Gradient descent is used in machine learning, which is a field in AI, to train models (eg. neural networks) on data. You get some data and use gradient descent

Gradient descent is used in machine learning, which is a field in AI, to train models (eg. neural networks) on data. You get some data and use gradient descent to pick the parameters (eg. neural network weights) to minimise the error on that training data. You can then use your trained model by putting other data into it and getting its outputs. The researchers in this article didn't do that. They used gradient descent to choose from a set of experiments. The choice of experiment was the end result and the direct output of the optimisation. Nothing was "learned" or "trained". Gradient descent and other optimisation tools are used in machine learning, but long predate machine learning and are used in many other fields. Taking "AI" to include "anything that uses gradient descent" would just render an already heavily abused term almost entirely meaningless.