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.
Accessible medical imaging
In 2020, more than 2.7 million people were diagnosed with cancer in the European Union (EU), whereas 1.3 million people died from cancer. By 2035, the number of cancer cases is expected to increase by 24%, making cancer the leading cause of death in the EU. Currently,...
