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An explanation of causal AI

In the 1950s, the creation of modern-day computers led scientists to question the existence of machine intelligence.

AI is an advanced mechanism of technology that aims to imitate the intelligence of the human mind. The term artificial intelligence was introduced in 1955.

AI has developed into forms such as generative AI, natural language processing and speech recognition. Today, AI can not only provide answers to queries, but also provide explanations for their conclusions, a process known as causal AI.

This video provides insight into what causal AI is along with its benefits in society.

Transcript - An explanation of causal AI

You know the phrase, correlation does not equal causation? That's what causal AI's all about.

Causal AI is a type of AI that understands the cause and effect of relationships in data. This stands in contrast to other types of AI -- like LLMs and generative AI -- which simply recognize correlations in data, not necessarily the underlying cause of relationships.

Causal AI intends to provide deeper explainability and reduce bias, using causal inference. This is notably important as AI has come under scrutiny for its inability to explain how it reaches its conclusions and generates content.

Read more about the importance of AI transparency and explainability in the link above, or in the description below. And be sure to subscribe for more videos on all things business tech.

You may have heard the claim that you can make statistics say anything. That can be applied to AI pattern recognition. Say, for example, you want to know what caused a system failure. A traditional AI model may flag an event in the business that happened at the same time as a factor. But that's really just correlation -- the two events may have no impact on each other.

Causal AI relies on fault tree analysis, a type of root cause analysis, to model causal relationships -- aka, get to the source of the problem or event.

It starts with the system failure event, and in a top-down approach, scrutinizes preceding events to find the root causes. The fault tree maps the relationship between component failures, and overall system failures.

The benefits of causal AI, when compared to other forms of AI, include:

  • Deeper explainability.
  • Reduced bias.
  • Data scientist testing before implementing in the real world.
  • Improved optimization.

Causal AI has real-world applications in a variety of industries:

  • In sales, it helps to understand the reason behind customer churn or transaction declines.
  • In healthcare, learning the effects of treatments.
  • In finance, analyzing the root cause of investment risk.
  • In manufacturing, finding the cause of production line failures
  • And in government, quantifying the effects of new policies through simulations.

Causal AI does come with some inherent risks, including AI hallucinations and inability to predict causal relationships. But experts anticipate causal AI adoption with continue to grow as a key technology in AI development.

What have you used causal AI for? Share your thoughts in the comments, and remember to like and subscribe, too.

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