This lack of data limits researchers’ ability to spot biases in algorithms trained on the images. And such algorithms could very well be biased: Of the images with skin tone information, only 11 ...

Opinion: Akerman's Melissa Koch explains why the quality of data in legal artificial intelligence matters more than the ...

AI systems that use data trained with descriptive labeling can yield much harsher decisions than humans make. It's not too late to fix the problem. Algorithms soon will run your life - and ruin it ...

Creating smart, accurate AI algorithms is an ongoing effort that requires validation of training sets and some level of human intervention. Here's a guide to how to craft effective ones.

The role of training data in this context is to provide the algorithm with a large set of unlabeled transactions containing only features such as sender and receiver addresses, transaction amounts ...

Sensor data: A model built to predict machinery failures could be trained on sensor data paired with labels like “high vibration” or “over temperature.” Depending on the use case, models ...

Everything I discussed there–repurposing labeled data, harvesting your own free sources, exploring pre-labeled public datasets, acquired pretrained models, using crowdsourced labeling services, etc.— ...

In unsupervised learning, the training data doesn’t come paired with any descriptive labels. Rather, machine learning algorithms process large amounts of data, which are then grouped into ...

Introducing Adaptive-k, a novel algorithm for robust training of label noisy datasets, which is easy to implement and does not require additional model training or data augmentation.