Affinity Groups
Announcements
Title | Date |
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NSF requests research and education use cases for NAIRR | 02/22/24 |
NVIDIA GenAI/LLM Virtual Workshop Series for Higher Ed | 02/17/24 |
Open Call: Minisymposia for PASC24 | 10/05/23 |
Upcoming Events & Trainings
Title | Date |
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Cyberinfrastructure-Enabled Machine Learning Summer Institute | 6/25/24 |
Topics from Ask.CI
CI Links
Title | Category | Tags | Skill Level |
---|---|---|---|
Introduction to Python for Digital Humanities and Computational Research | Docs | aibig-datadata-analysis +3 more tags | Beginner |
PyTorch for Deep Learning and Natural Language Processing | Docs | aibig-datadata-analysis +3 more tags | Beginner |
What is fairness in ML? | Docs | aivisualizationdata-analysis +2 more tags | Intermediate |
Engagements
AI for Business
The research focus is to apply the pre-training techniques of Large Language Models to the encoding process of the Code Search Project, to improve the existing model and develop a new code searching model. The assistant shall explore a transformer or equivalent model (such as GPT-3.5) with fine-tuning, which can help achieve state-of-the-art performance for NLP tasks. The research also aims to test and evaluate various state-of-the-art models to find the most promising ones.
High Performance Computing vs Quantum Computing for Neural Networks supporting Artificial Intelligence
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.