Artificial intelligence (AI) is “a technical and scientific field devoted to the engineered system that generates outputs such as content, forecasts, recommendations or decisions for a given set of human-defined objectives” [ISO/IEC 22989:2022].
Traditionally, AI systems are primarily used to analyse data and make predictions. It has since evolved in developing machines capable to analyse large amounts of data, recognise patterns, and make predictions or decisions based on the trained data.
To better understand how AI works, it is essential to know the following basic concepts:
1. Machine Learning
2. Deep Learning
3. Reasoning
4. Problem-solving
5. Processing Language
6. Large Language Model (LLM)
7. Perception/ Computer Vision
Generative Artificial Intelligence, evolving from advancements in AI technology and machine learning, has enabled the creation of new data and content across images, videos, music, articles, and software code. The application of Generative AI significantly impacts work efficiency, yet at the same time raises ethical considerations.
This guide provides an overview of generative AI such as the potential usage of generative AI tools, responsible use of these tools, how to design a prompt, how to evaluate the generated content, and how to cite content created from generative AI.
(Source: Canvas AI Image Generator (ai neural network), 20 May 2024)
Generative Artificial Intelligence (Gen AI) tools are AI chatbots that use humanlike conversational dialogue (natural language) to obtain responses and results through descriptive questions (known as "prompts") in a fluid manner. They generate content in the form of text, data, and multimedia such as images, music, videos, and voices by harnessing AI technology and a vast dataset as its foundation.
Although Generative AI tools can provide answers to queries in texts or create new multimedia content that seems real, they can also produce inaccurate, non-existent, or misleading results, which is known as "hallucinating" when the datasets or the prompts are of poor quality.
It is essential to learn how to craft effective prompts to guide the Generative AI tools to create quality content. It is equally important to learn how to use Generative AI tools and the generated content appropriately, thoughtfully, and responsibly.
Generative AI literacy plays an important role in evaluating and using Generative AI tools.
Generative AI tools have many potential uses in learning, teaching, and research as well as risks.
It is necessary to acquire and evaluate knowledge of AI and its uses, understand its limitations, and the ethical considerations to safeguard the educational experience, intellectual development, respect for intellectual property, and data privacy of the SUSS community and beyond.
Please always check with your instructor, who may have specific requirements for your course in using any generative AI tools, and provide guidance on the appropriate use and citation requirements.
Potential Usage
(Source: Teaching & Learning Centre, Singapore University of Social Sciences. (2023). Academic Integrity [online]. Available from: https://rise.articulate.com/share/i4wup4aZRMcXCL293kJqgBgfp3UmaQMl#/ [accessed 7 May 2024].)
Many generative AI tools do not come free, and most of them require registration. The providers of these tools may collect personal information such as IP address and email address and share them with their partners to analyse the user behaviour.
All users of Generative AI within SUSS (including the Institute of Adult Learning) must adhere to data privacy and security protocols, especially when dealing with sensitive or personal data. This includes understanding the data handling practices of the AI tools used. Confidential or sensitive information should be left out of prompts when using Generative AI.
Intellectual property belonging to SUSS shall not be input/uploaded onto the Generative AI tools.
Sources:
1. Singapore University of Social Sciences ("SUSS") Privacy Statement
2. Student Handbook (accessible by SUSS enrolled students through the Student Portal)
3. Generative AI Policy for Teaching and Learning (accessible only by faculty, teaching associates and staff through MySUSS intranet)
[Last accessed 7 May 2024]
You can refer to the professional sharing sessions on generative AI organized by the Teaching & Learning Centre, SUSS.
Do join the "SIG on Generative AI & Learning, Teaching and Assessment" which is a SUSS community of practice to advance pedagogical and assessment practices of generative AI.
The way how we design, plan, and write prompts will affect the accuracy, creativity, depth, effectiveness, and quality of responses and results we might get from the generative AI tools.
Write prompts like how you make a meaningful conversation or request for a product/service by providing clear and specific instructions
Basic Guidelines
AI Literacy
Always review and verify the generated content to ensure it does not provide misinformation or disinformation. You can make use of the CRAAP (Currency, Relevance, Authority, Accuracy, and Purpose) principles for evaluation.
