Beyond Correlations: Why We Need Causal AI

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 presentation, Darko Matovski, causaLens CEO, sets out how an understanding true causal drivers enables Causal AI to navigate complex and dynamic systems, being able to perform as its environment changes. In addition, Causal AI is capable of ‘imagining’ scenarios it has not encountered in the past, allowing it to simulate counterfactual worlds to learn from, instead of relying solely on ‘training’ data. Perhaps most interestingly, understanding causality gives an AI the ability to interact with humans more deeply, being able to explain its ‘thought process’ and integrate human expert knowledge