Using ideas from game theory to improve the reliability of language models
A new “consensus game,” developed by MIT CSAIL researchers, elevates AI’s text comprehension and generation skills.
A new “consensus game,” developed by MIT CSAIL researchers, elevates AI’s text comprehension and generation skills.
For the first time, researchers use a combination of MEG and fMRI to map the spatio-temporal human brain dynamics of a visual image being recognized.
Novel method makes tools like Stable Diffusion and DALL-E-3 faster by simplifying the image-generating process to a single step while maintaining or enhancing image quality.
FeatUp, developed by MIT CSAIL researchers, boosts the resolution of any deep network or visual foundation for computer vision systems.
MIT CSAIL postdoc Nauman Dawalatabad explores ethical considerations, challenges in spear-phishing defense, and the optimistic future of AI-created voices across various sectors.
Innovative AI system from MIT CSAIL melds simulations and physical testing to forge materials with newfound durability and flexibility for diverse engineering uses.
MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.
MIT researchers introduce a method that uses artificial intelligence to automate the explanation of complex neural networks.
“Minimum viewing time” benchmark gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification.
MIT CSAIL researchers innovate with synthetic imagery to train AI, paving the way for more efficient and bias-reduced machine learning.