Precision home robots learn with real-to-sim-to-real
CSAIL researchers introduce a novel approach allowing robots to be trained in simulations of scanned home environments, paving the way for customized household automation accessible to anyone.
Computer Science and Artificial Intelligence Laboratory (CSAIL)
Study across multiple brain regions discerns Alzheimer’s vulnerability and resilience factors
Genomics and lab studies reveal numerous findings, including a key role for Reelin amid neuronal vulnerability, and for choline and antioxidants in sustaining cognition.
MIT researchers advance automated interpretability in AI models
MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.
Creating and verifying stable AI-controlled systems in a rigorous and flexible way
Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.
AI method radically speeds predictions of materials’ thermal properties
The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.
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.
MIT researchers introduce generative AI for databases
This new tool offers an easier way for people to analyze complex tabular data.
The tenured engineers of 2024
Twelve faculty members have been granted tenure in six units across MIT’s School of Engineering.
Study reveals why AI models that analyze medical images can be biased
These models, which can predict a patient’s race, gender, and age, seem to use those traits as shortcuts when making medical diagnoses.
Arvind, longtime MIT professor and prolific computer scientist, dies at 77
The dedicated teacher and academic leader transformed research in computer architectures, parallel computing, and digital design, enabling faster and more efficient computation.
Understanding the visual knowledge of language models
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.
Technique improves the reasoning capabilities of large language models
Combining natural language and programming, the method enables LLMs to solve numerical, analytical, and language-based tasks transparently.
Researchers use large language models to help robots navigate
The method uses language-based inputs instead of costly visual data to direct a robot through a multistep navigation task.
New algorithm discovers language just by watching videos
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.
Helping robots grasp the unpredictable
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.