Social biases are well documented in decisions made by supervised models trained on ImageNet’s labels. But they also crept into the output of unsupervised models pretrained on the same dataset.

Large models pretrained in an unsupervised fashion and then fine-tuned on a smaller corpus of labeled data have achieved spectacular results in natural language processing. New research pushes forward with a similar approach to computer vision.

The words “big” and “large” have similar meanings, but they aren’t always interchangeable: You wouldn’t refer to an older, male sibling as your “large brother” (unless you meant to be cheeky). Choosing among words with similar meanings is critical in language tasks like translation.

Natural language processing lately has come to resemble an arms race, as the big AI companies build models that encompass ever larger numbers of parameters. Microsoft recently held the record — but not for long.

The ocean contains distinct ecosystems, but they’re much harder to see than terrestrial forests or savannas. A new model helps scientists better understand patterns of undersea life, which is threatened by pollution, invasive species, and warming temperatures.

Deep learning promises to help emergency responders find their way through disaster zones. MIT researchers developed a tool that maps where hurricanes and other calamities have wiped out roads, helping to show aid workers the fastest ways to get to people in need.

End-to-end backpropagation and labeled data are the peanut butter and chocolate of deep learning. However, recent work suggests that neither is necessary to train effective neural networks to represent complex data.

We’ve had great success with supervised deep learning on labeled data. Now it’s time to explore other ways to learn: training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world.