Imagine you’re working on a project with generative artificial intelligence (AI), creating something like a new piece of art, a story or even a complex data analysis. Everything seems to be going smoothly until you realize that the data you’re using might not be reliable. Suddenly, your confidence in the project starts to waver. This is why trusting your data is absolutely crucial when working with generative AI. This interesting topic came to us from TDWI in their article, “Digital Dialogue | The Role of Trusted Data in Generative.”
When you trust your data, you can be sure that the outputs from your AI models are accurate and meaningful. Reliable data ensures that the AI can learn correctly and produce results that are both useful and trustworthy.
Moreover, trustworthy data helps in maintaining the integrity of your work. In fields like healthcare, finance or any area where decisions based on AI can have significant consequences, the accuracy of data becomes even more critical.
In essence, trusting your data is like having a solid foundation for a building. Without it, everything else is at risk of collapsing. So, always ensure your data is accurate, reliable and up-to-date.
The real challenge is that most organizations have little knowledge on how AI systems make decisions. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
