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While artificial intelligence and machine learning (AI/ML) frameworks gain prominence in science and engineering, most researchers face significant challenges in adopting complex AI/ML workflows to campus and national cyberinfrastructure (CI) environments. Data from the Texas A&M High Performance Computing (HPRC) researcher training program indicate that researchers increasingly want to learn how to migrate and work with their pre-existing AI/ML frameworks on large scale computing environments. Building on the continuing success of our work in developing innovative pedagogical approaches for CI- training approaches, we expand CI-infused pedagogical approaches to teach technology-based AI and data sciences. We revisit the pedagogical approaches used in the decades-old tradition of laboratories in the Physical Sciences that taught concepts via experiential learning. Here, we structure a series of exercises on interactive computing environments that give researchers immediate hands-on experience in AI/ML and data science technologies that they will use as they work on larger CI resources. These exercises, called “tech-labs,” assume that participating researchers are familiar with AI/ML approaches and focus on hands-on exercises that teach researchers how to use these approaches on large-scale CI. The tech-labs offer four consecutive sessions, each introducing a learner to specific technologies offered in CI environments for AI/ML and data workflows. We report on our tech-lab offered for Python-based AI/ML approaches during which learners are introduced to Jupyter Notebooks followed by exercises using Pandas, Matplotlib, Scikit-learn, and Keras. The program includes a series of enhancements such as container support and easy launch of virtual environments in our Web-based computing interface. The approach is scalable to programs using a command line interface (CLI) as well. In all, the program offers a shift in focus from teaching AI/ML toward increasing adoption of AI/ML in large-scale CI.
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Creating intelligent cyberinfrastructure for democratizing AI
Abstract Artificial intelligence (AI) has the potential for vast societal and economic gain; yet applications are developed in a largely ad hoc manner, lacking coherent, standardized, modular, and reusable infrastructures. The NSF‐funded Intelligent CyberInfrastructure with Computational Learning in the Environment AI Institute (“ICICLE”) aims to fundamentally advanceedge‐to‐center, AI‐as‐a‐Service, achieved through intelligent cyberinfrastructure (CI) that spans the edge‐cloud‐HPCcomputing continuum,plug‐and‐playnext‐generation AI and intelligent CI services, and a commitment to design for broad accessibility and widespread benefit. This design is foundational to the institute's commitment to democratizing AI. The institute's CI activities are informed by three high‐impact domains:animal ecology,digital agriculture, andsmart foodsheds. The institute's workforce development and broadening participation in computing efforts reinforce the institute's commitment todemocratizing AI. ICICLE seeks to serve asthe national nexus for AI and intelligent CI, and welcomes engagement across its wide set of programs.
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- Award ID(s):
- 2112606
- PAR ID:
- 10505606
- Publisher / Repository:
- Wiley Online Library
- Date Published:
- Journal Name:
- AI Magazine
- Volume:
- 45
- Issue:
- 1
- ISSN:
- 0738-4602
- Page Range / eLocation ID:
- 22 to 28
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Journal Article:
- https://doi.org/10.1002/aaai.12166
