A new study from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) demonstrates a method for training AI models that significantly reduces the computational resources required. The technique, called ‘Lazy Learning,’ focuses on training only the most relevant parts of a neural network for a given task, rather than the entire model. This approach can cut training costs by up to 80% while maintaining competitive performance on benchmark tests. The research suggests a pathway toward more efficient and accessible AI development, potentially lowering the barrier for smaller organizations. For the full details, read the complete article at https://technologyreview.com/2024/05/15/1095671/mit-lazy-learning-ai-efficiency.