The perplexities of information retrieval

Practical AI: Machine Learning, Data Science, LLM

This podcast episode explores the development of generative AI in the context of answering questions, discovering knowledge, and exploring topics. The conversation highlights the challenges of traditional search engines and the goal of obtaining true answers to questions. It discusses the journey of Perplexity, the answer engine, from its early ambitions and skepticism to its growth in usage. The episode also emphasizes the importance of tools and agentic behaviors for accurate information retrieval from language models. It further discusses the need for balancing general models and specialist models in information retrieval systems and the challenges associated with changing prompts and AI model debt. The podcast concludes with the importance of building model-agnostic systems, exploring new types of user interface (UI) or user experience (UX), and the future of machine learning in generating information and making decisions.

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