Are LLMs stochastic parrots or reflection of our own intelligence? In this episode of Navigating Major Programmes, Riccardo Cosentino sits down with Lawrence Rowland for an extremely candid conversation surrounding the adoption of artificial intelligence, in major programmes and beyond. AI skeptics and AI enthusiasts alike, this episode was recorded for you.

“None of us are keeping up, none of us know what the hell is going on. So, if you can kind of just relax and enjoy it happening, you will also help everyone else so much more. Enjoy it. And enjoy what [AI] is telling us about us.”  –Lawrence Rowland

Lawrence began as an engineer on large capital projects with WSP and Motts, before moving onto Bechtel and Booz Allen. He spent ten years in project and portfolio management with CPC and Pcubed, before transitioning to data analytics and AI for projects, working originally for Projecting Success, and now for React AI. He now helps project services firms find relevant immediate AI applications for their business.

Key Takeaways:

• Large Language Model (LLM) 101
• What is an AI agent? What is the principal-agent problem (PAP)?
• What LLMs can teach you about your own thinking patterns
• The future of Google Gemini and AI adoption in general
• The weaknesses of the generative AI of today

Mentioned Links:

• A Path Towards Autonomous Machine Intelligence
• Principal Agent Problem
• Applied Category Theory
• Wisdom of Crowds
• State Space Models and Mamba
• Demis Hassabis and the return of alpha zero type tree search and RL

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Music: "A New Tomorrow" by Chordial Music. Licensed through PremiumBeat.