Turing Lecture: Machine learning and big scientific data benchmarks

This talk will review the challenges posed by the growth of experimental data, created by the new generation of large-scale experiments at UK national facilities such as the Diamond Synchrotron at the Rutherford Appleton Laboratory site at Harwell, near Oxford. Increasingly, scientists now need to use sophisticated machine learning and other AI technologies to automate parts of the data pipeline and to find new scientific discoveries in the deluge of experimental data. In industry, Deep Learning is now transforming many areas of computing and researchers are now exploring their use in analyzing their ‘Big Scientific Data’. The talk will include a discussion about the creation of a set of Big Scientific Data Machine Learning ‘benchmarks’ for exploring the use of these technologies in the analysis of experimental research data. Such benchmarks could also be important in providing new research insights into the robustness and transparency of such these algorithms.