Causal AI, the new frontier of Artificial Intelligence

AI appeared for the first time as a scientific discipline in the mid-50s. Since then, Artificial Intelligence has evolved, specifically in three substantial aspects: the machine's ability to calculate, the implementation of new models and the extension of the application fields. An example of this development is Causal AI. We need the help of Judea Pearl,winner of the Turing Prize, to get better into this topic and to better understand what we are talking about. In “the book of Why” he states that to build truly intelligent machines, they must be taught a cause and an effect.
This statement is part of a larger reasoning that tends to explain how and why different types of artificial intelligence have been developed in the last decade, such as machine learning and data modeling. But if these types of approaches have always focused on predictive modeling, over time it has become increasingly important to identify and understand the causality behind phenomena and how these can affect the future.
Today we have at our disposal a new revolutionary technology: Causal AI.
It represents the science that identifies the cause-effect relationships, which applied in the corporate context means to associate internal variables of an enterprise to context data. A new artificial intelligence system that thus uses causality to go beyond machine learning predictions and that can be directly integrated into human decision-making.

According to recent research by Oracle, 70% of business leaders would prefer artificial intelligence to make their business decisions, whereas an organization that uses technology to support decision-making is more reliable and more successful; however, they do not believe that they have the right tools to do so. This situation is having a negative impact on their ability to make timely decisions, with 85% of business leaders suffering from decision-making stress, and often regretting, feeling guilty or questioning, the validity of a decision taken in the last year.
The Causal AI aims to help and support these managers to make effective decision making, allowing them to understand data determined by the exact cause of an event. Thanks to this approach, business leaders have a tool at their disposal that allows them to visualize the results in a more intuitive way with a series of explanations, but above all with a cause analysis.
Going even further into the details of such an innovative technology, it is enough to think that at the heart of the success of Causal AI is the causal inference. What is it about? It’s about a methodology used by researchers to determine both the independent effect of an event but also to draw the cause-and-effect conclusions that derive from the data. Causal models allow, therefore, to simulate events by calculating hypothetical results for the evaluation of different types of scenarios.
This approach allows companies to respond efficiently to the "What - If" question, producing a significant competitive advantage and a saving of time and resources, which would otherwise be used in numerous and expensive physical tests. For all these reasons, Causal Artificial Intelligence is the basis of the engine of the Vedrai Suite.