The field of machine learning (ML) is experiencing a paradigm shift. While the focus on innovative algorithms and architecture once dominated and might continue to evolve, the spotlight is now on DATA. Large models are becoming commonplace, and real-world effectiveness is paramount. This necessitates a data-centric approach, encompassing the entire data lifecycle, from collection, cleansing, orchestration to supply, to satisfy the hunger of the humongous and ever growing models.

This panel discussion will delve into the industry challenges associated with data efficiency. We will explore: * The rise of data-centricity: Moving beyond algorithms to prioritize data quality, management, and utilization. * Challenges of large models and real-world application: Ensuring data is sufficient, representative, and addresses real-world complexities. * Data lifecycle considerations: Optimizing data collection, storage, transformation, and integration for robust AI systems.

By fostering open dialogue on these critical challenges and opportunities, this panel discussion aims to propel the field of data-centric AI towards a future of responsible, impactful, and collaborative innovation.