IL Tutorial Series #4: "AI," Machine-Generated Content, & ChatGPT

AI, Machine-Generated Content & ChatGPT

This module will define and discuss artificial intelligence (AI) and generative AI such as ChatGPT. By the end of the module, you will be able to:

• understand broadly how AI creates information from available data
• identify some of the risks and benefits of AI

Read the article linked under "Module Readings" on the left, study the glossary, and view the video below.

Then scroll down to complete a short quiz.

The term "AI" stands for Artificial Intelligence, but is used to cover a number of concepts, including machine-generated content, machine learning, robotics, and expert systems to name a few.

Here are some common terms you might encounter:

Generative A.I.: Technology that creates content — including text, images, video and computer code — by identifying patterns in large quantities of training data, and then creating original material that has similar characteristics. Examples include ChatGPT for text and DALL-E and Midjourney for images.

Large language model: A type of neural network that learns skills — including generating prose, conducting conversations and writing computer code — by analyzing vast amounts of text from across the internet. The basic function is to predict the next word in a sequence, but these models have surprised experts by learning new abilities.

Natural language processing: Techniques used by large language models to understand and generate human language, including text classification and sentiment analysis. These methods often use a combination of machine learning algorithms, statistical models and linguistic rules.

Neural network: A mathematical system, modeled on the human brain, that learns skills by finding statistical patterns in data. It consists of layers of artificial neurons: The first layer receives the input data, and the last layer outputs the results. Even the experts who create neural networks don’t always understand what happens in between.

Reinforcement learning: A technique that teaches an A.I. model to find the best result by trial and error, receiving rewards or punishments from an algorithm based on its results. This system can be enhanced by humans giving feedback on its performance, in the form of ratings, corrections and suggestions.

Pasick, A. (2023, 27 Mar). Artificial intelligence glossary: Neural networks and other terms explained. New York Times. https://www.nytimes.com/article/ai-artificial-intelligence-glossary.html