Editing Boltzmann machine (section)

==Problems==
Theoretically the Boltzmann machine is a rather general computational medium. For instance, if trained on photographs, the machine would theoretically model the distribution of photographs, and could use that model to, for example, [[Inpainting|complete]] a partial photograph.

Unfortunately, Boltzmann machines experience a serious practical problem, namely that it seems to stop learning correctly when the machine is scaled up to anything larger than a trivial size.{{Citation needed|date=January 2013}} This is due to important effects, specifically:

* the required time order to collect equilibrium statistics grows exponentially with the machine's size, and with the magnitude of the connection strengths{{Citation needed|date=August 2015}}
* connection strengths are more plastic when the connected units have activation probabilities intermediate between zero and one, leading to a so-called variance trap. The net effect is that noise causes the connection strengths to follow a [[random walk]] until the activities saturate.