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AI Tools and Resources

Introduction

This guide provides a basic overview of Generative Artificial Intelligence, in order to help you make informed choices about using AI tools. 

The field of AI is changing at a rapid pace, with new information about applications, policies, and social impacts coming out daily. While generative AI tools help users synthesize information and create content (essays, art, music, code, etc.), they can also make up facts or sources and create biased content. If you use a GenAI tool or platform, be sure to apply your critical thinking skills.

What is Generative AI?

Generative AI refers to a branch of AI focused on creating systems capable of producing original content, such as images, text, music, or even videos, often indistinguishable from human-created content. These systems employ techniques like neural networks and deep learning to learn patterns from existing data and generate new outputs based on those patterns. 

Large Language Models (LLMs) constitute a specific category of generative AI models with a specialized focus on text-based data. For example, ChatGPT writes a response to a prompt, providing text based on what words came before and what is the most likely next word. They are not search engines, but rather trained chatbots aiming to fill in the next missing content piece, i.e. what one might expect.

Key Terms

Below are some simple definitions for key ideas related to modern AI platforms. 

  • Adaptive Learning: Subject or course material is adjusted based on the performance of the learner. The difficulty of material, the pacing, sequence, type of help given, or other features can be adapted based on the learner’s prior responses.
  • Artificial intelligence (AI): Typically defined as the ability of a machine to perform cognitive functions we associate with human minds, such as perceiving, reasoning, learning, and problem solving. Examples of technologies that enable AI to solve complex problems include robotics, computer vision, language, virtual agents and machine learning.
  • Data Mining: The process of discovering patterns, correlations, or trends in large datasets. Data mining techniques are often used in AI and machine learning to extract valuable insights from data.
  • Deep Learning: A subset of machine learning. While basic machine learning models do become progressively better at whatever their function is, they still need some guidance. If an AI algorithm returns an inaccurate prediction, then an engineer has to step in and make adjustments. With a deep learning model, an algorithm can determine on its own if a prediction is accurate or not through its own neural network.
  • Generative AI: A type of AI system that generates text, images, or other media in response to user prompts.
  • Hallucination: A well-known phenomenon in large language models, in which the system provides an answer that is factually incorrect, irrelevant or nonsensical, because of limitations in its training data and architecture.
  • Large Language Models (LLMs): LLMs, such as ChatGPT, apply deep neural networks to text data and generate output from prompts.
  • Machine Learning: A subset of AI that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit programming; it is the process of teaching a computer system to recognize patterns and make predictions based on data.
  • Natural Language Processing: A branch of artificial intelligence concerned with giving computers the ability to understand text and spoken word in the same way humans can.
  • Neural Networks: Computational models inspired by the structure and function of the human brain. Neural networks consist of interconnected nodes (neurons) organized in layers, where each layer processes information and passes it to the next layer.
  • Supervised learning: A machine learning technique where the authors of the model tell the machine learning algorithm how to handle the training data in order to generate the desired output.
  • Training data: The information that is digested by a machine learning algorithm. 
  • Unsupervised learning: A machine learning technique where the machine learning algorithm creates its own labels for variables within the training data.

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