  Current artificial learning systems can recognize thousands of visual
categories, or play Go at a champion"s level, but cannot explain infants
learning, in particular the ability to learn complex concepts without guidance,
in a specific order. A notable example is the category of 'containers' and the
notion of containment, one of the earliest spatial relations to be learned,
starting already at 2.5 months, and preceding other common relations (e.g.,
support). Such spontaneous unsupervised learning stands in contrast with
current highly successful computational models, which learn in a supervised
manner, that is, by using large data sets of labeled examples. How can
meaningful concepts be learned without guidance, and what determines the
trajectory of infant learning, making some notions appear consistently earlier
than others?

Neurons and Cognition;Computer Vision and Pattern Recognition;Machine Learning