MIT Schwarzman College of Computing
MIT Schwarzman College of Computing

Second round of seed grants awarded to MIT scholars studying the impact and applications of generative AI

The 16 finalists — representing every school at MIT — will explore generative AI’s impact on privacy, art, drug discovery, aging, and more.

Large language models use a surprisingly simple mechanism to retrieve some stored knowledge

Researchers demonstrate a technique that can be used to probe a model to see what it knows about new subjects.

AI generates high-quality images 30 times faster in a single step

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.

New algorithm unlocks high-resolution insights for computer vision

FeatUp, developed by MIT CSAIL researchers, boosts the resolution of any deep network or visual foundation for computer vision systems.

3 Questions: What you need to know about audio deepfakes

MIT CSAIL postdoc Nauman Dawalatabad explores ethical considerations, challenges in spear-phishing defense, and the optimistic future of AI-created voices across various sectors.

Researchers enhance peripheral vision in AI models

By enabling models to see the world more like humans do, the work could help improve driver safety and shed light on human behavior.

Using generative AI to improve software testing

MIT spinout DataCebo helps companies bolster their datasets by creating synthetic data that mimic the real thing.

Dealing with the limitations of our noisy world

Tamara Broderick uses statistical approaches to understand and quantify the uncertainty that can affect study results.

New AI model could streamline operations in a robotic warehouse

By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.

Generative AI for smart grid modeling

MIT LIDS awarded funding from the Appalachian Regional Commission as part of a multi-state collaborative project to model and test new smart grid technologies for use in rural areas.