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Most artificial neural networks used for object detection and recognition aretrained in a fully supervised setup. This is not only very resource consumingas it requires large data sets of labeled examples but also very different fromhow humans learn. We introduce a setup in which an artificial agent firstlearns in a simulated world through self-supervised exploration. Followingthis, the representations learned through interaction with the world can beused to associate semantic concepts such as different types of doors. To dothis, we use a method we call fast concept mapping which uses correlated firingpatterns of neurons to define and detect semantic concepts. This associationworks instantaneous with very few labeled examples, similar to what we observein humans in a phenomenon called fast mapping. Strikingly, this method alreadyidentifies objects with as little as one labeled example which highlights thequality of the encoding learned self-supervised through embodiment usingcuriosity-driven exploration. It therefor presents a feasible strategy forlearning concepts without much supervision and shows that through pureinteraction with the world meaningful representations of an environment can belearned.

Fast Concept Mapping: The Emergence of Human Abilities in Artificial Neural Networks when Learning Embodied and Self-Supervised