The Role of Knowledge Graphs in Enhancing AI Determinism // Tom Smoker // MLOps podcast

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Managing Small Knowledge Graphs for Multi-agent Systems // MLOps podcast #236 with Tom Smoker, Technical Founder of whyhow.ai.
A big thank you to ‪@latticeflow‬ for sponsoring this episode! LatticeFlow - latticeflow.ai/
Tom unpacks his journey with AI, highlighting his work in the field of legal rack and information retrieval systems. He dives into the challenges of dealing with hallucinations in language models, emphasizing the importance of achieving determinism and explainability through the use of knowledge graphs. Tom's insights are particularly crucial for high-stakes environments like legal cases where accuracy is paramount.
// Abstract
RAG is one of the more popular use cases for generative models, but there can be issues with repeatability and accuracy. This is especially applicable when it comes to using many agents within a pipeline, as the uncertainty propagates. For some multi-agent use cases, knowledge graphs can be used to structurally ground the agents and selectively improve the system to make it reliable end to end.
// Bio
Technical Founder of WhyHow.ai. Did Masters and PhD in CS, specializing in knowledge graphs, embeddings, and NLP. Worked as a data scientist to senior machine learning engineer at large resource companies and startups.
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// Related Links
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models: arxiv.org/abs/2401.01313
Understanding the type of Knowledge Graph you need - Fixed vs Dynamic Schema/Data: / understanding-the-type...
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