What is causal AI? Why this deterministic AI approach is critical to success
https://www.dynatrace.com/news/blog/what-is-causal-ai-deterministic-ai/
In this first post of the series, we’ll discuss causal AI, how it works, and how it compares to other types of artificial intelligence, such as correlation AI. We’ll also discuss why it’s essential for success in the age of generative AI.
Measuring the importance of data quality to causal AI success
https://www.dynatrace.com/news/blog/the-importance-of-data-quality-to-causal-ai/
Traditional analytics and AI systems rely on statistical models to correlate events with possible causes. While this approach can be effective if the model is trained with a large amount of data, even in the best-case scenarios, it amounts to an informed guess, rather than a certainty. That’s where causal AI can help.
Causal AI use cases for modern observability that can transform
https://www.dynatrace.com/news/blog/causal-ai-use-cases-for-modern-observability/
Artificial intelligence adoption is on the rise everywhere—throughout industries, government agencies, and educational institutions of all sizes. And while generative AI was much hyped in 2023, the deterministic nature of causal AI—which determines the precise root cause of an issue—is a key foundational requirement to get reliable decisions and recommendations from generative AI technologies.
