Open Source Dynamic Causal Modelling of COVID-19
Presented by Will Jones, Embecosm
Dynamic Causal Modelling is a state of the art AI modelling technique that reverse engineers an observed time series into a set of causal components and relationships. DCM has historically been developed for and applied to problems in neuroscience and brain imaging, but the technique is a very general one that has more recently, for example, been applied to modelling the COVID-19 pandemic with excellent results.
The standard implementation of DCM is open source, but it’s current implementation is in MATLAB, a proprietary tool. In this talk I discuss my work on creating a standalone implementation of one particularly application of Dynamic Causal Modelling (that of COVID-19) compatible with the open source GNU Octave language.
Dr Will Jones is head of AI and Machine Learning for Embecosm. He recently completed his PhD at the University of Kent, which can be simply summarized as attempting to create a rigorous mathematical framework for the definition of artificial consciousness.