Causal AI

Nine out of every ten machine learning projects in industry never make it beyond an experimental phase and into production. One key factor that accounts for this alarming statistic is that machine learning algorithms can’t identify “confounders”. We explore why blindness to confounders is a big hindrance for AI, before showing why Causal AI, a new category…

Causal Inference Understanding cause and effect is critical for strategic and organizational decision-making. Current machine learning approaches remain purely based on correlation and prediction, and are limited to analytical insights that only partially address various management decision-making problems.  To generate and evaluate alternative strategic actions in terms of their effect on central business metrics, managers…

Why does explainable AI matter? Explainable AI (“XAI” for short) is AI that humans can understand. To illustrate why explainability matters, take as a simple example an AI system used by a bank to vet mortgage applications. A simple example of an AI use case: an AI model decides which mortgages to approve. There are…

With Causal AI, your Retail organization gains explainable, trustworthy insights and suggested actions that you can take, driving business-critical decisions and delivering tangible results.Unlock new levels of efficiency and effectiveness in your Retail organization, across Marketing Mix Modeling (MMM), pricing, promotions, supply chain, planning & strategy, and more. Do these questions sound familiar? How did…

Introduction Map of causality. The world of causality is broadly split into two main domains: Mainland of causal inference: Causal inference is concerned with understanding the effect of the actions you take. Causal inference provides tools which allow you to isolate and calculate the effect of a change within a system- even if that change never…

“I would rather discover one true cause than gain the kingdom of Persia.” ~ Democritus Until recently, discovering cause-and-effect relationships involved conducting a carefully controlled experiment or else relying on human intuition. Breakthroughs in science and technology have opened up new ways to search for causes. We explain how Causal AI autonomously finds causes, using…

The Causal AI Revolution is Happening Now “Machines’ lack of understanding of causal relations is perhaps the biggest roadblock to giving them human-level intelligence.”Judea Pearl, Turing Award winner and AI pioneer “Causality is very important for the next steps of progress of machine learning.”Yoshua Bengio, Turing Award winner and “Godfather of Deep Learning” “Causal AI is a key enabler…

$15M annual value unlocked through reduced downtime for a leading commodity manufacturer Customer $20bn Commodity Manufacturer Industry Manufacturing Use Case Root Cause Analysis Value Reduced downtime yields $15mn value The Challenge A leading commodity manufacturer came to causaLens to help optimize their Overall Equipment Effectiveness (OEE) at one of their particularly problematic manufacturing plants. They…

London-based pioneer, causaLens, is ready to scale and bring the transformational impact of Causal AI to all organisations causaLens, the London deep tech company delivering the future of AI, has raised a $45m Series A round. causaLens is the pioneer of Causal AI – the only AI technology that quantifies cause-and-effect relationships to reason alongside humans in…

Causal AI is the only technology that can reason and make choices like humans do. It utilizes causality to go beyond narrow machine learning predictions and can be directly integrated into human decision-making. It is the only AI system organizations can trust with their biggest challenges – a revolution in enterprise AI. “To build truly…