How to build robots that respond to novel situations? When prior experience is limited, enabling a model to describe its uncertainty can enable it to explore more avenues to success.

Watson set a high bar for language understanding in 2011, when it famously whipped human competitors in the televised trivia game show Jeopardy! IBM’s special-purpose AI required around $1 billion. Research suggests that today’s best language models can accomplish similar tasks right off the shelf.

Deep reinforcement learning has given machines apparent hegemony in vintage Atari games, but their scores have been hard to compare — with one another or with human performance — because there are no rules governing what machines can and can’t do to win. Researchers aim to change that.

Creating a virtual representation of a scene using traditional polygons and texture maps involves several complex operations, and even neural-network approaches have required manual preprocessing. Researchers propose a new deep-learning pipeline that visualizes scenes with far less fuss.

Deep learning models can be unwieldy and often impractical to run on smaller devices without major modification. Researchers at Facebook AI Research found a way to compress neural networks with minimal sacrifice in accuracy.

Neural networks are good at making predictions, but they’re not so good at estimating how certain they are. If the training data set is small and many sets of model parameters fit the data well, for instance, the network may not realize this explicitly, leading to overly confident predictions.

Researchers typically test deep reinforcement learning algorithms on games from Space Invaders to StarCraft. The Google Brain team in Zurich adds another option: football, also known as soccer.

Less than a month after XLNet overtook BERT, the pole position in natural language understanding changed hands again. RoBERTa is an improved BERT pretraining recipe that beats its forbear, becoming the new state-of-the-art language model — for the moment.

Image-to-image translation, in which stylistic features from one image are imposed on the content of another to create a new picture, traditionally has been limited to translating either shapes or textures. A new network translates both.