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Understanding intelligence and the brain requires theories at different levels, ranging from the biophysics of single neurons to algorithms, computations, and a theory of learning. In this symposium, we aim to bring together researchers from machine learning, artificial intelligence, neuroscience, and cognitive science to present and discuss state-of-the-art research that is focused on understanding intelligence at these different levels... Representation learning has become an invaluable approach for learning from symbolic data such as text and graphs. However, state-of-the-art embedding methods typically do not account for latent hierarchical structures which are characteristic for many complex symbolic datasets. In this work, we introduce a new approach for learning hierarchical representations of symbolic data by embedding them into hyperbolic space - or more precisely into an n-dimensional Poincaré ball. Due to the underlying hyperbolic geometry, this allows us to learn..