Three experts discuss the evolving landscape of data in AI, covering its critical role in model performance, challenges in sourcing and evaluating expert data, the development of advanced data generation techniques for diffusion models, and the complexities of multilingual pre-training.
Dr. Mazi Fadai discusses the critical challenges in multilingual AI, including data imbalances and flawed evaluation methodologies. She argues that tackling these difficult multilingual problems is not only essential for global accessibility but also a catalyst for fundamental AI innovation, much like how machine translation research led to the Transformer architecture. The talk introduces new, more culturally aware evaluation benchmarks like Global MMLU and INCLUDE as a path toward building more robust and globally representative language models.