Abstract: Model predictive controllers use dynamics models to solve constrained optimal control problems. However, computational requirements for real-time control have limited their use to systems ...

Opinion: Accenture’s outlook is only one marker. While markets may have overreacted, it points to a turbulent AI-driven phase ...

Despite all the new platforms, algorithms and trends, the way people make decisions hasn’t changed nearly as much.

Executives approved a bold AI roadmap. Cloud spending climbed 40, 50, even 70 percent. And yet the AI workloads that made ...

Abstract: Many practical applications of optimal control are subject to real-time computational constraints. When applying model predictive control (MPC) in these settings, respecting timing ...

Department of Biomedical Engineering and William G. Lowrie Department of Chemical and Biomolecular Engineering, The Ohio State University, Columbus, Ohio 43210, United States Article Views are the ...

Across business and politics, leaders are being held accountable for outcomes that depend on conditions far beyond the ...

Building a model is rarely a clean, linear process. Most of the work happens through testing, adjustment, and failure. Each ...

Our mandate then was to gain independence. But in today’s hyperconnected world, it’s all about standing together.

AI-Enhanced Problem-Based Learning in Pathology Technology: An OBE-Driven Triadic Model of Clinical Problem, AI Validation, ...

While there have been many sober warnings about AI and recursive self-improvement, Arianna Huffington argues that it is a ...

Leaders from Sinclair, Hearst Television, Graham Media Group and more unpacked advertising’s biggest pain points — and the ...