An AI system can be effectively tested after the machine has been completely trained. The most common way of testing is to actually divide the data that is available into three parts: the training set, the development set, and the testing set. The machine uses the training and development sets to understand the data and learn using the algorithms specified. The testing set is the data that the algorithm/neural network has never seen before. So, this is used as the verification data to understand if the AI entity has the ability to perform the task at hand on an equal performance scale as when working on the training data. If so, it means that the AI system is successful in learning. If not, it means that the Artificial Intelligence system needs to be either retrained or tweaked to achieve the desired result.
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