Harold Robert Meyer and The ADD Resource Center 04/23/2025
“Artificial intelligence is not merely a technological advancement; it is the ultimate force multiplier for human potential, broadening what is achievable for individuals, enhancing national competitiveness, and equipping humanity with powerful tools to tackle our greatest global challenges.” Harold Meyer
| Term | Definition |
|---|---|
| Prompt | The text or other input you provide to a generative AI model to tell it what you want it to create (e.g., “a cat wearing a hat,” “write a short story about a robot”). |
| Hallucination | When a generative AI model produces information that is false, nonsensical, or not based on the training data or the provided prompt. It might present this incorrect information confidently. |
| AI-generated content | Any text, images, audio, video, or other media that has been created by artificial intelligence models. |
| Style transfer | A technique where the artistic style of one image is applied to the content of another image (e.g., making a photo look like a Van Gogh painting). |
| Deepfake | A manipulated video or audio recording that realistically depicts someone saying or doing something they did not actually say or do. Often created using generative AI. |
| Latent space | Think of this as a hidden, abstract space where the AI stores compressed representations of the data it has learned. By navigating this space, the AI can generate new variations. |
| Training data | The large amounts of real-world data (text, images, etc.) that are used to teach a generative AI model how to understand patterns and create new content. |
| Bias | When the training data used to create an AI model contains prejudices or imbalances, the model can learn and perpetuate these biases in its generated output. |
| Fine-tuning | The process of taking a pre-trained AI model and further training it on a smaller, more specific dataset to make it better at a particular task or style. |
| Model | In the context of AI, a model is the learned representation of the data that the AI uses to make predictions or generate new content. |
| Parameters | The adjustable variables within an AI model that are learned from the training data. Models with more parameters are often more complex and capable. |
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