Powerful language models are reshaping the world, but serious challenges remain. In this revealing episode of "All Things LLM," hosts Alex and AI expert Ben tackle the core limitations and ethical risks facing all large language models—open or closed.

This episode covers:

• Hallucinations: Why LLMs make up plausible-sounding but false or misleading answers, and how techniques like Retrieval-Augmented Generation (RAG) help ground responses in reality.
• Bias: How AI adopts and even amplifies societal biases from its training data, real-world examples, and ongoing strategies—like rigorous data curation and specialized benchmarks (e.g., BOLD, BBQ)—to detect and mitigate unfairness.
• The Black Box Problem: Why even the developers of advanced models struggle to explain how and why LLMs generate specific outputs, and the emerging field of Mechanistic Interpretability (MI) working to unravel the mystery through techniques like activation patching.
• Environmental Impact: Hard-hitting statistics about the staggering energy and water consumed by training and running state-of-the-art models, and how the push toward “Green AI” is driving the development of smaller, more efficient systems.

Perfect for listeners searching for:

• LLM hallucinations explained
• AI model bias and ethics
• Black box problem in machine learning
• Environmental cost of AI
• Interpretable and accountable AI
• Green AI trends
• How to make AI trustworthy

This episode gives business leaders, engineers, and AI enthusiasts a realistic, honest look at the risks and responsibilities that come with deploying these powerful tools. Subscribe now, and join us next week as Alex and Ben break down LLM security—covering the latest threats, from prompt injection to data poisoning, and how to defend AI systems in the wild.

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