Nick Feamster (CDAC) – Why (and How) Networks Should Run Themselves

On August 20, 2020, CDAC Faculty Director Nick Feamster addressed the AI + Science seminar series, organized by the AI Joint Task Force formed by members of the University of Chicago, Argonne National Laboratory (ANL), Fermi National Accelerator Laboratory (FNAL), and Toyota Technological Institute at Chicago (TTIC).

More information on the AI + Science program.

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Figures 1a-b show the general strategy: during a training period (fig. 1a), a time-dependent state is imposed on the system. This is pictured by an origami bird whose wings are periodically moved by an external agent. After training (fig. 1b), a retrieval period during which the physical system evolves under the learned dynamics should lead to a dynamic state that matches the training as best as possible. (fig. 1c and 1d) Demonstration of the training and retrieval procedure using a programmable LEGO toy. The angular positions of two motors are imposed by hand during a training phase (fig. 1c), during which couplings between the motors are learned. Dynamics during retrieval, with learned couplings, can produce fixed points as well as dynamic states where the angles constantly change (fig. 1d). See this video for a detailed description.

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Fig 1. Percentage of broadband-serviceable locations (BSLs) with at least one submitted challenge per US state or territory. Nebraska had strong participation in the challenge process, followed by Virginia, New Mexico, and Maine. Most states and territories have low participation rates.

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