Increasingly, much work in AI -- from machine learning and natural language processing to planning, perception, and robotics -- is based on classical (continuous) optimization. While this foundation has proved to be of considerable practical utility, the changing landscape of the computational landscape, in particular the increasingly networked nature of computation in the 21st century, implies that classical optimization may provide increasingly restrictive, as AI will tackle applications that involve massively large networked environments, where data is stored heterogeneously in the ``cloud", and computation involves balancing multiple competing objectives, including cost, privacy, reliability, and security. The aim of this tutorial is the present a novel foundation for AI, based on a general mathematical framework called equilibration, which enables a unified approach to problems that lie outside the scope of classical optimization. The equilibration framework is based on the mathematical formalisms of variational inequalities (VIs) and projected dynamical systems (PDS). The tutorial will be structured into the following segments:

1. • Introduction and Motivation: 2:00-2:30 p.m.
   

2. • Mathematical background: 2:30-3:30 p.m.
   

3. • Coffee Break: 3:30-4:00 p.m. n
   

4. • Algorithms: 4:00-5:00 p.m.
   

5. • Applications: 5:00-6:00 p.m.