Insight Generation Platform for LifeScience Corporation - Hooman Sedghamiz | Vector Space Talks

Hooman Sedghamiz, a prominent figure in AI and ML at Bayer AG, discusses the advancements and challenges in the application of AI in life sciences, particularly focusing on the potential of large language models (LLMs) in various fields such as precision medicine and drug discovery. He emphasizes the importance of real-time evaluation and cost-effective strategies in maintaining the integrity of AI systems, particularly in chatbot interactions, and highlights the need for innovation in data pipelines to enhance retrieval-augmented generation. Sedghamiz also explores the scalability of AI models within large corporations and the potential cost savings from using open-source models over subscription-based services. He notes the emergence of vector stores and the significant progress in model evaluation and function calling, suggesting that AI's role in efficiency gains and scientific discovery is a developing field with untapped potential. The discussion also covers the necessity for tailored evaluation metrics and the integration of contextual data to build trust in AI applications, especially in healthcare, while identifying the challenges such as ETL inefficiencies and access to paywalled scientific knowledge.