AI Hallucinations: When the Machine Thinks It Knows
What if the most confident answer your AI gives you… was completely made up? In this episode, you’ll hear from Daniel Wilson, AI and Data Sovereignty Advisor to Malaysia’s National AI Office and founder of InfoScience AI. With years of hands-on experience in neuromorphic systems and secure AI architectures, Daniel helps you unpack a central paradox of generative models: why they hallucinate, why that might not be a bug, and how you can reduce the risk when using them. Together, you explore the mechanics of inference, the limits of context windows, and the subtle art of prompting. Daniel also explains how AI hallucinations mirror aspects of human memory—and what it takes to design systems that truly know when they don’t know. If you’ve ever used AI in your work, this episode will give you the tools to better understand its blind spots—and maybe your own.
Daniel Wilson is an AI and Data Sovereignty Advisor to Malaysia’s National AI Office, where he helps shape national strategy around ethical and secure AI deployment. A lifelong technologist, Dan has spent a lifetime coding across a wide range of languages and systems. He is the CTO of Cyber Intel Training and founder of Info Science AI, where his current focus is on cognitive computing, neuromorphic memory systems, and safe AI integration. Dan also hosts The AI Think Tank Podcast and Neuroscience Frontiers, and is a featured author at Data Science Central. His AI-driven digital forensic solutions have won him awards from MIT.
Daniel Wilson
AI and Data Advisor
