Learn More If there’s one thing that has fueled the rapid progress of AI and machine learning (ML), it’s data. Without high-quality labeled datasets, modern supervised learning systems simply ...

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 ...

But these AI and machine learning datasets — like the humans that ... learning excels in domains for which a lack of labeled data exists, it’s not a weakness. For example, unsupervised ...

Machine learning ... the training data and may not generalize well to new data. ML algorithms can also be sensitive to outliers and imbalanced or outdated training and test datasets.

A team led by computer scientists from MIT examined ten of the most-cited datasets used to test machine learning systems. They found that around 3.4 percent of the data was inaccurate or ...

In the world of machine learning and artificial intelligence, clean data is everything. Even a small number of mislabeled ...

A collaborative effort between Meta, Lawrence Berkeley National Laboratory and Los Alamos National Laboratory leverages Los ...

If the datasets used to train machine-learning models contain biased data, it is likely the system could exhibit that same bias when it makes decisions in practice. For instance, if a dataset ...