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Title: The TILOS AI Institute: Integrating optimization and AI for chip design, networks, and robotics
Abstract

Optimization is a universal quest, reflecting the basic human need todo better. Improved optimizations of energy‐efficiency, safety, robustness, and other criteria in engineered systems would bring incalculable societal benefits. But, fundamental challenges of scale and complexity keep many such real‐world optimization needs beyond reach. This article describes The Institute for Learning‐enabled Optimization at Scale (TILOS), an NSF AI Research Institute for Advances in Optimization that aims to overcome these challenges in three high‐stakes use domains: chip design, communication networks, and contextual robotics. TILOS integrates foundational research, translation, education, and broader impacts toward a new nexus of optimization, AI, and data‐driven learning. We summarize central challenges, early progress, and futures for the institute.

 
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NSF-PAR ID:
10491630
Author(s) / Creator(s):
 ;  ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
AI Magazine
ISSN:
0738-4602
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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