  The boundless possibility of neural networks which can be used to solve a
problem -- each with different performance -- leads to a situation where a Deep
Learning expert is required to identify the best neural network. This goes
against the hope of removing the need for experts. Neural Architecture Search
(NAS) offers a solution to this by automatically identifying the best
architecture. However, to date, NAS work has focused on a small set of datasets
which we argue are not representative of real-world problems. We introduce
eight new datasets created for a series of NAS Challenges: AddNIST, Language,
MultNIST, CIFARTile, Gutenberg, Isabella, GeoClassing, and Chesseract. These
datasets and challenges are developed to direct attention to issues in NAS
development and to encourage authors to consider how their models will perform
on datasets unknown to them at development time. We present experimentation
using standard Deep Learning methods as well as the best results from challenge
participants.
