Using deep learning to image the Earth’s planetary boundary layer
Lincoln Laboratory researchers are using AI to get a better picture of the atmospheric layer closest to Earth's surface. Their techniques could improve weather and drought prediction.
Algorithms
3 Questions: Enhancing last-mile logistics with machine learning
MIT Center for Transportation and Logistics Director Matthias Winkenbach uses AI to make vehicle routing more efficient and adaptable for unexpected events.
A faster, better way to prevent an AI chatbot from giving toxic responses
Researchers create a curious machine-learning model that finds a wider variety of prompts for training a chatbot to avoid hateful or harmful output.
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.
MIT-derived algorithm helps forecast the frequency of extreme weather
The new approach “nudges” existing climate simulations closer to future reality.
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.
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.