From Model-centric to Data-centric Artificial Intelligence
Two basic components of all AI systems are Data and Model, both go hand in hand in producing desired results. In this article we talk about how the AI community has been biased towards putting more effort in the model, and see how it is not always the best approach.
We all know that machine learning is an iterative p rocess, because machine learning is largely an empirical science. You do not jump to the final solution by thinking about the problem, because you can no easily articulate what the solution should look like. Hence you empirically move towards better solutions. When you are in this iterative process, there are two major directions at you you disposal.
This involves designing empirical tests around the model to improve the performance. This consists of finding the right model architecture and training procedure among a huge space of possibilities.
This consists of systematically changing/enhancing the datasets to improve the accuracy of your AI system. This is usually overlooked and data collection is treated as a one off task.
