#15: Digging into explainable AI
Unaligned with Robert Scoble
00:00
Understanding AI Limitations and Explainability
This chapter explores the complexities of large language models, focusing on their propensity to produce incorrect answers due to their reliance on next-word prediction. It discusses the challenge of verifying outputs and highlights the role of explainable AI techniques, including self-evaluation and retrieval-augmented generation. Furthermore, the chapter addresses the need for AI to incorporate human-like reasoning and transparency, particularly in applications like autonomous vehicles.
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