The definitions above, separated by ten years, represent two very different conceptions of learning. For Simon learning depends on an internal change in representation, and for Kaelbling it is instead measured in terms of an external change in behaviour. Furthermore, Kaelbling's focus on the situatedness of the learning system being embedded in its environment reflects the recent experience gained by much direct experimentation with physical robots.

Very little in computing these days is promoted with as much vigour as multimedia. Multimedia, we are told, will increase the usability and productivity of systems; it will improve the ability of people to learn from educational applications; it will combine naturally with other interface technologies to create a more natural interaction than ever before.

It is well known that a program—to accomplish any task—is welcomed by new users as long as it is easy to use, to understand, to learn. In the recent past, programs were written first and a nice adhoc interface was added later. The look and feel of this interface gradually became the convincing part of the whole system (program plus interface): in fact the way in which the screen presented the available commands, the possibility of on-line help, of undo operations, etc. were firstly greeted and then requested by the new users of complex programs.

Two phenomena have probably affected modern data analysts' lives more than anything else. First, the size of real-world data sets is getting increasingly large, especially during the last decade or so. Second, modern computational methods and tools are being developed which add further capability to traditional statistical analysis tools. These two developments have created a new range of problems and challenges for analysts, as well as new opportunities for intelligent systems in data analysis.