Shayan delves into the complexities of data labeling, highlighting the inherent biases and inconsistencies that arise from human involvement. He emphasizes the unsustainable nature of relying on manual labeling, especially in high-stakes scenarios like medical diagnoses or adversarial contexts such as spam detection. The conversation also touches on the significant capital and time constraints faced when trying to build impactful machine learning models.
