Learning Algorithms for Deep Architecture Systems (Algorithmes d'Apprentissage pour Systèmes à Architecture Profonde)

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More than 50 years after the founding of the field of artificial intelligence, we are still baffled by the capacity of humans for abstract thought. When faced with the daunting task of implementing such complex thinking in a machine, one notices the richness and variety of concepts the human mind can manipulate. Humans can look at the 3D array of pixels encoding the video of a scene, and produce from it several different high-level (abstract) representations with varying structures: the set of objects it contains, the network of relationships between these objects, a textual description of the semantics of the scene, etc. How could we recreate, in a computer, this ability that humans have to smoothly transition between these rich structures and preserve the core semantics of the real world they represent? This research program will attempt to answer this question, by taking inspiration from the deeply layered organization of computations in the human brain. It proposes to investigate algorithms that can adapt the behavior of an artificial neural network, organized in a deep architecture, so that it can take as input an observation with some arbitrary structure and transform it so as to output a desired target with a possibly different structure. Deep learning systems have been developed for problems with data of limited structural complexity and have thus improved the state-of-the-art on such problems. This research program will aim to bring the same improvements to problems with structured data that do not have deep learning solutions at present. Such adaptive, predictive systems will find application in a number of fields, such as computer vision and natural language processing. More fundamentally, the results of this research will shed light on principles for building, from simulated experience, autonomous intelligent systems capable of manipulating high-level abstract concepts, and shrink the gap between the state-of-the-art in artificial intelligence and human intelligence.