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Title Category Tags Skill Level
ACCESS HPC Workshop Series Learning big-datadeep-learningmachine-learning +13 more tags Beginner, Intermediate
AI/ML TechLab - Accelerating AI/ML Workflows on a Composable Cyberinfrastructure Docs ACESdocumentationTAMU +19 more tags Intermediate
Automated Machine Learning Book Learning aidata-analysisdeep-learning +4 more tags Intermediate, Advanced

Engagements

High Performance Computing vs Quantum Computing for Neural Networks supporting Artificial Intelligence
Pace University

A personalized learning system that adapts to learners' interests, needs, prior knowledge, and available resources is possible with artificial intelligence (AI) that utilizes natural language processing in neural networks. These deep learning neural networks can run on high performance computers (HPC) or on quantum computers (QC). Both HPC and QC are emergent technologies. Understanding both systems well enough to select which is more effective for a deep learning AI program, and show that understanding through example, is the ultimate goal of this project. The entry to learning technologies such as HPC and QC is narrow at present because it relies on classical education methods and mentoring. The gap between the knowledge workers needed, which is in high demand, and those with the expertise to teach, which is being achieved at a much slower rate, is widening. Here, an AI cognitive agent, trained via deep learning neural networks, can help in emergent technology subjects by assisting the instructor-learner pair with adaptive wisdom. We are building the foundations for this AI cognitive agent in this project.

The role of the student facilitator will involve optimizing a deep learning neural network, comparing and contrasting with the newest technologies, such as a quantum computer (and/or a quantum computer simulator) and a high performance computer and showing the efficiency of the different computing approaches. The student facilitator will perform these tasks at the rate described in the proposal. Milestone work will be displayed and shared publicly via posting to the Jupyter Notebooks on Google Colab and linked to regular Github uploads.

Status: Complete

People with Expertise

Grant Scott

University of Missouri

Programs

Great Plains

Roles

mentor, steering committee

Expertise

Andrew Cheng

Rutgers University New-Brunswick

Programs

CAREERS

Roles

student-facilitator

Expertise

Daniel Sierra-Sosa

Hood College

Programs

Campus Champions

Roles

research computing facilitator

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Expertise

People with Interest

Xiaoyi Lu

Programs

ACCESS CSSN

Roles

cssn

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Interests

Matthew Chung

University of California Riverside

Programs

Campus Champions

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Interests

Adedeji Adekunle

Rutgers University, Camden

Programs

CAREERS

Roles

student-facilitator

Interests