Unveiling the Future: The Impact of chatGPT Talks

Unveiling the Future: The Impact of chatGPT Talks

Table of Contents:

1. Introduction
2. The Need for AI in Computing Services
3. Limitations of Out-of-the-Box ChatGPT
4. The Concept of Chaining
5. Introduction to subMIT
6. The Role of Archie in subMIT
7. The Prototype: Archie the AI Augmented Research Chat Intelligence
8. Archie's Integration with subMIT
9. The Future of Archie: Direct Interaction with Computing Resources
10. Conclusion

Introduction

AI Augmented Support for Computing Services: A Revolution in Physics

Article:

Introduction

In recent years, computing has become an integral part of physics. The availability of vast computational power has allowed scientists to process large data sets and perform complex simulations. However, using computing services often requires specific knowledge and expertise, making it cumbersome for researchers. Traditional solutions, such as help desks or giant repositories of information, are slow and tedious to Interact with. This is where AI augmented support comes in.

The Need for AI in Computing Services

AI has emerged as a powerful tool for assisting researchers in navigating the complexities of computing services. Large language models, like ChatGPT, have shown promise in providing text-Based support. However, out-of-the-box solutions have limitations. They lack knowledge of the user's specific computing system and often provide inaccurate or irrelevant answers. Additionally, they can only offer text-based support, neglecting the interactive nature of computing.

Limitations of Out-of-the-Box ChatGPT

While ChatGPT is a valuable resource, it falls short when it comes to understanding and interacting with complex computer systems. Its lack of familiarity with specific installations and its text-based approach limit its usefulness. Furthermore, creating custom models is prohibitively expensive, with costs reaching up to $100 million for training GPT-4.

The Concept of Chaining

To address the limitations of ChatGPT, a concept called chaining has been developed. Chaining involves creating a sequence of inputs and outputs to guide the AI's understanding and response. By incorporating the user's chat history, the specific question, and Relevant input files, chaining allows for a more Context-aware and accurate response.

Introduction to subMIT

subMIT is a centralized computing resource available to the Physics department. It offers high-performance servers, vast storage space, and powerful tools like Jupyter hub and batch job submission through Slurm or HTCondor. subMIT emphasizes user support, providing a comprehensive Website, a well-written user guide, and an active help desk.

The Role of Archie in subMIT

Archie is an AI augmented research chat intelligence designed to assist users of subMIT. Unlike traditional chatbots, Archie is seamlessly integrated into the collaboration and actively participates in managing the computing resources. It can write emails, handle help desk queries, and interact with users through Slack. Archie leverages the power of chaining to provide accurate and relevant assistance to subMIT users.

The Prototype: Archie the AI Augmented Research Chat Intelligence

Archie is powered by LangChain, Gradio for the user interface, and Chroma for the vector database. It is currently live and accessible to users. Archie not only provides text-based chat interactions but also acts as an identity within the subMIT help team. The prototype demonstrates the potential of AI augmentation in improving user experience and efficiency.

Archie's Integration with subMIT

The goal of Archie is to seamlessly integrate as a member of the subMIT help desk team. It should perform actions within subMIT, such as checking user quotas, and be knowledgeable about the nuances of the system. By acting as a user on subMIT, Archie becomes an entity that users can interact with beyond just a chatbot.

The Future of Archie: Direct Interaction with Computing Resources

The development of Archie continues, with plans to enable direct interaction with computing resources. This would allow Archie to assist users with specific computing tasks, such as debugging errors or monitoring job statuses. As Archie learns from user interactions, its responses can be refined, leading to continuous improvement and a more seamless user experience.

Conclusion

AI augmented support for computing services, as demonstrated by Archie, has the potential to revolutionize how users interact with complex systems. By leveraging chaining and integrating with existing tools like subMIT, Archie offers a more personalized and efficient user experience. As Archie continues to evolve, it will become an indispensable member of the subMIT help desk team, working alongside humans to provide comprehensive support and improve user satisfaction.