Learning higher level representations and invariant transformations

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Can we design an artificial intelligence system that, once plunged in an unknown world, like a newborn baby opening his eyes for the first time, would be able to form high level concepts exclusively from the enormous quantity of raw sensory information that it receives? Such a system should be able to discover deep regularities and stable structures in this ever changing information stream, so that it may eventually form its own subjective notions of space, time, of things (more or less stable entities) moving around. This research program aims at exploring new fundamental principles to guide the building of next generation autonomous learning systems with this kind of abilities. Our approach will be based on computer implementations capable of growing "layers of understanding", stacked one upon the other. Each such layer shall be building a slightly higher level "understanding" of the representation extracted by the previous one, by exploiting regularities that the previous layer failed to capture. We intend to focus on a particular class of regularities called "invariant transformations", that we want to automatically discover and learn. The layer by layer extraction of ever higher level, ever more meaning carrying representations, is expected to be a key element for opening the door to near human-level performance in artificial learning systems (thus advancing the state-of-the-art in the fields of machine learning, pattern recognition and data mining). It is also expected to produce computerized systems capable of far more accurate predictions from past observed data. This would have direct technological applications in a wide range of fields where superior prediction systems are immensely useful: from engineering to finance, from business decision making to medicine.