This podcast episode digs deep into the history, challenges, opportunities, and applications of deep learning, particularly focusing on transformers, large language models, and the use of deep learning in biology. Experts discuss the need for efficiency, elasticity, and test time search in AI models, as well as the potential of deep learning to design better RNA and mRNA molecules for medicines. They emphasize the importance of empirical evidence in drug discovery and highlight the challenges of using deep learning for human augmentation, considering practical limitations and ambiguities in understanding general intelligence. Furthermore, the discussion delves into the interdisciplinary collaboration between deep learning and biology at Inceptive, using iterative data generation and feedback loops to find innovative solutions to biological problems.
