{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Low Down on Low-Code","title":"Explainable AI w/ James Duez of Rainbird","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/1344c79d\"></iframe>","width":"100%","height":180,"duration":2766,"description":"In this episode, James Duez, CEO of Rainbird, dives into the intricacies of how knowledge and context are represented within AI systems. We explore how structured knowledge graphs, deterministic reasoning engines, and hybrid architectures can elevate AI decision-making and trustworthiness. Whether you're a product manager, AI developer, or business leader, this conversation clarifies how to architect AI solutions that are explainable, auditable, and reliable.\nThe distinction between case context (transaction-specific data) and policy context (knowledge and rules) in AI systems.\nHow Rainbird leverages knowledge graphs and ontologies to formalize and reason over policy and tacit organizational knowledge.\nThe limitations of large language models (LLMs) in reasoning and the importance of keeping them outside the core decision logic.\nThe role of inference engines in building deterministic, auditable AI decision layers grounded in policy and knowledge.\nThe evolution from traditional knowledge management tools like SharePoint to advanced knowledge elicitation and modeling using AI.\nThe concept of knowledge as a first-class citizen in AI architecture, enabling faster and more reliable decision-making.\nThe risks of relying solely on LLMs, including hallucinations, model drift, and lack of explainability, and how hybrid architectures mitigate these issues.\nThe importance of roles like knowledge engineers and business analysts in managing and architecting organizational knowledge layers.\nRainbird's reusable, enterprise-grade platform for building, managing, and reasoning over knowledge graphs, separable from data sources.\nThe future of hybrid AI architectures: combining symbolic reasoning with probabilistic models to balance flexibility and trust.","thumbnail_url":"https://img.transistorcdn.com/bxFjDudHZxWaYrd-K7RUewyX1s54chL4YCT94Y5XfcE/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQ5OTE5LzE3MTA0/NDA5MzEtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}