Deep neural networks (DNNs) are capable of learning to identify shapes, so “we’re on the right track in developing machines with a visual system and vocabulary as flexible and versatile as ours,” say KU Leuven researchers.
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A visualization of the model taken at one time point while running. Each sphere represents a computational unit, with lines representing the connection between units. The colors represent the output of each unit. The left and right of the image are the inputs to the model and the center is the output, which is used to guide the virtual bee down a simulated corridor. (credit: The University of Sheffield)