Abstract

The first successful applications of machine intelligence were based on expert systems constructed using rules elicited from human experts. Limitations in the applicability of this approach helped drive the second generation of machine intelligence methods, as typified by neural networks, which can be characterised as black-box statistical models fitted to large data sets. In this talk I will describe a new paradigm for machine intelligence which has emerged over the last five years, and which allows strong prior knowledge from domain experts to be combined with machine learning techniques to enable a new generation of large-scale applications. The talk will be illustrated with tutorial examples as well as real-world case studies.