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 for dynamic systems such as financial time-series. 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. 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 knowledge.

At causaLens we are building the world’s largest Causal AI research lab to accelerate progress in this powerful science. This talk will present the current challenges causal AI must overcome to unleash its full potential, as well as the latest progress made towards achieving those goals. Finally, some examples of the positive impact this science is already having in the field will be shared.