The pace of research is nowadays extremely intensive, with datasets and publications being published at an unprecedented rate. In this context data science, artificial intelligence, machine learning and big data analytics are providing researchers with new automatic techniques which not only help them to manage this flow of information but are also able to identify automatically interesting patterns and insights in this vast sea of information. However, the emergence of mechanised scientific discovery is likely to dramatically change the way we do science, thus introducing and amplifying serious societal implications on the role of researchers themselves, which need to be analysed thoroughly.
Mannocci, A., Salatino, A., Osborne, F., Motta, E. (2017). 2100 AI: Reflections on the mechanisation of scientific discovery. In Proceedings of the Re-coding Black Mirror 2017 Workshop, co-located with 16th International Semantic Web Conference (ISWC 2017), Vienna, Austria, October 22, 2017. (pp.1-6). CEUR-WS.
2100 AI: Reflections on the mechanisation of scientific discovery
Osborne, F;
2017
Abstract
The pace of research is nowadays extremely intensive, with datasets and publications being published at an unprecedented rate. In this context data science, artificial intelligence, machine learning and big data analytics are providing researchers with new automatic techniques which not only help them to manage this flow of information but are also able to identify automatically interesting patterns and insights in this vast sea of information. However, the emergence of mechanised scientific discovery is likely to dramatically change the way we do science, thus introducing and amplifying serious societal implications on the role of researchers themselves, which need to be analysed thoroughly.
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