Multiple Day

Research on reducing the time required for computationally intensive tasks, such as training large language models or performing complex analyses on massive datasets, is a rapidly growing field. Current efforts focus on optimizing data quality, employing novel algorithms like one-shot federated learning, and leveraging advanced model architectures such as transformers, alongside techniques like weight averaging and active learning to accelerate training and inference. These advancements significantly impact various domains, from accelerating drug discovery (protein structure prediction) and medical image analysis to improving the efficiency and accessibility of artificial intelligence development and deployment. The ultimate goal is to achieve comparable or superior results with drastically reduced computational resources and time.