Data-Centric AI: The Latest Research You Need to Know - KDnuggets

While a vast majority of research efforts today are preoccupied solely with ML models and algorithms, the data itself tends to be secondary and is treated as fixed. This claim is potentially detrimental – there's a big risk of favoring theory over practice as the models are becoming more divorced from the ground truth. There's a need to combat this trend by providing incentive and information to researchers and practitioners alike to work with the data instead. All of the articles are available on the Proceedings page here, as are Datasets and Benchmarks and the Data-Centric AI Workshop respectively. It's extremely important to measure the quality of data sets; however, there's currently no universally agreed-upon method of how to do it.