AI Overview and Definitions

What is generative AI?

Artificial intelligence (AI), in its simplest definition, refers to any technology or machine that can perform complex tasks typically associated with human intelligence. These tasks can include problem-solving, planning, reasoning, and decision-making. As the field of AI continues to expand, the terminology and definitions used to describe it also continue to evolve. The three main categories of AI presented in this overview are artificial narrow intelligence, artificial general intelligence, and generative artificial intelligence.

• Artificial Narrow Intelligence (ANI), also called weak AI or narrow AI, refers to systems that are designed to perform specific, often complex, tasks such as analyzing large data sets, making predictions, identifying patterns, or generating text and images. All existing AI systems in use today, including generative AI models, fall under this category. These systems operate within defined limits and do not possess human-like understanding, reasoning, or adaptability across multiple domains.
• Artificial General Intelligence (AGI) refers to the hypothetical ability of a machine to demonstrate broad, human-level intelligence. This includes the capacity to learn new tasks independently, apply knowledge across a range of areas, and adapt to unfamiliar situations. AGI does not currently exist and remains a goal of ongoing research.
• Generative Artificial Intelligence (GenAI) refers to a subset of artificial narrow intelligence that uses algorithms to create or generate new, realistic content such as text, images, audio, and video based on patterns found in training data. Examples of GenAI systems include ChatGPT, Claude, Gemini, and Midjourney. Although these tools can produce original-seeming results, they operate within the boundaries of their training and do not possess general understanding or self-awareness.

GenAI systems have become a topic of significant discussion because they are challenging long-standing practices in education, especially in the assessment of learning and knowledge creation. The rapid growth of these tools has also raised questions about how they may transform professional roles, such as those in journalism, computer programming, and creative industries.

What can and can’t generative AI do?

GenAI systems typically use deep learning techniques and massively large data sets to understand, summarize, generate, and predict new content. Such a system synthesizes seemingly new and realistic outputs based on the data it has been trained on.

GenAI systems cannot think, do, or learn as humans do, even though they may seem that way. Additionally, these systems cannot evaluate the accuracy or quality of the data they have learned from or the output they provide. They may not provide accurate references or understand what they are explaining or the process by which they arrived at the output. They are not content experts, and cannot be relied on as such.

What is a “prompt?”

Generative AI platforms generate in response to user input, or prompts. Prompts can include words, phrases, questions, or keywords that users enter to signal the AI to generate a response based on those factors—the better the prompt, the better the results.

A good prompt has four key elements: Persona, Task, Requirements, and Instructions.

• Persona: Prompts starting with “act as ... ” or “pretend to be ... ” will provide responses similar to that of the role which you provide. Setting a specific role for a given prompt increases the likelihood of more accurate information, when done appropriately.
• Task: Be clear about what you want an answer to, what you want the AI generator to do, find, analyze, etc.
• Requirements: Provide as much information as possible to reduce assumptions the generator may make.
• Instructions: Inform the AI generator how to complete the task.

Example Prompt: You are an expert computer scientist who has been asked to explain the relationship between sorting and searching techniques. Provide a paragraph comparing and contrasting these two techniques. Be concise and use an academic tone.

You can use this as a starting point and utilize follow up directions to refine the result.