Does AutoML work for diverse tasks?

Over the past decade, machine learning (ML) has grown rapidly in both popularity and complexity. Driven by advances in deep neural networks, ML is now being applied far beyond its traditional domains like computer vision and text processing, with applications in areas as diverse as solving partial differential equations (PDEs), tracking credit card fraud, and predicting medical conditions from gene sequences. However, progress in such areas has often required expert-driven development of complex neural network architectures, expensive hyperparameter tuning, or both. Given that such resource intensive iteration is expensive and inaccessible to most practitioners, AutoML has emerged with an overarching goal of enabling any team of ML developers to deploy ML on arbitrary new tasks. Here we ask about the current status of AutoML, namely: can available AutoML tools quickly and painlessly attain near-expert performance on diverse learning tasks?