Reducing AI Hallucinations & Staying Awake at the Wheel

AI hallucinations are a fundamental feature of Large Language Models (LLMs). In essence, hallucination is the core function of LLMs - they generate plausible continuations based on learned patterns. This capability often results in outputs that are both useful and engaging.
In many applications, particularly in creative and knowledge-intensive professions, non-deterministic answers frequently hold greater value than deterministic ones.
However, there's a crucial caveat: we also want LLMs to provide factual and correct information, especially in critical applications (e.g., medical diagnosis, customer service, etc.). Herein lies the paradox - hallucinations represent both one of the most significant limitations of LLMs and one of their most compelling features that we have all come to love.
This video explores:
The nature and types of AI hallucinations
Underlying causes of this phenomenon
Implications for AI reliability and user trust
Practical strategies to mitigate unwanted hallucinations
Additionally, we examine the "falling asleep at the wheel" phenomenon, drawing on recent research to discuss its potential consequences as AI models become increasingly accurate.