Data Quality Improvement

Data Quality Improvement

Recently, there have been a lot of AI failures in the mainstream media. The reason for a lot of these failures is that there was poor quality of data somewhere in the creation of artificial intelligence. To solve this, humans-in-the-loop can provide a successful feedback loop to discourage errors moving forward - machine learning cannot detect these errors on its own. 

To avoid failures in the outcome of your AI projects, improve your AI data quality by collecting more training data and using a human-in-the-loop platform, like CrowdFlower AI.