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VideoJan 29, 2021

Data & Technology Outlook: Future of AI and Data-Driven Business Applications

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Michael J. Franklin

Morton D. Hull Distinguished Service Professor; Senior Advisor to the Provost for Computing and Data Science; Faculty Co-Director, Data Science Institute

James Phillips

President, Business Applications Group at Microsoft
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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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