Reasoning skills of large language models are often overestimated
New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.
New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.
This new tool offers an easier way for people to analyze complex tabular data.
Twelve faculty members have been granted tenure in six units across MIT’s School of Engineering.
These models, which can predict a patient’s race, gender, and age, seem to use those traits as shortcuts when making medical diagnoses.
The dedicated teacher and academic leader transformed research in computer architectures, parallel computing, and digital design, enabling faster and more efficient computation.
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.
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.
MIT CSAIL’s frugal deep-learning model infers the hidden physical properties of objects, then adapts to find the most stable grasps for robots in unstructured environments like homes and fulfillment centers.
With generative AI models, researchers combined robotics data from different sources to help robots learn better.
A new approach could streamline virtual training processes or aid clinicians in reviewing diagnostic videos.
“Alchemist” system adjusts the material attributes of specific objects within images to potentially modify video game models to fit different environments, fine-tune VFX, and diversify robotic training.
Fifteen new faculty members join six of the school’s academic departments.
MIT CSAIL researchers enhance robotic precision with sophisticated tactile sensors in the palm and agile fingers, setting the stage for improvements in human-robot interaction and prosthetic technology.