Feb 10, 2021

Over the last several decades, driven by a multitude of benchmarks, supervised learning algorithms have become really good at achieving high accuracy on test datasets. As valuable as this is...

Amazon is monitoring its delivery drivers with in-vehicle cameras that alert supervisors to dangerous behavior. The online retail giant rolled out a ceiling-mounted surveillance system that flags drivers who, say, read texts, fail to use seatbelts, exceed the speed limit, or ignore a stop sign.

For children, associating a word with a picture that illustrates it helps them learn the word’s meaning. Research aims to do something similar for machine learning models. Researchers improved a BERT model’s performance on some language tasks by training it on a large dataset of image-word pairs.

Doctors who treat breast cancer typically use a quick, inexpensive tumor-tissue stain test to diagnose the illness and a slower, more costly one to determine treatment. A new neural network could help doctors to go straight from diagnosis to treatment.

Over the last several decades, driven by a multitude of benchmarks, supervised learning algorithms have become really good at achieving high accuracy on test datasets. As valuable as this is, unfortunately maximizing average test set accuracy isn’t always enough.

Western governments and institutions struggling to formulate principles of algorithmic fairness tend to focus on issues like race and gender. A new study of AI in India found a different set of key issues.

Computer vision is helping people resell their used designer handbags. Rebag, a resale company for luxury handbags, watches, and jewelry, launched Clair AI, an app that automatically appraises second-hand bags from brands like Gucci, Hermes, and Prada.