Beyond correlations: Why do we need causal AI?

Dr Darko Matovski, CEO of causaLens, will explain causal AI; he’ll discuss why it’s important machines understand cause and effect to make more accurate predictions. This approach can work in static environments and for closed problems with fixed rules but fails in every other situation. Darko suggests that 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.

Darko suggests that 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.



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