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.
Dose assessment: from conceptual model to environmental radioactivity monitoring
Radionuclides are discharged into the environment from a variety of nuclear and radiation facilities, potentially causing harmful effects on human health and the environment. If discharges are likely to result in adverse radiological effects, they must be evaluated in...
