Generative AI is a powerful technology that can create human-like text, images, audio, and more. While it offers exciting possibilities, it also raises important questions about its limitations and ethical implications. Generative AI responses may sometimes be inaccurate, untruthful, or misleading. OpenAI has cautioned against relying on it for essential research and answers.
* ChatGPT is not connected to the internet and was last updated in DEC 2023.
Critical thinking and evaluation needs to be applied to the content that is produced, such as:
This file is licensed under the Creative Commons Attribution-Share Alike 4.0 International license. Author: The Original Benny CFrom Deakin University Library's Guide "Using Generative AI":
Text content creation:
Image content creation:
Sound content creation:
Coding creation:
Video creation:
Here is an excellent link found by the STEM Club for Girls at Fullers Library! It is a great overview of Ai Terms.
Artificial Intelligence (AI): A field of study within computer science, focused on the development of computer systems that can accomplish tasks typically associated with human intelligence. These tasks include speech recognition, route mapping, decision making, etc.
Bias: The training data of an AI model can skew the output, leading it to generate inaccurate or offensive material.
Chatbot: A program designed to communicate with humans in a natural manner, sometimes to facilitate providing information or completing tasks.
Chat Generative Pre-trained Transformer (ChatGPT): A chatbot developed by OpenAI. ChatGPT is a transformer type of AI that is designed to mimic conversations using natural language processing, through which users can write prompts to generate text-based responses.
Generative AI: A model of artificial intelligence that can generate new content such as text, images, video, etc., through pattern recognition, by examining large amounts of training data and creating material that contains similar characteristics to identified patterns in the dataset. Examples include ChatGPT, Claude, Midjourney or DALL-E.
Hallucinations: Instances where a generative AI model generates output that contains inaccurate or irrelevant information, especially when it may look correct. For example, when asking ChatGPT (or any text-based generative AI) to generate a list of citations for a topic, the citations it provides may look accurate but the source material associated with the citation may not actually exist when searching for it.
Large Language Model (LLM): An AI model that receives large amounts of training data that establishes the capacity for it to respond to conversational queries. AI such as ChatGPT, Bard, or Claude use LLM.
Natural Language Processing (NLP): The programmed capacity to understand conversations and respond in kind.
Prompt: A structured text-based query that asks a generative AI to generate new content in the form of text, image, video, etc.
Prompt Engineering: The process of refining prompts to elicit more desirable results from generative AI.
Training Data: The development of a generative AI model involves the input of specific types of data, often in large amounts. This process is referred to as “training” and it determines the content output of the specific model. For example, if developing an AI that reviews artwork specifically, the AI model will be trained only on data containing artwork.
This glossary is an adaptation of the following sources: Generative AI as a Research Tool from Brown University and Librarians Can Play a Key Role Implementing Artificial Intelligence in Schools from School Library Journal.
ChatGPT is not connected to the Internet and was last updated in DEC 2023.
SPINNING A 'WEB OF LIES'
By Maggie Hicks
AUGUST 23, 2023
Amy Chatfield, an information-services librarian for the Norris Medical Library at the University of Southern California, can hunt down and deliver to researchers just about any article, book, or journal, no matter how obscure the topic or far-flung the source.
So she was stumped when she couldn’t locate any of the 35 sources a researcher had asked her colleague to deliver.
Each source included an author, journal, date, and page numbers, and had seemingly legit titles such as “Loan-out corporations for entertainers and athletes: A closer look,” published in the Journal of Legal Tax Research.
Then she started noticing oddities about the sources...
ChatGPT for Dummies
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Inside AI
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Generative AI : How ChatGPT and other AI tools will revolutionize business
Chapter 1: Introduction to Generative AI -- Chapter 2: Data -- Chapter 3: AI Fundamentals -- Chapter 4: Core Generative AI Technology -- Chapter 5: Large Language Models -- Chapter 6: Auto Code Generation -- Chapter 7: The Transformation of Business -- Chapter 8: The Impact on Major Businesses -- Chapter 9: The Future.
Trustworthy AI
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The AI Dilemma
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The misuse of AI has led to wrongful arrests, denial of medical care, even genocide-this book offers 7 powerful principles that business can use now to end the harm. AI holds incredible promise to improve virtually every aspect of our lives, but we can't ignore its risks, mishaps and misuses. Juliette Powell and Art Kleiner offer seven principles for ensuring that machine learning supports human flourishing. They draw on Powell's research at Columbia University and use a wealth of real-world examples. Four principles relate to AI systems themselves. Human risk must be rigorously determined and consciously included in any design process. AI systems must be understandable and transparent to any observer, not just the engineers working on them. People must be allowed to protect and manage their personal data. The biases embedded in AI must be confronted and reduced. The final three principles pertain to the organizations that create AI systems. There must be procedures in place to hold them accountable for negative consequences. Organizations need to be loosely structured so that problems in one area can be isolated and resolved before they spread and sabotage the whole system. Finally, there must be psychological safety and creative friction, so that anyone involved in softwaredevelopment can bring problems to light without fear of reprisal. Powell and Kleiner explore how to implement each principle, citing current best practices, promising new developments, and sobering cautionary tales. Incorporating the perspectives of engineers, businesspeople, government officials, and social activists, this book will help us realize the unprecedented benefits and opportunities AI systems can provide.
Generative AI in Practice
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Introduction to Generative AI
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