Embedding the Scientific Record on the Web: Towards Automating Scientific Discoveries

Abstract

Future AI systems will be key contributors to science, but this is unlikely to happen unless we reinvent our current publications and embed our scientific records in the Web as structured Web objects. This implies that our scientific papers of the future will be complemented with explicit, structured descriptions of the experiments, software, data, and workflows used to reach new findings. These scientific papers of the future will not only culminate the promise of open science and reproducible research, but also enable the creation of AI systems that can ingest and organize scientific methods and processes, re-run experiments and re-analyze results, and explore their own hypothesis in systematic and unbiased ways. In this talk, I will describe guidelines for writing scientific papers of the future that embed the scientific record on the Web, and our progress on AI systems capable of using them to systematically explore experiments. I will also outline a research agenda with seven key characteristics for creating AI scientists that will exploit the Web to independently make new discoveries [1]. AI scientists have the potential to transform science and the processes of scientific discovery [2, 3].