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GAI & Artificial Intelligence (AI) Resource Guide

About This Guide

This guide introduces Generative AI and Artificial Intelligence(AI). It includes videos, text, handouts, and more conversations about AI and Generative AI, mostly in higher education. Each tab on the left is broken out by patron type (student, faculty, staff). All resources are available to everyone.

Updated 9/19/2024

The library recognizes the evolving landscape of Artificial Intelligence (AI) and Generative AI and appreciates the diverse perspectives surrounding their use. As a resource center dedicated to facilitating open access to information and supporting critical thinking, we strive to present a neutral platform for exploration and dialogue on this complex topic.

The library does not take a stance for or against AI or Generative AI. Our role is to offer a comprehensive range of resources that represent various viewpoints on these technologies' ethical, social, and practical implications. 

For questions regarding policy and guidelines, we encourage users to refer to the official policies established by Utica University and the Office of the Provost. 

We invite you to utilize the library's resources to inform your understanding of AI and Generative AI.  Remember, critical thinking and informed discussion are essential as we navigate the evolving world of AI. We encourage you to engage with the resources and perspectives available at the library and within the wider university community.

Before going into definitions and examples of AI and Generative AI(GAI), here are some common myths and misconceptions that will be addressed throughout the guide.

  • MISCONCEPTION: AI is new.
    • As early as the 1950s, there were creations, ideas, and discussions around "artificial intelligence."
      • 1950: Alan Turing writes a paper titled Computing Machinery and Intelligence that asks "Can machines think?"
      • 1956: The Dartmouth Conference, noted for being the official birthplace of "artificial intelligence" as a separate study field and where the term was created.
      • 1980s: Focus on Machine Learning- developing algorithms that could learn data and make predictions. 
      • 1990s: 1998 brings Google's first web index
      • 2010s: Emphasizing Deep Learning (See Key Terms)
      • 2020s: Generative AI and Large Language Models (See Key Terms)
  • MISCONCEPTION: Generative AI and AI are the same thing.
    • Generative AI is a subterm of AI. Click HERE to learn about the structure of AI.
  • MYTH: AI and Generative AI are neutral and unbiased. (On this Page, we will discuss this myth in-depth.)
    • This is not true. The tools are trained on the human-created data and human biases that affect their thinking. 
  • MYTH: AI and Generative AI will replace "real learning"
    • Many worry that these tools will replace students' work.  Will some students cheat? Yes, but that happens without AI and Generative AI tools and happened long before this. 
    • Education continues to evolve and this is another element of education that must change.
  • MISCONCEPTION: AI and Generative AI can think like humans.
    • They do not possess human-like emotions and perceptions that heavily influence thought processes. It may try and mimic human responses based on learned patterns, but it currently does not 100% think like humans
  • MISCONCEPTION: AI and Generative AI threaten jobs. (Check out How Does Artificial Intelligence Create New Jobs?)
    • While AI has helped reduce workloads by automating tasks and making jobs easier, it has also created new jobs.  It cannot go unmonitored, there needs to be people who oversee the product. 
  • MYTH: AI and Generative AI are evil and a threat.
    • AI and Generative AI are not inherently evil. It can be used for good, evil, or something in between. It is not risk-free. It is more important to focus on how people use AI and Generative AI than the levels of evil in AI. 
  • MYTH: Generative AI knows everything
    • Generative AI is trained on limited data. It doesn't have real-time awareness. It can't access information beyond its training.

Algorithm: A set of well-defined instructions or rules that makes a machine perform a specific task. 

Bias: A systematic error from assumptions made by the model. Bias, which comes from the people who made the machines and the societal system they are in, can negatively affect results and quality.

Corpus: A large dataset of material that can be used to train a machine to perform linguistic tasks.

Dataset: A collection of related data points, usually with a uniform order and tags.

Deep Learning: A machine learning technique that teaches computers to do what comes naturally to humans: learn by example. 

Hallucination: A 'nonsense' result produced by a GAI when prompted to return information outside of its corpus. Examples: citations for articles/media that don't exist; plausible-sounding responses that contain incorrect, misleading, or fully fabricated information

Image recognition: Identifying a person, object, place, or text in an image. 

Large Language Models: Apply deep neural networks to text data and generate output from prompts.

Narrow AI: AI tool created for a specific function: no reasoning/logic; unable to respond outside of corpus. Examples: spelling and grammar check tools; any 'recommender' tool; Siri

Natural Language Processing  A subset of AI that focuses on computers gaining the ability to understand text and spoken words in the same way humans can.

Prompt: A sentence or phrase that gives AI a model to generate a response/output

This term can be confusing when trying to figure out what tools are using Generative AI and tools that are just AI-based. To make it easier to understand, we will be using the following definition when talking about AI (although there are other definitions):  What is artificial intelligence? and Artificial Intelligence Foundations

AI or Artificial Intelligence is a human-created computer system that processes data through algorithms. It can execute tasks such as visual perception, speech recognition, and decision-making that would normally require human intelligence.

