Within the general excitement about artificial intelligence (AI), there has been special interest in the technology’s application to discovery of scientific knowledge. Like AI itself, this subfield has a long history and many successes, but also outstanding challenges. In this talk, I will focus on two problems that have received considerable attention: discovery of numeric equations and construction of qualitative process models. In each case, I will define the computational task, review basic approaches, and report successes that led to scientific insights. After this, I will turn to a third paradigm – inductive process modeling – that moves beyond earlier efforts to generate quantitative explanations of scientific data in terms of domain knowledge. I will present multiple approaches to this problem, present encouraging results, and discuss open issues that merit further attention from the AI community.
Methods for semi-automated hypothesis generation from scientific literature: an open science approach
The rapid growth of scientific publications makes it difficult to manually review and keep up to date with new research findings. Literature-based discovery (LBD) is a field of artificial intelligence at the intersection of natural language processing and machine...
