Generative artificial intelligence (AI) has quickly become one of the most game-changing technologies around, sparking innovations in everything from art and entertainment to healthcare and finance. Basically, generative AI involves systems, usually based on deep learning models, that can create content like text, images, music or even complex simulations by learning patterns from tons of data. But no matter how powerful these systems are, the quality of the data they’re trained on is crucial for their effectiveness, accuracy and ethical impact. 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 depends a lot on the quality of its training data. High-quality data that’s clean, well-structured, representative and free from major biases helps the model learn more accurately and produce reliable results. On the flip side, poor data quality can lead to models that generate misleading or incorrect content, which can be a big deal in critical areas like healthcare, finance or autonomous systems.
Focusing on data quality lets us build generative AI systems that are accurate, reliable, fair and ethical, fully tapping into the potential of this amazing technology. The tricky part is that many organizations don’t really understand how AI systems make decisions. Explainable AI helps users understand and trust the results and outputs created by machine learning algorithms.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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