This novel circuit architecture cancels out unwanted signals at the earliest opportunity.

New findings could help engineers design materials for light and heat management.

LLMs trained primarily on text can generate complex visual concepts through code with self-correction. Researchers used these illustrations to train an image-free computer vision system to recognize real photos.

Three innovations by an MIT-based team enable high-resolution, high-throughput imaging of human brain tissue at a full range of scales, and mapping connectivity of neurons at single-cell resolution.

The SPARROW algorithm automatically identifies the best molecules to test as potential new medicines, given the vast number of factors affecting each choice.

Combining natural language and programming, the method enables LLMs to solve numerical, analytical, and language-based tasks transparently.

The method uses language-based inputs instead of costly visual data to direct a robot through a multistep navigation task.

DenseAV, developed at MIT, learns to parse and understand the meaning of language just by watching videos of people talking, with potential applications in multimedia search, language learning, and robotics.

The technique characterizes a material’s electronic properties 85 times faster than conventional methods.

Smaller than a coin, this optical device could enable rapid prototyping on the go.