MIT Technology Review February 19, 2018 Some critical machine-learning mechanisms have no analogue in the natural world, where learning seems to occur in a different way. Researchers at the University of Washington have created an artificial neural network that mimics the structure and behavior of the olfactory learning system in Manduca sexta moths. Their model can robustly learn new odors, and their simulations of integrate-and-fire neurons match the statistical features of in vivo firing rate data. This work that could have significant implications for the design of synthetic neural networks that need to learn quickly…read more. Open Access TECHNICAL ARTICLE
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Modeling Uncertainty Helps MIT’s Drone Zip Around Obstacles
IEEE Spectrum February 12, 2018 Researchers at MIT have developed a new motion planning framework called NanoMap, which uses a sequence of 3D snapshots to allow fast-moving (10 m/s) drones to safely navigate around obstacles even if they’re not entirely sure where they are. The key idea of NanoMap is to store a history of noisy relative pose transforms and search over a corresponding set of depth sensor measurements for the minimum-uncertainty view of a queried point in space… read more. Video.