A machine-learning method imagines what a sentence visually looks like, to situate and ground its semantics in the real world, improving translation, like humans can.

Rapid and accurate analytical test method enhances the production of high-quality cell therapy products.

A machine-learning model can identify the action in a video clip and label it, without the help of humans.

A new neural network approach captures the characteristics of a physical system’s dynamic motion from video, regardless of rendering configuration or image differences.

A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model’s behavior.

An efficient machine-learning method uses chemical knowledge to create a learnable grammar with production rules to build synthesizable monomers and polymers.

A new “common-sense” approach to computer vision enables artificial intelligence that interprets scenes more accurately than other systems do.

MIT-IBM Watson AI Lab researchers aim to design concrete mixtures that use AI to shrink environmental footprint and cost, while recycling byproducts and increasing performance.

A new AI-powered, virtual platform uses real-world physics to simulate a rich and interactive audio-visual environment, enabling human and robotic learning, training, and experimental studies.

Reducing the complexity of a powerful machine-learning model may help level the playing field for automatic speech-recognition around the world.