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n the present white paper we discuss the current state of Artificial Intelligence (AI) research and its future opportunities. We argue that solving the problem of invariant representations is the key to overcoming the limitations inherent in today's neural networks and to making progress towards Strong AI. Based on this premise, we describe a research strategy towards the next generation of machine learning algorithms beyond the currently dominant deep learning paradigm. Following the example of biological brains, we propose an unsupervised learning approach to solve the problem of invariant representations. A focused interdisciplinary research effort is required to establish an abstract mathematical theory of invariant representations and to apply it in the development of functional software algorithms, while both applying and enhancing our conceptual understanding of the (human) brain.
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