How AI is improving simulations with smarter sampling techniques
MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.
MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.
David Singer, head of the MIT Department of Political Science, discusses the Strengthening Democracy Initiative, focused on the rigorous study of elections, public opinion, and political participation.
By enabling users to chat with an older version of themselves, Future You is aimed at reducing anxiety and guiding young people to make better choices.
New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.
A new method called Clio enables robots to quickly map a scene and identify the items they need to complete a given set of tasks.
The program will invite students to investigate new vistas at the intersection of music, computing, and technology.
The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.
The innovations map the ocean floor and the brain, prevent heat stroke and cognitive injury, expand AI processing and quantum system capabilities, and introduce new fabrication approaches.
Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.
MIT researchers speed up a novel AI-based estimator for medication manufacturing by 60 times.
The digital adviser helps users swiftly navigate the 24-step “Disciplined Entrepreneurship” process.
By analyzing X-ray crystallography data, the model could help researchers develop new materials for many applications, including batteries and magnets.
Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.
“Co-LLM” algorithm helps a general-purpose AI model collaborate with an expert large language model by combining the best parts of both answers, leading to more factual responses.
“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.