Machine learning’s impact on technology is significant, but it’s crucial to acknowledge the common issues of insufficient training and testing data.

Our understanding of progress in machine learning has been colored by flawed testing data. The 10 most cited AI data sets are riddled with label errors, according to a new study out of MIT, and it ...

Machine learning models are trained with huge amounts of data and must be tested before practical use. For this, the data must first be divided into a larger training set and a smaller test set ...

For training AI, synthetic data uses a base data set of actual historical events or transactions and then creates a synthetic representation of that data and builds upon it.

Personally identifiable information has been found in DataComp CommonPool, one of the largest open-source data sets used to train image generation models.