A new study from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) demonstrates a novel method for training AI models that significantly reduces computational costs and environmental impact. The research, led by a team of scientists, introduces a technique called ‘early pruning,’ which identifies and removes redundant parameters in neural networks during the initial training phases. This approach allows models to achieve comparable performance to fully trained networks while using only a fraction of the energy and processing power. The findings suggest a pathway toward more sustainable AI development as model sizes continue to grow. The full details of the study are available in the published paper: https://example.com/full-article.