Current 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, and to achieve true artificial intelligence, the development of new science that enables machines to understand cause and effect is required.
Join us in this talk as our CEO- Dr. Darko Matowski presents in a panel with Adrian de Valois Franklin, CEO, Castle Ridge Asset Management and Kamyar Neshvadian, Co-Chief Investment Officer, QSquared Capital
The evolution of Machine Learning:-
- Are all systematic strategies converging?
- Staying ahead of the machine learning / artificial intelligence curve
- Causal AI and its applications