Study could lead to LLMs that are better at complex reasoning
Researchers developed a way to make large language models more adaptable to challenging tasks like strategic planning or process optimization.
Computer Science and Artificial Intelligence Laboratory (CSAIL)
Using generative AI to help robots jump higher and land safely
MIT CSAIL researchers combined GenAI and a physics simulation engine to refine robot designs. The result: a machine that out-jumped a robot designed by humans.
Researchers present bold ideas for AI at MIT Generative AI Impact Consortium kickoff event
Presentations targeted high-impact intersections of AI and other areas, such as health care, business, and education.
Unpacking the bias of large language models
In a new study, researchers discover the root cause of a type of bias in LLMs, paving the way for more accurate and reliable AI systems.
Envisioning a future where health care tech leaves some behind
The winning essay of the Envisioning the Future of Computing Prize puts health care disparities at the forefront.
Teaching AI models what they don’t know
A team of MIT researchers founded Themis AI to quantify AI model uncertainty and address knowledge gaps.
Teaching AI models the broad strokes to sketch more like humans do
SketchAgent, a drawing system developed by MIT CSAIL researchers, sketches up concepts stroke-by-stroke, teaching language models to visually express concepts on their own and collaborate with humans.
AI learns how vision and sound are connected, without human intervention
This new machine-learning model can match corresponding audio and visual data, which could someday help robots interact in the real world.
Study shows vision-language models can’t handle queries with negation words
Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.
Hybrid AI model crafts smooth, high-quality videos in seconds
The CausVid generative AI tool uses a diffusion model to teach an autoregressive (frame-by-frame) system to rapidly produce stable, high-resolution videos.