Student Guide to Generative AI: Home

Generative AI, often called GenAI, is a type of artificial intelligence that can create output based on training data. Output may include text, audio, image, or video output. ChatGPT is probably the most well-known of these Generative AI tools, but there are many others rapidly being developed.

The tools can create increasingly sophisticated output and are expected to rapidly impact fields such as business, journalism, art, and education. Experts are both excited about the possibilities this new technology creates and concerned about some of the ethical questions surrounding its use.

For a quick intro to how Generative AI technology works, we recommend the short video "What is Generative AI?" from Linked in Learning (UNC Onyen log-in required.)

For a deeper dive into the subject, the explainer video "Generative AI in a Nutshell" by Henrik Kniberg is a good option.

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Defining Generative AI
To understand generative artificial intelligence (GenAI), we first need to understand how the technology builds from each of the AI subcategories listed below.
Artificial Intelligence, the broadest category, is the theory and methods to build machines that think and act like humans (shows an illustration of a brain with brightly colored areas). Programmers teach AI exactly how to solve specific problems by providing precise instructions and steps.
Machine Learning, a subcategory of Artificial Intelligence, is the ability for computers to learn from experience or data without human programming (shows an illustration of a laptop with a lightbulb on the screen).
Deep Learning, a subcategory of Machine Learning, mimics the human brain using artificial neural networks such as transformers to allow computers to perform complex tasks (shows an illustration of a network with notes and connections).
Generative AI, a subcategory of Generative AI, generates new text, audio, images, video or code based on content it has been pre-trained on (shows decorative icons beside ChatGPT, Midjourney, and Bard).

This image is from (AI for Education).

These definitions were compiled vebatim from multiple sources (see citations for more information).

1. Algorithm: An algorithm is a set of instructions that tells a computer how to solve a problem.
   Algorithms are used in a wide variety of AI applications, including natural language processing, machine learning, and computer vision. ¹

2. Artificial Intelligence: AI is the simulation of human intelligence in machines programmed to think and mimic human actions, like learning and problem-solving. ²

3. Bias: The presence of systematic and undesired preferences or imbalances in the output generated by an AI model. Bias can emerge in various forms, such as in the content, language, or perspectives generated by the AI system. ³

4. Chatbot: A chatbot is a computer program that simulates human conversation through text or voice.
   Chatbots are often used in customer service applications, where they can answer questions and provide support to customers without the need for human intervention. ¹

5. Data Mining: Data mining is a process of discovering new knowledge from large amounts of data. It is a subset of AI that uses machine learning & mathematical techniques to extract knowledge from data that can be used for fraud detection, risk management, & more. ¹

6. Ethical/Responsible AI: Ethical and thoughtful development and use of AI systems so that fairness, transparency, privacy, and societal impact are primary considerations. This is to ensure that AI benefits society while minimizing potential harms. ²

7. Generative AI: AI systems that can generate new content, such as text, images, or music. It involves developing algorithms and models that can understand patterns in existing data and use that understanding to generate novel output. ³

8. Hallucination: In AI, a hallucination refers to a mistake or error made by a model where it generates incorrect or nonsensical output that do not align with reality or the intended task. ²

9. Large Learning Model (LLM): Components of artificial intelligence developed based on the training of vast datasets of documents from various sources. The computer program analyzes data input and maps out words in the dataset. It next tries to predict which words are positioned before or after other words using predictive patterns of most likely combinations. ³

10. Machine Learning: Machine learning (ML) is a subset of AI that allows software applications to become more accurate in predicting outcomes without being explicitly programmed to do so.¹

11. Neural Network: A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. ⁴

12. Natural Language Processing (NLP): NLP is a branch of AI that deals with the interaction between computers and human (natural) languages. It encompasses a wide range of tasks, including text analysis, chatbots, speech recognition, and more. ¹

13. Pattern Recognition: A field of computer science that deals with the automatic discovery of predictive information from data. It is an area of machine learning that develops algorithm that can recognize data patterns & use them to make predictions. ¹

14. Plugins: Software components or modules that can be added to existing programs or systems to extend their functionality, allowing for customization without altering the core software. ²

15. Prompt: The initial input text or instructions given to a model to generate new content based on that starting point. It provides context and guides the model's output. The prompt can be a few words or sentences that set the tone or specify the desired content. ³

 

¹ K, K. B. (2024, March 30). 20 AI TERMINOLOGIES/JARGONS that you must know - Kanika B K - Medium. Medium. https://medium.com/@KanikaBK/20-ai-terminologies-jargons-that-you-must-know-0d4985e10338

² Kulkarni, A. (2024, April 14). 12 AI TERMS YOU MUST KNOW - AkShay kulkarni - Medium. Medium. https://akshaykulkarni101.medium.com/12-ai-terms-you-must-know-679b03ac5f7f

³ Academics: Artificial intelligence: Key AI Terms Glossary. (n.d.). https://academics.waldenu.edu/artificial-intelligence/glossary

⁴ Amazon Web Services, Inc. (n.d.). What is a Neural Network? - Artificial Neural Network Explained - AWS. https://aws.amazon.com/what-is/neural-network/