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.
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.
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.