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 but fails in every other situation. In order to make consistently accurate predictions about the future and to achieve true artificial intelligence, the development of new science is required. A science that enables machines to understand cause and effect.
This is the power of Causal AI.
In this interview with Alexander Fleiss, Chairman and Co-Founder of Rebellion Research Darko Matovski, causaLens CEO, sets out the six key areas in which causal AI outperforms correlation-based machine learning.