Manifold learning techniques such as SDE have the ability to extract data from a high dimensional space and describe it in terms of its degrees of freedom. Thus low level concepts such as “collection of pixels” become integrated into higher-level concepts such as “teapot rotated at this angle”.

In other words, this is how you teach a system abstraction. Thus, use of something of this nature may be a necessary component of an artificially intelligent system. The only problem is that current methods may be computationally infeasible for this use. Of course, approximation would be a good idea here.