In the popular view of traditional science, scientists are in the lab hovering over their experiments, micromanaging every little detail. For example, they may iteratively test a wide variety of material compositions, synthesis and processing protocols, and environmental conditions to see how these parameters influence material properties. In each iteration, they analyze the collected data, looking for patterns and relying on their scientific knowledge and intuition to select useful follow-on measurements.
This manual approach consumes limited instrument time and the attention of human experts who could otherwise focus on the bigger picture. Manual experiments may also be inefficient, especially when there is a large set of parameters to explore, and are subject to human bias—for instance, in deciding when one has collected enough data and can stop an experiment. The conventional way of doing science cannot scale to handle the enormous complexity of future scientific challenges. Advances in scientific instruments and data analysis capabilities at experimental facilities continue to enable more rapid measurements. While these advances can help scientists tackle complex experimental problems, they also exacerbate the human bottleneck; no human can keep up with modern experimental tools!