Automation in Labeling
Shayan discusses the growing accessibility of data labeling work, which has led to an oversupply of labor and declining wages. He emphasizes the need for automation to alleviate the burden of undesirable jobs, like content moderation, and advocates for a more efficient process where a single expert can leverage software to streamline labeling tasks. This shift aims to create sustainable workflows while reducing reliance on manual labor.In this clip
From this podcast
Super Data Science: ML & AI Podcast with Jon Krohn
635: The Perils of Manually Labeling Data for Machine Learning Models — with Shayan Mohanty
Related Questions
Is less labeled data needed for training machine learning models according to the episode The Fallacy of "Ground Truth" with Shayan Mohanty - #576 and the clip Active Learning Insights?
Is less labeled data needed for training machine learning models as discussed in the episode "Big Data Doesn't Exist" and the clip "Deep Learning Insights" featuring Ilya Sutskever (OpenAI Chief Scientist) - Building AGI, Alignment, Spies, Microsoft, & Enlightenment and Running Out of Reasoning Tokens?
Is less labeled data needed for training machine learning models as discussed in the episode Ilya Sutskever (OpenAI Chief Scientist) - Building AGI, Alignment, Spies, Microsoft, & Enlightenment and the clip Running Out of Reasoning Tokens?