing humans do is reasoning. We need to understand this phenomenon in order to solve some kind of problems.
- Minimal Information Principle: hoe uitleggen? Uitwerken?

Description <> Implementation
important difference!
necessary for: programming, optimisation (same result, faster implementation), learning