This article presents a comprehensive overview of Vivo's BlueLM large language model, covering its historical evolution, the massive data and algorithmic challenges faced during training, safety and performance optimizations, and how the model has been integrated into various consumer and enterprise products.
In this talk, senior researcher Dai Quanyu from Huawei Noah's Ark Lab presents his work on applying causal inference to identify and correct various biases in recommendation systems, detailing underlying theoretical frameworks, bias‑mitigation algorithms such as inverse propensity weighting and robust learning, and real‑world product deployments.
The article outlines a three‑dimensional framework—technical, business, and soft‑skill competencies—that algorithm engineers need to master in order to successfully deliver machine‑learning solutions in production environments, offering practical advice on data handling, model evaluation, stakeholder communication, and personal development.
