When to trust an AI model
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
Research
MIT researchers introduce generative AI for databases
This new tool offers an easier way for people to analyze complex tabular data.
Researchers leverage shadows to model 3D scenes, including objects blocked from view
This technique could lead to safer autonomous vehicles, more efficient AR/VR headsets, or faster warehouse robots.
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.
A smarter way to streamline drug discovery
The SPARROW algorithm automatically identifies the best molecules to test as potential new medicines, given the vast number of factors affecting each choice.
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.
Making climate models relevant for local decision-makers
A new downscaling method leverages machine learning to speed up climate model simulations at finer resolutions, making them usable on local levels.
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.
New computer vision method helps speed up screening of electronic materials
The technique characterizes a material’s electronic properties 85 times faster than conventional methods.