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After four decades of research there still exists a Classification accuracygap of about 20% between our best Unsupervisedly Learned Representationsmethods and the accuracy rates achieved by intelligent animals. It thus maywell be that we are looking in the wrong direction. A possible solution to thispuzzle is presented. We demonstrate that Reinforcement Learning can learnrepresentations which achieve the same accuracy as that of animals. Our mainmodest contribution lies in the observations that: a. when applied to a realworld environment Reinforcement Learning does not require labels, and thus maybe legitimately considered as Unsupervised Learning, and b. in contrast, whenReinforcement Learning is applied in a simulated environment it does inherentlyrequire labels and should thus be generally be considered as SupervisedLearning. The corollary of these observations is that further search forUnsupervised Learning competitive paradigms which may be trained in simulatedenvironments may be futile.

Unsupervisedly Learned Representations: Should the Quest be Over?