- Understand AI
- Use and Apply AI
- Analyse and Evaluate AI
- Create AI
Example of a Prompt:
|
You are an expert librarian specializing in Singapore history. List 3 relevant and useful sources that contain credible and authoritative publications on Singapore history. Search and retrieve books and peer-reviewed journal articles based on the 3 topics:
Provide the citation count, source of the citation, and summary for these books and journal articles. Organize the information into a list by topic and compile them into a table. Compile the information into a bibliography. Include the title, author, publisher, and year of publication. Use APA style, 7th edition. Provide the date of compilation at the end of the table |
Below are selected resources that provide guidance on how to write or generate effective prompts:
1. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models (Google Brain Team)
2. Chatgpt Cheat Sheet (University of Dartmouth)
4. Dalle3 Prompt Tips And Tricks Thread
5. GenAI Chatbot Prompt Library for Educators
6. GitHub: Awesome ChatGPT Prompts
7. How to Perfect Your Prompt Writing for ChatGPT, Midjourney, and Other AI Generators
8. Keywordseverywhere (chatgpt prompt templates)
9. Prompt Engineering (OpenAI)
10. Learn about Copilot prompts (Microsoft Copilot)
Prompts to Try (Microsoft Copilot)
11. The CLEAR path: A framework for Enhancing Information Literacy through Prompt Engineering
All ideas that are not original must be acknowledged, including ideas from generative AI tools.
As with acceptable academic practice, attribution is expected if students use AI to generate responses as part of assessment submissions. Attribution includes the following:
How to cite Generative AI tools (e.g., ChatGPT) as a source
Presently, there are no specific, formal guidelines from the major citation styles about how to cite ChatGPT or other Generative AI tools as a source.
The general recommendation is to use the format for personal communications to cite any response generated by AI tools.
For more information, you may refer to the following Citation Guides:
(Extracted from Teaching & Learning Centre, Singapore University of Social Sciences. (2023). Academic Integrity [online]. Available from: https://rise.articulate.com/share/i4wup4aZRMcXCL293kJqgBgfp3UmaQMl#/ [accessed 7 May 2024].)
Further Reading:
You can make use of our subscribed library resources and complement these sources with other online resources to access information on generative AI.
Please refer to our library guide to learn how to search for resources or book a consultation session with our librarians to get personalized guidance on searching and using information effectively.
Introduction to Generative AI by
Generative AI tools like ChatGPT are amazing--but how will their use impact our society? This book introduces the world-transforming technology and the strategies you need to use generative AI safely and effectively. Introduction to Generative AI gives you the hows-and-whys of generative AI in accessible language. In this easy-to-read introduction, you'll learn: How large language models (LLMs) work How to integrate generative AI into your personal and professional workflows Balancing innovation and responsibility The social, legal, and policy landscape around generative AI Societal impacts of generative AI Where AI is going Anyone who uses ChatGPT for even a few minutes can tell that it's truly different from other chatbots or question-and-answer tools. Introduction to Generative AI guides you from that first eye-opening interaction to how these powerful tools can transform your personal and professional life. In it, you'll get no-nonsense guidance on generative AI fundamentals to help you understand what these models are (and aren't) capable of, and how you can use them to your greatest advantage. Foreword by Sahar Massachi. About the technology Generative AI tools like ChatGPT, Bing, and Bard have permanently transformed the way we work, learn, and communicate. This delightful book shows you exactly how Generative AI works in plain, jargon-free English, along with the insights you'll need to use it safely and effectively. About the book Introduction to Generative AI guides you through benefits, risks, and limitations of Generative AI technology. You'll discover how AI models learn and think, explore best practices for creating text and graphics, and consider the impact of AI on society, the economy, and the law. Along the way, you'll practice strategies for getting accurate responses and even understand how to handle misuse and security threats. What's inside How large language models work Integrate Generative AI into your daily work Balance innovation and responsibility About the reader For anyone interested in Generative AI. No technical experience required. About the author Numa Dhamani is a natural language processing expert working at the intersection of technology and society. Maggie Engler is an engineer and researcher currently working on safety for large language models. The technical editor on this book was Maris Sekar. Table of Contents 1 Large language models: The power of AI Evolution of natural language processing 2 Training large language models 3 Data privacy and safety with LLMs 4 The evolution of created content 5 Misuse and adversarial attacks 6 Accelerating productivity: Machine-augmented work 7 Making social connections with chatbots 8 What's next for AI and LLMs 9 Broadening the horizon: Exploratory topics in AI
Generative Deep Learning by
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos Train GANs to generate images based on your own dataset Build diffusion models to produce new varieties of flowers Train your own GPT for text generation Learn how large language models like ChatGPT are trained Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN Compose polyphonic music using Transformers and MuseGAN Understand how generative world models can solve reinforcement learning tasks Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.
