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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

Federal budget cuts threaten to decimate America’s AI superiority—and other countries are watching

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.

BlogSep 03, 2024

Developing a smart material that can learn from example

A video frame example from Cityscape dataset where different colors denote different labels in pixel level. For example, red means person and light blue means sky.

BlogJun 06, 2024

Behind Automatic Video Semantic Segmentation

Microbial organisms drive the carbon cycle. Image adapted from Wu et al. 2024 https://doi.org/10.1016/j.scitotenv.2023.168627

BlogMay 30, 2024

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Federal budget cuts threaten to decimate America’s AI superiority—and other countries are watching

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Michael J. Franklin

James Phillips

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

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