Human teachers can teach more effectively by adjusting their methods in response to student feedback. It turns out that teacher networks can do the same.

A consortium of top AI experts proposed concrete steps to help machine learning engineers secure the public’s trust. Dozens of researchers and technologists recommended actions to counter public skepticism toward artificial intelligence, fueled by issues like data privacy.

An AI-driven alarm system helps rescue patients before infections become fatal. The problem: Machine learning can spot patterns in electronic health data indicating where a patient’s condition is headed that may be too subtle for doctors and nurses to catch.

Faced with a classification task, an important step is to browse the catalog of machine learning architectures to find a good performer. Researchers are exploring ways to do it automatically.

Are your scientist friends intimidated by machine learning? They might be inspired by a primer from one of the world’s premier tech titans. Former Google CEO Eric Schmidt and Cornell PhD candidate Maithra Raghu school scientists in machine learning in a sprawling overview.

Quantum computing has made great strides in recent years, though it still faces significant challenges. If and when it gets here, machine learning may be ready for it.

A simple linear classifier paired with a self-supervised feature extractor outperformed a supervised deep learning model on ImageNet, according to new research.

In small data settings where labels are scarce, semi-supervised learning can train models by using a small number of labeled examples and a larger set of unlabeled examples. A new method outperforms earlier techniques.

Transformer networks have revolutionized natural language processing, but they hog processor cycles and memory. New research demonstrates a more frugal variation.

Geoffrey Hinton, Yoshua Bengio, and Yann LeCun presented their latest thinking about deep learning’s limitations and how to overcome them.

Androids may not dream of electric sheep, but some crack jokes about horses and cows. Meena, a 2.6-billion parameter chatbot developed by Google Brain, showed impressive conversational ability, discussing a variety of topics.

Softmax commonly computes probabilities in a classifier’s output layer. But softmax isn’t always accurate in complex tasks — say, in a natural-language task, when the length of word vectors is much smaller than the number of words in the vocabulary.

Computer vision has potential to spot cancer earlier and more accurately than human experts. A new system surpassed human accuracy in trials, but critics aren’t convinced.

Researchers aiming to increase accuracy in object detection generally enlarge the network, but that approach also boosts computational cost. A novel architecture sets a new state of the art in accuracy while cutting the compute cycles required.

Earlier language models powered by Word2Vec and GloVe embeddings yielded confused chatbots, grammar tools with middle-school reading comprehension, and not-half-bad translations. The latest generation is so good, some people consider it dangerous.