How much processing power do various nations have on hand to drive their AI strategy? An international trade group aims to find out. The Organisation for Economic Co-operation and Development (OECD) is launching an effort to measure the computing capacity available in countries around the world.

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.

With access to a trained model, an attacker can use a reconstruction attack to approximate its training data. A method called InstaHide recently won acclaim for promising to make such examples unrecognizable to human eyes while retaining their utility for training.

The latest pretrained language models have shown a remarkable ability to learn facts. A new study drills down on issues of scale, showing that such models might learn the approximate weight of a dog or cost of an apple, at least to the right order of magnitude.

A new consortium of companies, schools, and research labs is building open tools for next-generation machine learning. MLCommons aims to foster innovation in machine learning by developing new benchmarks, datasets, and best practices.

A computer vision system is helping to keep runners with impaired vision on track.What’s new: A prototype smartphone app developed by Google translates camera images into audio signals.

Controversy erupted over the need for transparency in research into AI for medicine. Google Health introduced a system that purportedly identified breast cancer more accurately than human radiologists.

A typical GAN’s output doesn’t necessarily reflect the data distribution of its training set. Instead, GANs are prone to modeling the majority of the training distribution, sometimes ignoring rare attributes — say, faces that represent minority populations.

Chips specially designed for AI are becoming much faster at training neural networks, judging from recent trials. MLPerf, an organization that’s developing standards for hardware performance in machine learning tasks, released results from its third benchmark competition.

When the chips are down, humans can track critical details without being distracted by irrelevancies. New research helps reinforcement learning models similarly focus on the most important details.

Baidu backed out of a U.S.-led effort to promote ethics in AI, leaving the project without a Chinese presence. The Beijing-based search giant withdrew from the Partnership on AI, a consortium that promotes cooperation on issues like digital privacy and algorithmic bias.

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.

Amazon, Google, and Microsoft are developing machine learning tools for the fossil fuel industry even as they pledge to limit greenhouse gas emissions.

Progress in language models is spawning a new breed of chatbots and, unlike their narrow-domain forebears, they have the gift of gab. Recent research tests the limits of conversational AI.

Few-shot learning seeks to build models that adapt to novel tasks based on small numbers of training examples. This sort of learning typically involves complicated techniques, but researchers achieved state-of-the-art results using a simpler approach.