Exploring Deepfakes by
Master the innovative world of deepfakes and generative AI for face replacement with this full-color guide Purchase of the print or Kindle book includes a free PDF eBook Key Features: Understand what deepfakes are, their history, and how to use the technology ethicallyGet well-versed with the workflow and processes involved to create your own deepfakesLearn how to apply the lessons and techniques of deepfakes to your own problems Book Description: Applying Deepfakes will allow you to tackle a wide range of scenarios creatively. Learning from experienced authors will help you to intuitively understand what is going on inside the model. You'll learn what deepfakes are and what makes them different from other machine learning techniques, and understand the entire process from beginning to end, from finding faces to preparing them, training the model, and performing the final swap. We'll discuss various uses for face replacement before we begin building our own pipeline. Spending some extra time thinking about how you collect your input data can make a huge difference to the quality of the final video. We look at the importance of this data and guide you with simple concepts to understand what your data needs to really be successful. No discussion of deepfakes can avoid discussing the controversial, unethical uses for which the technology initially became known. We'll go over some potential issues, and talk about the value that deepfakes can bring to a variety of educational and artistic use cases, from video game avatars to filmmaking. By the end of the book, you'll understand what deepfakes are, how they work at a fundamental level, and how to apply those techniques to your own needs. What You Will Learn: Gain a clear understanding of deepfakes and their creationUnderstand the risks of deepfakes and how to mitigate themCollect efficient data to create successful deepfakesGet familiar with the deepfakes workflow and its stepsExplore the application of deepfakes methods to your own generative needsImprove results by augmenting data and avoiding overtrainingExamine the future of deepfakes and other generative AIsUse generative AIs to increase video content resolution Who this book is for: This book is for AI developers, data scientists, and anyone looking to learn more about deepfakes or techniques and technologies from Deepfakes to help them generate new image data. Working knowledge of Python programming language and basic familiarity with OpenCV, Pillow, Pytorch, or Tensorflow is recommended to get the most out of the book.
Natural Language Processing with Transformers, Revised Edition by
Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments
Artificial Intelligence by
A global perspective on AI AI is much more than just a simple tool powering our smartphones or allowing us to ask Alexa about the latest cinema times. It is a technology that is, in very subtle but unmistakable ways, exerting an ever-increasing influence over our lives - and the more we use it, the more AI is altering our existence. The rise of AI and super-intelligent AI raises ethical issues. AI is the power behind Google's search engine, enables social media sites to serve up targeted advertising, and gives Alexa and Siri their voices. It is also the technology enabling self-driving vehicles, predictive policing, and autonomous weapons that have the ability to kill without direct human intervention. All of these bring up complex ethical issues that are still unresolved and will continue to be the subject of ongoing debate. There are untold how-to books on AI technology, replete with methods to improve and advance the statistics and algorithms of AI; however, the social, ethical and security impacts are often at best a secondary consideration - if discussed at all. This book explores the complex topic of AI ethics in a cross-functional way, alternating between theory and practice. Practical and recent events, and their associated challenges, are presented, such as the collateral effects of the COVID-19 pandemic on the application of AI technologies. The book also gives an up-to-date overview of the potential positive and negative outcomes of AI implementations together with an analysis of AI from an ethical perspective. Before you dive into a world populated with AI, read this book to understand the associated ethical challenges of AI technologies
Building Generative AI-Powered Apps A Hands-on Guide for Developers by 
Generative AI on AWS by
Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock
Synthetic Data and Generative AI by
Synthetic Data and Generative AI covers the foundations of machine learning with modern approaches to solving complex problems and the systematic generation and use of synthetic data. Emphasis is on scalability, automation, testing, optimizing, and interpretability (explainable AI). For instance, regression techniques - including logistic and Lasso - are presented as a single method without using advanced linear algebra. Confidence regions and prediction intervals are built using parametric bootstrap without statistical models or probability distributions. Models (including generative models and mixtures) are mostly used to create rich synthetic data to test and benchmark various methods.
