Where labeled training data is scarce, an algorithm can learn to request labels for key examples. While this practice, known as active learning, can supply labeled examples that improve performance in some tasks, it fails in others.

Nothing is certain except death and taxes, the saying goes — but how to make taxes fair and beneficial remains an open question. New research aims to answer it.

If you change an image by moving its subject within the frame, a well trained convolutional neural network may not recognize the fundamental similarity between the two versions. New research aims to make CNN wise to such alterations.

DeepMind released three papers that push the boundaries — and examine the issues — of large language models.

Transformers have matched or exceeded earlier architectures in language modeling and image classification. New work shows they can achieve state-of-the-art results in some reinforcement learning tasks as well.

Classical machine learning techniques could help children with autism receive treatment earlier in life. Researchers led by Ishanu Chattopadhyay at University of Chicago developed a system that classified autism in young children based on data collected during routine checkups.

New research shows that drastically increasing the number of layers in a graph neural networks improves its performance on large datasets.

Facebook released a chatbot that summarizes dialog on the fly and uses the summary to generate further repartee.

Chatbots often respond to human input with incorrect or nonsensical answers. Why not enable them to search for helpful information?

A new machine learning method aims to improve a machine’s ability to determine and locate points of interest.

To understand a movie scene, viewers often must remember or infer previous events and extrapolate potential consequences. New work improved a model’s ability to do the same.

A neural network can make a photo resemble a painting via neural style transfer, but it can also learn to reproduce an image by applying brush strokes. A new method taught a system this painterly skill without any training data.

The emerging generation of trillion-parameter models needs datasets of billions of examples, but the most readily available source of examples on that scale — the web — is polluted with bias and antisocial expressions. A new study examines the issue.

The transformer architecture is notoriously inefficient when processing long sequences — a problem in processing images, which are essentially long sequences of pixels. One way around this is to break up input images and process the pieces

Models trained using supervised learning struggle to classify inputs that differ substantially from most of their training data. A new method helps them recognize such outliers.