In this episode of the Transform Now podcast, host Michael Marchuk sits down with Vincent Granville, pioneering AI scientist and co-founder of Bonding AI, to explore how organizations can build AI systems they can truly trust. Granville challenges the prevailing acceptance of AI hallucinations, arguing they're not just bugs but structural features of how large language models are built. He introduces his company's xLLM platform, which offers hallucination-free, secure enterprise AI through a dual-layer approach that combines natural language responses with structured, verifiable outputs.
Key topics covered:
Why hallucinations persist in LLMs and why patches don't fix the root problem
The value of relevancy and trustworthiness scores in AI responses
Model optimization techniques including quantization and auto-distillation
How smaller, purpose-built models can outperform massive generic LLMs
Practical steps for organizations transitioning to deterministic AI systems
The future of verifiable AI in regulated industries like finance
Whether you're a technical leader, business executive, or AI enthusiast, this episode provides valuable insights into building more reliable, cost-effective, and trustworthy AI systems for your organization.
Connect with Vincent Granville:
Website: mltechniques.com
Company: Bonding AI
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