Unlocking the Secrets of Prompt Engineering by
Enhance your writing with AI by mastering prompt engineering techniques and become an expert in developing and utilizing LLM prompts across applications Key Features Master prompt engineering techniques to harness AI's writing potential Discover diverse LLM applications for content creation and beyond Learn through practical examples, use cases, and hands-on guidance Purchase of the print or Kindle book includes a free PDF eBook Book Description Unlocking the Secrets of Prompt Engineering is your key to mastering the art of AI-driven writing. This book propels you into the world of large language models (LLMs), empowering you to create and apply prompts effectively for diverse applications, from revolutionizing content creation and chatbots to coding assistance.Starting with the fundamentals of prompt engineering, this guide provides a solid foundation in LLM prompts, their components, and applications. Through practical examples and use cases, you'll discover how LLMs can be used for generating product descriptions, personalized emails, social media posts, and even creative writing projects like fiction and poetry. The book covers advanced use cases such as creating and promoting podcasts, integrating LLMs with other tools, and using AI for chatbot development. But that's not all. You'll also delve into the ethical considerations, best practices, and limitations of using LLM prompts as you experiment and optimize your approach for best results.By the end of this book, you'll have unlocked the full potential of AI in writing and content creation to generate ideas, overcome writer's block, boost productivity, and improve communication skills. What you will learn Explore the different types of prompts, their strengths, and weaknesses Understand the AI agent's knowledge and mental model Enhance your creative writing with AI insights for fiction and poetry Develop advanced skills in AI chatbot creation and deployment Discover how AI will transform industries such as education, legal, and others Integrate LLMs with various tools to boost productivity Understand AI ethics and best practices, and navigate limitations effectively Experiment and optimize AI techniques for best results Who this book is for This book is for a wide audience, including writers, marketing and business professionals, researchers, students, tech enthusiasts, and creative individuals. Anyone looking for strategies and examples for using AI co-writing tools like ChatGPT effectively in domains such as content creation, drafting emails, and inspiring artistic works, will find this book especially useful. If you are interested in AI, NLP, and innovative software for personal or professional use, this is the book for you. ]]>
The Quick Guide to Prompt Engineering by
Design and use generative AI prompts that get helpful and practical results in this concise and quick start guide. In The Quick Guide to Prompt Engineering, renowned technology futurist and AI thought leader Ian Khan delivers a practical and insightful resource for taking the first steps in understanding and learning how to use generative AI. You will learn how to design and use prompts to get the most out of Large Language Model generative AI applications like ChatGPT, DALL-E, Google's Bard, and explore how to understand generative artificial intelligence and how to engineer prompts in a wide variety of industry use cases. You'll also find illuminating case studies and hands-on exercises, as well as step-by-step guides, to get you up to speed on prompt engineering in no time at all. The book has been written for the non-technical user to take the first steps in the world of generative AI. Along with a helpful glossary of common terms, lists of useful additional reading and resources, and other resources, you'll get: Explanations of the basics of generative artificial intelligence that help you to learn what's going on under the hood of ChatGPT and other LLMs Stepwise guides to creating effective, efficient, and ethical prompts that help you get the most utility possible from these exciting new tools Strategies for generating text, images, video, voice, music, and other audio from various publicly available artificial intelligence tools Perfect for anyone with an interest in one of the newest and most practical technological advancements recently released to the public, The Quick Guide to Prompt Engineering is a must-read for tech enthusiasts, marketers, content creators, technical professionals, data experts, and anyone else expected to understand and use generative AI at work or at home. No previous experience is required.
Writing AI Prompts for Dummies by
Learn the art of writing effective AI prompts and break into an exciting new career field Unlock the full power of generative AI with Writing AI Prompts For Dummies, a comprehensive guide that will teach you how to confidentially write effective AI prompts. Whether it's text, images, or even videos and music you're aiming to create, this book provides the foundational knowledge and practical strategies needed to produce impressive results. Embark on a journey of discovery with Writing AI Prompts For Dummies and learn how to: Craft AI prompts that produce the most powerful results. Navigate the complexities of different AI platforms with ease. Generate a diverse range of content, from compelling narratives to stunning visuals. Refine AI-generated output to perfection and integrate that output effectively into your business or project. This resource is brimming with expert guidance and will help you write AI prompts that achieve your objectives. Whether you're a marketer, educator, artist, or entrepreneur, Writing AI Prompts For Dummies is your indispensable guide for leveraging AI to its fullest potential. Get ready to harness the power of artificial intelligence and spark a revolution in your creative and professional efforts.
eJournal Articles
Please access the peer-reviewed eJournal articles on generative artificial intelligence subscribed by the library.
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Reports
You can access the latest reports, trends, and developments on generative AI from EDUCAUSE and Gartner.
1. Reports and latest trends and developments on Generative AI from Gartner's Generative AI Resource Center
2. Resources on AI in Higher Education from EDUCAUSE
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Below are the selective online courses on generative AI from O'Reilly:
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Please access the learning courses and videos on generative AI through the LinkedIn Learning courses that we curated for you.
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Besides SUSS Library's subscribed resources, you can complement these sources with useful online resources for your learning and research purposes. Please direct your enquiries to the respective institutions if you require clarification on the use of their resources.
1. AI Singapore
2. AI Verify Foundation by IMDA
3. Artificial Intelligence Teaching Guide / Standford University
5. Evaluating Information – Applying the CRAAP Test
5. Generative AI in Higher Education: The Product Landscape.
6. Generative AI Research / By MIT Sloan
7. Generative AI Product Tracker / By Ithaka
8. How AI Impacts on Academic Publishing
9. OECD - Artificial Intelligence
10. National AI Strategy (Singapore)
11. The Art of the Prompt: How to Get the Best Out of Generative AI / Microsoft
12. What is Artificial Intelligence (AI)? / ISO
13. World Economic Forum Annual Meeting 2024 - Turbocharging Learning While Mitigating Risks
14. 10 Ways Artificial Intelligence Is Transforming Instructional Design
