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.
Algorithms
Engineering household robots to have a little common sense
With help from a large language model, MIT engineers enabled robots to self-correct after missteps and carry on with their chores.
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.
Putting AI into the hands of people with problems to solve
Alumni-founded Pienso has developed a user-friendly AI builder so domain experts can build solutions without writing any code.
Using AI to discover stiff and tough microstructures
Innovative AI system from MIT CSAIL melds simulations and physical testing to forge materials with newfound durability and flexibility for diverse engineering uses.
A new way to let AI chatbots converse all day without crashing
Researchers developed a simple yet effective solution for a puzzling problem that can worsen the performance of large language models such as ChatGPT.
How symmetry can come to the aid of machine learning
Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.