MIT researchers teach AI models to interpret charts
The new ChartNet training dataset could improve the accuracy of vision-language models that help analyze business trends or interpret scientific figures.
The new ChartNet training dataset could improve the accuracy of vision-language models that help analyze business trends or interpret scientific figures.
MIT faculty member in electrical engineering and computer science to focus on innovation in engineering education and new pedagogical approaches.
Building on a long-standing MIT–IBM collaboration, the new lab will chart the convergence of AI, algorithms, and quantum computing.
A new method could bring more accurate and efficient AI models to high-stakes applications like health care and finance, even in under-resourced settings.
New dataset of 30,000-plus competition math problems from 47 countries gives AI researchers a harder test — and students worldwide a better training ground.
A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.
The associate professors of EECS and chemistry, respectively, are honored for exceptional contributions to teaching, research, and service at MIT.
Researchers are developing hardware and algorithms to improve collaboration between divers and autonomous underwater vehicles engaged in maritime missions.
Researchers use control theory to shed unnecessary complexity from AI models during training, cutting compute costs without sacrificing performance.
Researchers developed a system that intelligently balances workloads to improve the efficiency of flash storage hardware in a data center.