causaLens, a deep-tech company which recently launched the world’s first Causal AI platform, is set to build the world’s largest Causal AI research lab.
Causal AI is the next step in AI as it understands ’cause and effect’ compared to current ML techniques which are based on historical correlations.
causaLens is also debuting its first research paper “Data Generating Process to Evaluate Causal Discovery Techniques for Time Series Data” showcasing a benchmarking system for causal discovery techniques – a key step towards helping the community to harmonise the evaluation of causal techniques.
“We are excited to announce that Dr Hana Chockler, Associate Professor at King’s College London with more than two decades of research experience in causality, joins us as Principal Investigator to lead one of our research teams. Dr Chockler brings a powerful vision of advancing towards universal causal and explanation mechanisms which will contribute to further scientific development and deliver immense value for our customers through product innovation,” explains causaLens CEO Darko Matovski.
“Over the last four years we have observed that enterprises across Finance, Technology, Energy, Telecommunications and other industries are trying to optimise their businesses by acquiring machine learning capabilities. However, machine learning has severe limitations when applied to the real world. It assumes that historical patterns will hold in the future, which often is not the case in our increasingly dynamic world. At the same time, there is a lack of trust. Businesses struggle to let machines they don’t fully understand make decisions for them,” explains causaLens CTO Maksim Sipos.
“Causality has the potential to solve these problems with truly robust, explainable and high-performing models. We have observed that the academic research in causal inference and causality, is not applicable in realistic settings, and makes unrealistic assumptions. This led us to build the largest Causal AI lab in the world.”
“Even the most sophisticated enterprises with large teams made of the best of breed data science talent have struggled to navigate the changing environment in 2020. We are looking forward to continuing to make advancements in Causal AI, engineering systems that are robust as the world changes and that can recommend business actions to directly optimise KPIs,” Matovski comments.
