Adrien Morisot, an ML engineer at Cohere, discusses the transformative use of synthetic data in AI training. He explores the prevalent practice of using synthetic data in large language models, emphasizing model distillation techniques. Morisot shares his early challenges in generative models, breakthroughs driven by customer needs, and the importance of diverse output data. He also highlights the critical role of rigorous validation in preventing feedback loops and the potential for synthetic data to enhance specialized AI applications across various fields.
