In this episode of Before AGI, we explore how AI learns and the challenges of understanding its decision-making process. From one-shot learning to the black box problem, we discuss why explainable AI is vital for building trust in AI systems.
🔍 Key Topics:
One shot learning: How AI learns from minimal data
Challenges with AI biases and decision-making transparency
Tools for explainability: LIME, SHAP, and MANNs
The push for human-centered AI: Privacy, ethics, and accountability
🎧 Dive into this insightful discussion to understand why making AI explainable and ethical is essential for its integration into society!
More from Ian Ochieng:
🌐 Website: ianochiengai.substack.com
📺 YouTube: Ian Ochieng AI
🐦 Twitter: @IanOchiengAI
📸 Instagram: @IanOchiengAI
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