Generative artificial intelligence (AI) has rapidly evolved into one of the most transformative technologies of our time, driving innovations across industries from art and entertainment to healthcare and finance. At its core, generative AI refers to systems, often based on deep learning models, that can create content—whether text, images, music or even complex simulations—based on the patterns they learn from vast amounts of data. However, as powerful as these systems are, the quality of the data they are trained on plays a pivotal role in determining their effectiveness, accuracy and ethical implications. CIO brought this important topic to us in their article, “Making the gen AI and data connection work.“
The accuracy of a generative AI model is directly tied to the quality of the data it is trained on. High-quality data—data that is clean, well-structured, representative and free from significant biases—enables the model to learn more accurately and produce reliable outputs. Poor data quality, on the other hand, can lead to models that generate misleading or incorrect content, which can have serious implications, especially in critical fields like healthcare, finance or autonomous systems.
By prioritizing data quality, we can develop generative AI systems that are accurate, reliable, fair and ethical, ultimately harnessing the full power of this groundbreaking technology. 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. |
|
Sponsored by Access Innovations, uniquely positioned to help you in your AI journey.
