In this episode, Curtis Northcutt, CEO & Co-Founder of Cleanlab, discusses the importance of data-centric AI and the challenges of addressing noisy data. They also delve into the journey of Cleanlab in improving data labeling accuracy, the success of the startup in finding and correcting bad data, and the frustrations of bug smashing. Additionally, they explore the challenges of understanding the value and capabilities of AI tools and companies, as well as the hiring opportunities in DevRel and front-end engineering.

Curtis Northcutt is Co-Founder & CEO of Cleanlab, the company that helps AI & ML teams automatically find and fix errors in their datasets. They have over 5K stars on GitHub and are already working with companies such as Wells Fargo and Google on ML data quality. In this episode, we discuss the difference between data noise and model noise, the growing importance of ML data quality with the momentum around generative AI models and applications, how Curtis' focus as CEO has shifted over time & much more!

Cleanlab's Curtis Northcutt and Berkeley Research Group's Steven Gawthorpe discuss using AI to combat economic crimes and corruption. They highlight Cleanlab's approach to data curation, automating data labeling, and assessing data reliability for investigations. The podcast explores the intersection of AI, data science, and ethical governance in fostering a more just society.