Retrieval Function Insights
The discussion highlights the significance of the retrieval function in AI, emphasizing three key components that could shape its future. Joon shares insights on the adaptability of large language models, specifically mentioning the transition from GPT-3 to ChatGPT and its implications for agent performance. The conversation also touches on the parallels between human memory and AI processes, suggesting that novelty plays a crucial role in both domains.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Modeling Human Behavior with Generative Agents with Joon Sung Park - 632
Related Questions
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business in the episode MLOps for GenAI Applications // Harcharan Kabbay // #256 and the clip Evaluating LLM Responses?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business and the clip Model Evaluation Insights?
What's your opinion on using large language models (LLMs) for scientific research, especially for generating new ideas for hypotheses, as discussed in the episode Does ChatGPT “Think”? A Cognitive Neuroscience Perspective with Anna Ivanova - 620 and the clip Language Model Insights?