  This paper reviews some of the challenges posed by the huge growth of
experimental data generated by the new generation of large-scale experiments at
UK national facilities at the Rutherford Appleton Laboratory site at Harwell
near Oxford. Such "Big Scientific Data" comes from the Diamond Light Source and
Electron Microscopy Facilities, the ISIS Neutron and Muon Facility, and the
UK's Central Laser Facility. Increasingly, scientists are now needing to use
advanced machine learning and other AI technologies both to automate parts of
the data pipeline and also to help find new scientific discoveries in the
analysis of their data. For commercially important applications, such as object
recognition, natural language processing and automatic translation, deep
learning has made dramatic breakthroughs. Google's DeepMind has now also used
deep learning technology to develop their AlphaFold tool to make predictions
for protein folding. Remarkably, they have been able to achieve some
spectacular results for this specific scientific problem. Can deep learning be
similarly transformative for other scientific problems? After a brief review of
some initial applications of machine learning at the Rutherford Appleton
Laboratory, we focus on challenges and opportunities for AI in advancing
materials science. Finally, we discuss the importance of developing some
realistic machine learning benchmarks using Big Scientific Data coming from a
number of different scientific domains. We conclude with some initial examples
of our "SciML" benchmark suite and of the research challenges these benchmarks
will enable.
