It is hard to believe that ChatGPT isn’t even a year old yet, when you look at the tidal wave of new apps that have been released in its wake.

After decades where the phrase “artificial intelligence” was sufficient, commentary in the public sphere has already moved to the next level – generative AI. What’s the difference? As Forbes describes it, “Traditional AI systems are primarily used to analyze data and make predictions, while generative AI goes a step further by creating new data similar to its training data.” Federal agencies are exploring ways to harness generative AI, though they are cautious about potential regulatory requirements and AI “hallucinations” (when an AI application fabricates content that doesn’t match reality).

Learning Objectives:

• Understand the important distinctions between traditional AI and generative AI, particularly with regard to your agency’s use cases
• Define the elements that constitute “reliable data,” including such factors as timeliness, error rate, and masking of personally identifiable information
• Delineate the role of domain experts in both applying generative AI and assessing the accuracy and usefulness of its results

Complimentary:    $ 0.00