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Supported by the National Science Foundation's Improving Undergraduate STEM Education: Hispanic-Serving Institutions (IUSE-HSI) Program, a collaborative summer research internship initiative united a public four-year institution with two local community colleges to offer community college students significant engineering research opportunities and hands-on experiences. In summer 2023, four students from the community college in computer science and engineering participated in a eight-week research internship project in a research lab at the four-year university. This internship project aimed to develop and implement of real-time computer vision on energy-efficient cortex-m microprocessor. This projet explores a unique approach to engage community college students in the realm of artificial intelligence research. By focusing on the development and implementation of real-time computer vision on energy-efficient Cortex-M microprocessors, we offer a practical and educational avenue for students to delve into the burgeoning field of AI. Through a combination of theoretical understanding and practical application, students are empowered to explore AI concepts, gain proficiency in low-power computing, and contribute to real-world AI projects. Furthermore, the project offered student interns a valuable opportunity to refine their research capabilities, particularly in the realms of scientific writing and presentation, while simultaneously boosting their self-assurance and enthusiasm for pursuing STEM careers in the field of AI.
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AI Safety for Physical Infrastructures: A Collaborative and Interdisciplinary Approach
ABSTRACT Where AI systems are increasingly and rapidly impacting engineering, science, and our daily lives, progress in AI safety for physical infrastructures is lagging. Most of the research and educational programs on AI safety do not consider that, in today's connected world, safety and security in physical infrastructures are increasingly entangled. This technical note sheds light, for the first time, on how computer science and engineering communities, for example, mechanical and civil, can collaborate on addressing AI safety issues in the physical infrastructures and the mutual benefits of this collaboration. We offer examples of how probabilistic views of engineers on safety can contribute to quantifying critical parameters such as “threshold” and “safety buffer” in the AI safety models, developed by the world‐leading computer scientists. We also offer examples of how novel AI and machine learning tools, for example,do‐operator, a mathematical operator for intervention (vs. conditioning);do‐calculus, machinery of causal calculus; and physics‐informed neural networks with a small number of samples can help fatigue and fracture research. We envision AI safety as a process, not an object, and contribute to realizing this vision by initiating a collaborative and interdisciplinary approach in establishing this process.
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- Award ID(s):
- 2041788
- PAR ID:
- 10566901
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Fatigue & Fracture of Engineering Materials & Structures
- Volume:
- 48
- Issue:
- 4
- ISSN:
- 8756-758X
- Format(s):
- Medium: X Size: p. 1919-1928
- Size(s):
- p. 1919-1928
- Sponsoring Org:
- National Science Foundation
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