BayesiaLab & Hellixia Innovations with GenAI

This new three-day course program is designed to help you employ Generative AI in the context of Bayesian networks.
This new three-day course program is designed to help you employ Generative AI in the context of Bayesian networks usingHellixia, BayesiaLab's GenAI assistant.Hellixiaoffers a powerful set of capabilities to streamline the design, analysis, and documentation of knowledge models.
The course focuses on the five core functions ofHellixia, giving you practical skills to integrate them into your modeling workflows. These core functions either use internal knowledge embedded in Large Language Models (LLMs) or combine it with specific knowledge files, similar toRetrieval-Augmented Generation (RAG).

LLM Knowledge Miningtaps into the knowledge embedded in Large Language Models (LLMs) and can automatically generate a wide variety of network types, including:
- Semantic Networks
- Semantic Flowcharts
- Causal Semantic Diagrams
- Knowledge Graphs
- (Risk) Causal Networks.

LLM-Augmented Machine Learningcan identify causal relationships between nodes and automatically add arcs to a network and/or proposeStructural Priors.Hellixiaalso generates narratives to explain these relationships for easier interpretation and subsequent validation by domain experts.
In this context, Hellixia can also propose meaningful names for newly induced factors (latent variables) generated by BayesiaLab'sMultiple-Clusteringfunction. Similarly,Hellixiacan suggest names for segments identified with BayesiaLab'sData Clusteringfunction.

LLM-Powered Brainstorming Assistantssupport and accelerate brainstorming sessions by leveraging dimensions provided by subject matter experts, e.g., viaBEKEE. Hellixia uses these inputs to construct a semantic network that organizes, clusters, and defines the dimensions, thereby becoming a crucial tool for deciding which dimensions to include in a model.
In the quantitative phase, Hellixia assists in the elicitation ofRoot PriorsandICI Local Effects, providing probability values, confidence levels, and explanatory texts.

The LLM Text-Driven Causal Discovery function analyzes unstructured textual data, such as customer reviews, transcripts, survey responses, or knowledge documents, to extract structured insights from LLMs.Hellixiaidentifies key drivers and themes, organizes them into a semantic network, and elicitsRoot PriorsandICI Local Effectsfor each dimension, thus enabling a full driver analysis and a subsequent optimization. This mirrors the brainstorming workflow with subject matter experts (seeBEKEE) but relies entirely on LLM intelligence, making it ideal when expert input is unavailable or when handling large volumes of freeform text.

LLM-Enhanced Network Documentation Toolssimplify and enrich the documentation and presentation of networks.Hellixiagenerates Node Comments, Long Names, and narratives about relationships. Furthermore, it supports the multilingual translation of networks and creates icons for nodes based on their semantic content.

Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks.
After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab, which has since emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. BayesiaLab enjoys broad acceptance in academic communities, business, and industry.
1. LLM Knowledge Mining2. LLM-Augmented Machine Learning3. LLM-Powered Brainstorming Assistants4. LLM Text-Driven Causal Discovery5. LLM-Enhanced Network Documentation ToolsAbout the Instructor