*From TechTarget

Programming for AI focuses on specific skills for the best use. Using current examples, we can see the skills and their applications:

  • Learning: 
    • AI constantly analyzes data and updates its algorithms, improving its abilities over time.
      • EX: Google Ads providing purchase suggestions based on previous searches.  
  • Self-Correction: 
    • This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible.
      • EX: Telling your Spell-Checker to "add to dictionary"  for Pfannestiel. The program will continue to self-correct when seeing that person's name in your documents.
  • Creativity:
    • Using rules-based systems, statistical methods, and other AI techniques to create new images, new text, new music, and new ideas.
      • EX: This is where we start to see Generative AI Tools more, like asking DALL·E to create an image from the words "sunset" and "beach" to get a photo that was never taken.

Here are some AI Tools you might not have known you were using:

  • Personalized Recommendations: Streaming services (Netflix), shopping websites (Amazon), and social media (TikTok) use AI algorithms to provide personalized recommendations based on the user's preferences, history, and behavior.
  • Virtual Assistants:  Siri, Google Assistant, and Alexa utilize AI algorithms to understand and respond to their user's voice commands.
  • Email Filters and Spam Detection: Email filters help identify and organize emails while distinguishing between important messages and spam. 
  • Image and Voice Recognition:  Google Photos, Apple's Face ID, and speech-to-text applications identify objects, faces, or speech patterns in images and audio recordings.
  • Search Engines: Google, being the biggest one, uses AI algorithms to enhance search results while providing more relevant and personalized information as they gain more information about users' preferences.

Understanding Generative AI:

Generative AI is a subcategory of AI. It uses algorithms to create content like generating images, text, and music. It can be used for many needs, from creative content to organizational tasks. Generative AI generates content that is similar and based on data that it was trained on.* Similar to AI, these tools may not be free or may require you to have an account(with your personal data) when using it. 

*Ex: ChatGPT only had data from 2021 and earlier, meaning it wouldn't make inferences regarding 2023 data.

Types of Generative AI & Examples:

  • Text Content
    • The most known Generative AI right now, ChatGPT, is a text-based AI System. It can produce blogs, scripts, news articles, reflective statements, and even poetry. There are practical items it can create like college schedules or bulleted lists for chores. 
  • Image Creation
    • It learns through descriptions and the more specific you are with the image, the closer it will be to something you picture in your head. It can use different styles as well such as hand drawing, oil paintings, anime, and pop art.
    • Here are some AI-generated images from Canva with the prompt "Moose playing Hockey" 
    • Some tools cannot use existing, non-free materials, or non-public domain works.
    • Image tools can steal concepts and images from artists. 
      • The big difference between an artist and an AI Generative tool: Formula.
      • These tools are trained to find concepts that already exist to create something "new".  If you use the same prompt, the AI tools will create similar content every time.  
        • Click HERE to learn about Legal Issues with AI Tools  
  • Sound Creation
    • These music generators analyze music and metadata (artist name, album name, genre, year released) to find patterns in music genres.  Some have been trained to associate lyrics with songs.
    • Popular examples are AI Covers of Songs:  Whitney Houston ft. Frank Sinatra - Until I Found You (AI Cover)

      • If it has only been exposed to one type of music like Taylor Swift's, then the generated music will sound somewhat similar to her songs.

    • Examples of tools: AIVA and Soundful

  • Video Content

    • These AI tools require more elements(audio, visual, and text elements). Some programs are given video editing software as well to create captions, transitions, and animations.

Here are some Generative AI Tools you might not have known you were using:

  • ChatGPT 3.5 or ChatGPT4
    • Developed by OpenAI, is an artificial intelligence tool that generates text based on given prompts. 
  • Gemini
    • It is an AI chatbot that can respond to a user's question with an almost human-like "understanding." It can create content, summarize text, and translate between languages.
  • GrammarlyGO
    • If you already have a Grammarly account, you’ll get 500 prompts per month, and you can use this to write blog introductions, outlines, ads, and emails.
  • Magic Design
    • From Canva, creates a curated selection of personalized and on-brand designs just for you.
  • DALL-E 3
    • Created by OpenAI
    • Produces images and art in any style based on a user description.
  • Synthesia  
    • A tool that allows you to turn text into video with AI-generated voices.
  • GitHub Copilot  
    • An AI pair programmer that translates human language into programming code.
  • SlidesAI  
    • SlidesAI is used for Google Slides and generates presentation slides from any text.
  • Explainpaper  
    • Users can upload papers and highlight confusing text to get an explanation.

For more view Top 35 Generative AI Tools by Category