  We present a system for bottom-up cumulative learning of myriad concepts
corresponding to meaningful character strings, and their part-related and
prediction edges. The learning is self-supervised in that the concepts
discovered are used as predictors as well as targets of prediction. We devise
an objective for segmenting with the learned concepts, derived from comparing
to a baseline prediction system, that promotes making and using larger
concepts, which in turn allows for predicting larger spans of text, and we
describe a simple technique to promote exploration, i.e. trying out newly
generated concepts in the segmentation process. We motivate and explain a
layering of the concepts, to help separate the (conditional) distributions
learnt among concepts. The layering of the concepts roughly corresponds to a
part-whole concept hierarchy. With rudimentary segmentation and learning
algorithms, the system is promising in that it acquires many concepts (tens of
thousands in our small-scale experiments), and it learns to segment text well:
when fed with English text with spaces removed, starting at the character
level, much of what is learned respects word or phrase boundaries, and over
time the average number of "bad" splits within segmentations, i.e. splits
inside words, decreases as larger concepts are discovered and the system learns
when to use them during segmentation. We report on promising experiments when
the input text is converted to binary and the system begins with only two
concepts, "0" and "1". The system is transparent, in the sense that it is easy
to tell what the concepts learned correspond to, and which ones are active in a
segmentation, or how the system "sees" its input. We expect this framework to
be extensible and we discuss the current limitations and a number of directions
for enhancing the learning and inference capabilities.
