Conveners
Curious About AI: 2B
- Chris de loof (Belnet)
Description
Explore the development of HAWAT, an agentic AI assistant designed for network troubleshooting. This innovative system leverages advanced technologies like Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Reasoning and Acting (ReAct) frameworks to autonomously manage network conditions and provide natural language interfaces for network administrators. Next, dive into the AI4LAM initiative, which is revolutionizing the cultural heritage sector. This collaborative network is dedicated to advancing AI tools and services for libraries, archives, and museums, enhancing the management and accessibility of digitized content while fostering innovation and knowledge sharing. Finally, discover the FedXAI4DNS project, which employs Federated Learning and Explainable AI to bolster DNS security in privacy-aware environments. This project showcases how AI can collaboratively detect malicious traffic, ensuring network security without compromising user privacy. This session promises to be a captivating journey through the latest AI innovations, offering valuable insights for anyone curious about the transformative potential of artificial intelligence.
The AI for Libraries, Archives, and Museums (AI4LAM) community is an international, participatory network dedicated to advancing the use of artificial intelligence within the cultural heritage sector. The community is at the forefront of developing and maintaining cutting-edge AI tools and services tailored for heritage institutions to better provide access, management and (re)use of digitized...
Machine Learning (ML) has seen limited adoption within large-scale networks (e.g. NRENs). Organisations are reluctant to share their data in fear of compromising end-user privacy, thus representative datasets to train accurate ML classifiers are usually not available. Moreover, complex black-box ML classifiers are not intrinsically explainable, hence network engineers are reluctant to deploy...
HAWAT (Heuristic Analysis With Adaptive Troubleshooting) is an agentic AI assistant for network troubleshooting. This presentation explores the development of a chatbot system designed to interface with network hardware, leveraging recent advances in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Reasoning and Acting (ReAct) agentic frameworks. Our system demonstrates...
