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.

(0:00) Welcome and introduction to episode 41

(0:25) Overview of large language models, hallucinations, and DataGemma

(2:38) Retrieval-Interleaved Generation vs. Retrieval-Augmented Generation

(5:32) Scaling in AI: Insights from Professor Ethan Mollick and dual scaling laws

(10:40) Sponsor: PwC's 2024 US Responsible AI Survey

(13:24) Deep dive into Responsible AI: Risks, objectives, and strategies

(18:14) ROI on Responsible AI and closing remarks