The AI Use Cases Podcast
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Responsible AI and Scaling: From Large Language Models to Dual Scaling Laws and ROI Insights
In episode 41, the discussion kicks off with an introduction to large language models and their potential pitfalls, such as hallucinations, using DataGemma as a case study. The conversation then contrasts Retrieval-Interleaved Generation with Retrieval-Augmented Generation, highlighting their unique approaches. Insights from Professor Ethan Mollick illuminate dual scaling laws in AI, shedding light on the intricacies of scaling AI technologies. The episode also features a segment on PwC's 2024 US Responsible AI Survey, followed by an in-depth exploration of Responsible AI, focusing on its risks, objectives, and strategies. The episode wraps up by evaluating the ROI on Responsible AI initiatives.
Key Points
- DataGemma models reduce hallucinations in large language models by anchoring them in real-world statistical data from Google's Data Commons.
- The Retrieval-Interleaved Generation (RIG) and Retrieval-Augmented Generation (RAG) methodologies enhance language model factuality and reasoning by querying and incorporating trusted sources.
- Responsible AI practices are essential for mitigating risks, ensuring regulatory compliance, and building trust with stakeholders, ultimately driving competitive differentiation and value generation.
Chapters
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Transcript
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