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 ...

Industries from retail to finance are using clustering to personalize services, detect fraud, monitor equipment and improve ...

Learn More Almost anyone can poison a machine learning ... is data poisoning and why does it matter? Data poisoning is a type of adversarial ML attack that maliciously tampers with 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 ...

This requires large bio-signal datasets in which instances of atrial fibrillation are labeled ... at the bio-signal. Machine learning allows researchers to learn from data and account for the ...

By using large bio-signal datasets, machine-learning algorithms are able ... It is often cheap to gather bio-signal data, but it can be expensive to label it. Instead, researchers can use the ...