Curriculum for an AI and knowledge organization

It is important in what order you teach things to an intelligence.  The "ideal" curriculum may be different for AIs versus that for humans.  In the case of humans (and a few AI systems) some results appear in: In Order to Learn, F. E. Ritter, et al, editors, Oxford Univ. Press, 2007. In the case of an AI  I have described some of what I've taught Asa H in my various publications and in this blog.

 As an example, with both AIs and humans one should teach letters first, then words, then phrases, then composition.  In general, start with small items to learn.  Progress toward larger items.  If the elements of the topic being taught are interrelated teach the individual elements first, then teach the associations between the elements.

Constrain early learning more.  Relax the constraints as learning proceeds.

On the other hand, with a multiagent AI we might sometimes wish to train different agents on the same patterns but presented in a different order. This can force the different agents to form different mental models/categories and enhance mental diversity.

Humans have an issue with the splitting of attention but AIs will typically have more STM than a human does and so this is less of a problem.  AIs can potentially do more in parallel.

Some knowledge organization/partitioning/clustering/sorting can be built directly into an AI's memory and can follow standard library techniques and practice.(for example see Dewey decimal classification and relative index, Forest Press, 1971 and Theory of classification, K. Kumar, Vikas pub., 2004) The knowledge stored in one given hard drive might be what would have otherwise been found on a given stack in a library of print books. In Asa H, for instance, sufficiently similar case vectors are clustered into a given casebase (one of many casebases which Asa then uses).

Advanced training for a society of  AIs might possibly resemble, be organized according to, and be modeled after the  training of humans in their various common career tracks.

Issues such as these become important as the size of the knowledgebase grows and the knowledge becomes more diverse.  It must also be possible to change the knowledge and its organization for reasons like those described by Arbesman in: The Half-life of Facts: Why everything we know has an expiration date, Penguin, 2012.