The significance of training cost lies in its direct impact on the feasibility and scope of AI projects. High training costs can limit innovation and accessibility, especially for smaller organizations and researchers with limited budgets. The computational demands of training large models, such as deep neural networks, have grown significantly, necessitating advanced hardware and substantial energy consumption. Optimizing training procedures, improving algorithmic efficiency, and developing more energy-efficient hardware are essential strategies to manage and reduce these costs. Additionally, the training cost influences decisions regarding the use of pre-trained models versus training models from scratch, balancing the trade-offs between customization, performance, and expense.
Historical overview: The concept of training cost has become increasingly prominent with the rise of deep learning in the 2010s. As models have grown in complexity and size, the resources needed to train them have surged, bringing attention to the costs involved.
Key contributors: While specific contributors to the concept of training cost are hard to pinpoint due to its broad and foundational nature, organizations and researchers focusing on efficient computing, such as Google DeepMind and OpenAI, have been instrumental in driving innovations that reduce training costs through more efficient algorithms and hardware optimization.