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April 10, 2024
A new technique can more effectively perform a safety check on an AI chatbot. MIT researchers enabled their model to prompt a chatbot to generate toxic responses, which are used to prevent the chatbot from giving hateful or harmful answers when deployed.
March 24, 2024
Boston MA (SPX) Mar 21, 2024 - Imagine yourself glancing at a busy street for a few moments, then trying to sketch the scene you saw from memory. Most people could draw the rough positions of the major objects like cars, people,
May 31, 2023
Saliency cards, developed at MIT and IBM, seek to describe a machine-learning modelโs behavior and to help users quickly compare methods so they can choose the right one for their task.
May 31, 2023
Miragenews.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from miragenews.com Daily Mail and Mail on Sunday newspapers.
May 10, 2023
Unlike humans, computer vision models dont typically exhibit perceptual straightness, so they learn to represent visual information in a highly unpredictable way
May 10, 2023
Get latest articles and stories on Science at LatestLY. MIT researchers have discovered that a specific training method can help computer vision models learn more perceptually straight representations, like humans do. Training involves showing a machine-learning model millions of examples so it can learn a task. Science News | Specific Training Can Help Computer Vision Models Learn Like Humans: Study.
May 9, 2023
MIT researchers discovered that a specific training technique can enable certain types of computer vision models to learn more stable, predictable visual representations, which are more similar to those humans learn using a biological property known as perceptual straightening.
March 15, 2023
CAMBRIDGE, MA — March 14, 2023 — Researchers from MIT and other institutions report that a “hash function” — a core database [...]
November 29, 2022
A machine-learning technique called SALIENT addresses key bottlenecks in computation with graph neural networks by optimizing usage of the hardware, particularly GPUs. This upgrade significantly reduces training and inference time on extensive datasets to keep pace with fast-moving and growing data in finance, social networks, and fraud detection in cryptocurrency.