One of the biggest challenges in AI is to replicate human reasoning and language. That’s why it is so important to include human-in-the-loop when creating high-quality AI datasets to train a machine learning model.
However, access to this high-quality data has, and continues to be, a challenge that has frustrated the more widespread adoption of AI technologies. Crowdsourcing was long ago identified as a solution to this challenge: by micro-tasking out data collection and annotation jobs to hundreds of thousands of contributors, datasets were likely to be completed much faster and with more accuracy and diversity.
However, with so many variables at play, how do you ensure quality when crowdsourcing data?
Watch Bradley Metrock (This Week in Voice) and Francisco España (VP of DefinedCrew at DefinedCrowd) as they discuss about the huge potential and advantages of using a qualified crowd to build an AI model.