In a paper scheduled to be presented at the upcoming International Conference on Learning Representations, Amazon researchers propose an AI approach that greatly improves performance on certain meta-learning tasks (i.e., tasks that involve both accomplishing related goals and learning how to learn to perform them). They say it can be adapted to new tasks with only a handful of labeled training examples, meaning a large corporation could use it to, for example, extract charts and captions from scanned paperwork. In conventional machine learning, a model trains on a set of labeled data (a support set) and learns to correlate features with the labels. It's then fed a separate set of test data (a query set) and evaluated based on how well it predicts that set's labels. By contrast, during meta learning, an AI model learns to perform tasks with their own sets of training data and test data and the model sees both. In this way, the AI learns how particular ways of responding to the training data affect performance on the test data.