ABSTRACT
Artificial intelligence, machine learning, and deep learning are terms that many scientists have seen appear and grow in the practice of their discipline, including those who work to understand the Earth and its many systems and processes. The reasons for this AI ‘wave’ are numerous, chief among them the fast progress in predictive ability achieved by deep learning since the early 2010s, which can deal with images, text, as well as numerical and other data types. This versatility also extends to the set of problem domains; AI/ML techniques are almost as widely applicable as computing itself, and wherever there is data to be learnt from, the odds are that some deep learning model is learning from it. We cover key concepts and definitions of these new technologies in Chapter 2. Much like the rest of this book, the coverage is not meant to be comprehensive. Rather, it aims to provide the reader with a sufficient vocabulary to navigate the bestiary of models in current use, and to get a glimpse of the avenues and opportunities that could open up as a result. The main objective of the book is not to answer ‘how to’, but ‘what if’.
