Scientists developed a new way to help big computer models learn from many tasks without forgetting what they already know. This approach, called Share, allows the model to use only small amounts of information about each new task and still perform well, making it easier and more efficient for large-scale AI systems to keep learning over time.
Read Full Paper →Scientists created a new way to adapt large language models more efficiently by reusing pre-trained components. This method could have significant benefits, including reducing energy consumption and making it easier for people in low-income or remote areas to access these powerful tools.
Read Paper →We created a new type of artificial neural network that can undo complex transformations or "degradations" applied to images, allowing us to recover the original information. This breakthrough has potential applications in various fields, including image and video processing, where it could enable precise control over generative outputs without requiring extensive retraining.
Read Paper →Scientists created a new system to help robots work together to complete tasks in the real world by improving how they share information with each other. This improvement can make it easier for multiple robots with different skills to work together, leading to more successful and efficient task completion.
Read Paper →Scientists studied how computers learn from images and words by letting them interact with a simulated environment to improve their understanding of the physical world. However, this approach failed to give computers general knowledge about the way things work in different situations, limiting their ability to apply what they learned to new tasks.
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