The current state of the art in machine learning relies on past patterns and correlations to make predictions of the future. This approach can work in static environments and for closed problems with fixed rules. However, it does not work for financial time-series and other dynamic systems. In order to make consistently accurate predictions about the future, the development of new science that enables machines to understand cause and effect is required. Adapting ML strategies before, during and after a pandemic requires this development. This expert panel will cover:
- Improving models to decrease the chances of false predictions
- Automation technology being utilised as part of crisis solution
- Opportunities opening up for investment strategies post-Covid
causaLens will present various demonstrations of its solutions to the audience, including practical case-studies of its Causal AI technology in trading and financial markets.