Current state-of-the-art machine learning has severe limitations in dynamic environments and fails to unlock the true potential of AI for businesses.
Causal AI is a new category of intelligent machines that understand cause and effect ― a major step towards true AI.
Observational data only
Understands business context
Predictions, interventions & counterfactuals
Current ML technology fails when applied to dynamic, complex systems. It produces static models that overfit to yesterday’s world. The models are data-hungry and unintelligible to humans.
causaLens has the solution. Models built with causal AI cut through the noise to identify enduring causal signals. They rapidly adjust to changing conditions, maintaining performance even as the world changes. And they’re fully transparent to humans.
Intuitively incorporate domain knowledge and expertise into the modelling process.
Gain insight into the effects of actions and design optimal interventions.
Imagine counterfactual worlds to explain the past and navigate an uncertain future.
We believe that teaching machines to understand cause and effect will unlock the true power that AI has to offer, transforming business efficiency and providing unimaginable benefits to the world.
This is a formidable task. As such, we are committed to building the world’s largest Causal AI research lab to succeed in this effort.