In this webinar Darko Matovski, PhD, CEO and co-founder of causaLens will discuss Causal AI; a nascent science which aims to enable machines to understand cause and effect. An LSE graduate now working for causaLens will also discuss the process of transitioning from academia to the AI industry and share their experience.
The current state-of-the-art in machine learning can work for static environments and closed loop problems with fixed rules, but it often fails in other situations. The likelihood of failure is especially pronounced for highly dynamic, low signal-to-noise environments, which are so common for time-series data.
An understanding of the true causal drivers should enable causal AI to navigate and draw inferences and make forecasts for complex, dynamic and metamorphosing systems. Further, it should be capable of ‘imagining’ scenarios not encountered in the past so allowing it to simulate counterfactual worlds to learn from, instead of relying solely on ‘training’ data.
Understanding causality should give AI the ability to interact with humans more profoundly, being able to explain its ‘thought process’ and incorporate human domain knowledge. The academic and industry AI community is racing to advance the science of causality and apply its power to solve a wide range of problems.
This will be an interactive session. Questions and audience participation are encouraged throughout!
This event is being run in collaboration with LSE Data Science Society.