Companies can get swept up in the idea that they need an advanced deep learning system that can do it all, Pelz-Sharpe said. However, if they want to tackle a focused use case, such as automating a billing process, they don’t need an advanced system. These systems are expensive and use a lot of data, meaning they have a high carbon footprint.
A dedicated system will have been trained on a much smaller amount of data while likely completing a specific use case just as well as a more general system.
“Because it’s highly specialized, that AI has been trained on the most accurate possible data” while maintaining a small data set, Pelz-Sharpe said. A deep learning model, meanwhile, must churn through massive amounts of data to achieve anything.