skip to main content

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

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.

more » « less
Author(s) / Creator(s):
 ;  ;  ;  
Publisher / Repository:
Wiley Blackwell (John Wiley & Sons)
Date Published:
Journal Name:
AI Magazine
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract

    This article is a short introduction toAI4OPT, the NSF AI Institute for Advances in Optimization.AI4OPTfuses AI and optimization, inspired by societal challenges in supply chains, energy systems, chip design and manufacturing, and sustainable food systems. By combining machine learning and mathematical optimization,AI4OPTstrives to develop AI‐assisted optimization systems that bring orders of magnitude improvements in efficiency, perform accurate uncertainty quantification, and address challenges in resiliency and sustainability.AI4OPTalso applies its “teaching the teachers” philosophy to provide longitudinal educational pathways in AI for engineering.

    more » « less
  2. Abstract

    This paper highlights the overall endeavors of the NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI‐EDGE) to create a research, education, knowledge transfer, and workforce development environment for developing technological leadership in next‐generation edge networks (6G and beyond) and artificial intelligence (AI). The research objectives of AI‐EDGE are twofold: “AI for Networks” and “Networks for AI.” The former develops new foundational AI techniques to revolutionize technologies for next‐generation edge networks, while the latter develops advanced networking techniques to enhance distributed and interconnected AI capabilities at edge devices. These research investigations are conducted across eight symbiotic thrust areas that work together to address the main challenges towards those goals. Such a synergistic approach ensures a virtuous research cycle so that advances in one area will accelerate advances in the other, thereby paving the way for a new generation of networks that are not only intelligent but also efficient, secure, self‐healing, and capable of solving large‐scale distributed AI challenges. This paper also outlines the institute's endeavors in education and workforce development, as well as broadening participation and enforcing collaboration.

    more » « less
  3. Abstract

    The Institute for Student‐AI Teaming (iSAT) addresses the foundational question:how to promote deep conceptual learning via rich socio‐collaborative learning experiences for all students?—a question that is ripe for AI‐based facilitation and has the potential to transform classrooms. We advance research in speech, computer vision, human‐agent teaming, computer‐supported collaborative learning, expansive co‐design, and the science of broadening participation to design and study next generation AI technologies (called AI Partners) embedded in student collaborative learning teams in coordination with teachers. Our institute ascribes to theoretical perspectives that aim to create a normative environment of widespread engagement through responsible design of technology, curriculum, and pedagogy in partnership with K–12 educators, racially diverse students, parents, and other community members.

    more » « less
  4. Abstract

    The Institute for Foundations of Machine Learning (IFML) focuses on core foundational tools to power the next generation of machine learning models. Its research underpins the algorithms and data sets that make generative artificial intelligence (AI) more accurate and reliable. Headquartered at The University of Texas at Austin, IFML researchers collaborate across an ecosystem that spans University of Washington, Stanford, UCLA, Microsoft Research, the Santa Fe Institute, and Wichita State University. Over the past year, we have witnessed incredible breakthroughs in AI on topics that are at the heart of IFML's agenda, such as foundation models, LLMs, fine‐tuning, and diffusion with game‐changing applications influencing almost every area of science and technology. In this article, we seek to highlight seek to highlight the application of foundational machine learning research on key use‐inspired topics:

    Fairness in Imaging with Deep Learning: designing the correct metrics and algorithms to make deep networks less biased.

    Deep proteins: using foundational machine learning techniques to advance protein engineering and launch a biomanufacturing revolution.

    Sounds and Space for Audio‐Visual Learning: building agents capable of audio‐visual navigation in complex 3D environments via new data augmentations.

    Improving Speed and Robustness of Magnetic Resonance Imaging: using deep learning algorithms to develop fast and robust MRI methods for clinical diagnostic imaging.

    IFML is also responding to explosive industry demand for an AI‐capable workforce. We have launched an accessible, affordable, and scalable new degree program—the MSAI—that looks to wholly reshape the AI/ML workforce pipeline.

    more » « less
  5. Abstract

    The EngageAI Institute focuses on AI‐driven narrative‐centered learning environments that create engaging story‐based problem‐solving experiences to support collaborative learning. The institute's research has three complementary strands. First, the institute creates narrative‐centered learning environments that generate interactive story‐based problem scenarios to elicit rich communication, encourage coordination, and spark collaborative creativity. Second, the institute creates virtual embodied conversational agent technologies with multiple modalities for communication (speech, facial expression, gesture, gaze, and posture) to support student learning. Embodied conversational agents are driven by advances in natural language understanding, natural language generation, and computer vision. Third, the institute is creating an innovative multimodal learning analytics framework that analyzes parallel streams of multimodal data derived from students’ conversations, gaze, facial expressions, gesture, and posture as they interact with each other, with teachers, and with embodied conversational agents. Woven throughout the institute's activities is a strong focus on ethics, with an emphasis on creating AI‐augmented learning that is deeply informed by considerations of fairness, accountability, transparency, trust, and privacy. The institute emphasizes broad participation and diverse perspectives to ensure that advances in AI‐augmented learning address inequities in STEM. The institute brings together a multistate network of universities, diverse K‐12 school systems, science museums, and nonprofit partners. Key to all of these endeavors is an emphasis on diversity, equity, and inclusion.

    more » « less