No matter how sophisticated a predictive model is, it only establishes a correlation between a behavior or event and an outcome. However, that is very different from saying that the outcome happened because of the behavior or event. Correlation doesn't imply causation. There can be correlation but not causation. And while causation implies correlation, its influence may be so minor its irrelevant. Equating them risks creating an incubator for hallucinations and bias,
Causality will enable businesses to do more than create predictions, generate content, identify patterns, and isolate anomalies. They’ll also be able to play out countless scenarios to understand the consequences of various actions, explain the causal drivers of their business, and analytically problem-solve. They’ll know WHAT to do, HOW to do it, and WHY certain actions are better than others to prescriptively shape future outcomes.
Simply put, humans are causal by nature, so AI must also be causal by nature. LLMs are similar to the limbic brain, which drives instinctive actions based on memories, making them good at automating tasks. Casual AI plays the role of the cerebral cortex, which encodes explicit memories into tacit know-how, and the neocortex, which drives higher-order reasoning such as problem-solving, planning, and decision-making.
While GenAI has sparked excitement, Causal AI might offer much greater potential by being able to unravel the intricate web of cause-and-effect relationships across a business.
Instana can now use causal AI to identify root causes of IT faults and showcase how and why certain entities were identified as probable cause of the identified problematic entities.
Using the ML framework, we can test and identify the various aspects of promotional artwork to give Netflix members a better and more personalized experience..
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