Join us for a deep dive into the unsettling world of LLM (Large Language Model) hallucinations – where AI confidently asserts false or nonsensical information. In this episode of Before AGI, we explore why AI sometimes "makes things up" and what's being done to fix it.
Key Insights:
The different types of LLM hallucinations (factual errors, fabricated stories, contradictions).
Real-world examples and the potential for harm (legal, medical, and personal).
The root causes: training data problems, model architecture limitations, and decoding strategies.
Cutting-edge solutions: RLHF, RAG, fact-checking, and prompt engineering.
The ethical implications of unreliable AI and the future of trustworthy systems.
Whether you're an AI developer, a user of AI tools, or simply curious about the limits of artificial intelligence, this episode reveals the critical challenges and exciting progress in making AI more reliable and truthful.
More from Host Ian Ochieng:
🌐 Website: ianochiengai.substack.com
📺 YouTube: Ian Ochieng AI
🐦 Twitter: @IanOchiengAI
📸 Instagram: @IanOchiengAI
