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The Artificial Intelligence Podcast

Dr. Tony Hoang
The Artificial Intelligence Podcast
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5 of 73
  • Interview #73 Jay Dawani, CEO of Lemurian Labs
    Join Jay Dawani, CEO of Lemurian Labs, as he discusses the critical infrastructure challenges facing AI development and his company's efforts to rebuild the AI software stack from the ground up. Drawing from his experience as a former NASA AI advisor working on Mars Rover navigation and exoplanet research, Dawani explains how current AI systems are plagued by massive inefficiencies, with some data centers operating at only 10-15% utilization despite consuming enormous amounts of energy. The conversation explores how the industry must shift from kernel-based programming models designed for single GPUs to dynamic runtime systems that can efficiently manage communication and memory across hundreds of thousands of processors, ultimately making advanced AI more accessible and sustainable.
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    25:32
  • Interview #72 Richard White, CEO of Fathom
    Join Richard White, CEO of Fathom, as he discusses how AI is transforming workplace productivity through intelligent meeting transcription and note-taking solutions. White explains how modern AI can now write better notes than most humans within 30 seconds of a meeting ending, while also enabling companies to capture and disseminate organizational knowledge more effectively by analyzing patterns across thousands of hours of meetings. The conversation explores the evolution from basic transcription services to sophisticated AI workflows that can generate action items, update CRMs, and provide executives with real-time alerts about important developments across their teams, ultimately reshaping how organizations communicate and collaborate in an AI-first workplace.
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    26:34
  • Interview #71 Nadia Harhen, General Manager of AI Simulation at SandboxAQ
    Join Nadia Harhen, General Manager of AI Simulation at SandboxAQ, and Jordan Crivelli-Decker from the Biosim team as they discuss how Large Quantitative Models (LQMs) are revolutionizing drug discovery and materials science by generating synthetic data through physics-based computational chemistry rather than just predicting outcomes like traditional language models. They explain how this approach accelerates drug development timelines, reduces animal testing needs, and enables breakthrough solutions for complex molecular problems that conventional software cannot handle, including work with Nobel laureate Dr. Stanley Prusiner that moved from research to clinical trials in just 18 months. The conversation explores how combining quantum mechanics principles with machine learning creates novel molecular IP across industries from pharmaceuticals to defense applications, bridging the gap between AI capabilities and practical scientific breakthroughs.
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    28:41
  • Interview #70 Debo Dutta, Chief AI Officer at Nutanix
    Debo Dutta, Chief AI Officer at Nutanix, discusses the critical challenges enterprises face when scaling generative AI beyond proof-of-concepts, identifying skills gaps, unclear implementation pathways, and data governance concerns as primary barriers. He emphasizes that successful AI transformation requires a holistic approach addressing people, processes, and technology, with particular focus on data cleaning and creating "gold standard" datasets as foundational competitive advantages. Looking ahead, Dutta predicts the emergence of AI agents operating with human oversight rather than fully autonomous systems, while highlighting that clean, private enterprise data will become the primary moat as AI models become increasingly commoditized.
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  • Interview #69 Michael Wu, Chief AI Scientist at PROS
    Dr. Michael Wu, Chief AI Scientist at PROS, discusses his transition from computational neuroscience research to applied AI in industry, emphasizing how his perspective without domain-specific baggage has enabled innovative problem-solving approaches. He explores the evolving landscape of AI agents and tools, highlighting the importance of Model Control Protocol (MCP) as a bridge between passive language models and actionable AI systems that can interact with existing enterprise tools. Wu emphasizes the need for organizations to create fail-safe environments that encourage AI experimentation while maintaining security, and advocates for balancing innovation speed with responsible development practices that prioritize safety, privacy, and legal compliance from the outset.
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    35:30

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About The Artificial Intelligence Podcast

Interviews and conversations with thought leaders in Artificial Intelligence, Machine Learning and Data Science
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