Author: Alexey Boyarsky
In today's natural sciences, the problem of complexity concerns not only the experimental data, but also the generated scientific knowledge: complex scientific concepts; abstract notions; their relation to phenomena and data putting the experiments into the proper context. The growing level of complexity of scientific knowledge requires automated support also at this level. We need tools that support the scientific workflow at essentially every step, starting from data collection and data analysis and ending with the most advanced reasoning and inference steps. This has fundamental implications on how we perceive the role of technical systems and in particular information processing infrastructures for scientific work: They are no longer a subordinate instrument that facilitates (or makes more miserable) daily work of highly gifted individuals, but become an essential tool and enabler for performing scientific progress, and eventually might be the instrument within which scientific discoveries are made, represented and brought to use. In this talk we present our vision of such a system.