AI Overview and Definitions

What is generative AI?

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

• Artificial narrow intelligence (ANI), which may also be referred to as weak AI or narrow AI, performs specific, but often complicated, tasks such as analyzing large data sets, making predictions, or identifying patterns.
• Artificial general intelligence (AGI) is the ability for technologies/machines to demonstrate broad human-level intelligence. This includes the ability to learn and apply its intelligence to solve problems.
• Generative artificial intelligence (GenAI) is a set of algorithms that can create/generate seemingly new, realistic content—such as text, images, and audio—from a set of training data.

GenAI systems, such as ChatGPT and Midjourney, have quickly become a topic of intense conversation as they are challenging long-standing practices in education, particularly in the assessment of knowledge. Additionally, the proliferation of GenAI systems have called into question the need for certain job roles, such as in the creation of articles in journalism and the need for programmers/coders in computer science.

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.