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What is Generative AI? What are some key terms to know?
This chapter explores:
- the origins and evolution of artificial intelligence;
- the meaning of key terms like AI, generative AI, large language model, and machine learning;
- the recent proliferation of new AI tools, and some ways they have impacted education.
AI has a long history. Flip through the timeline below to explore a selection of the key advances in AI. Click on the image in the top right hand corner to view fullscreen.
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Generative AI is a type of artificial intelligence that uses machine learning to generate new content by analyzing and processing vast amounts of data from diverse sources. Generative AI tools can generate text, images, video, sound, code, mathematical calculations, and more. The generated responses of these tools are probabilistic, which can result in errors in responses. Large language models (LLMs), for instance, specialize in analyzing and processing text and generating new text. Different LLMs have distinct datasets and employ unique training methods. GPT 3.5, GPT 4, and PaLM 2 are examples of LLMs. OpenAI’s ChatGPT 4 is a chatbot created on GPT 4.
A useful glossary of AI terms can be found here and a brief introductory video from Ethan Mollick and Lilach Mollick of the Wharton School is below:
While generative AI is not new, OpenAI’s launch of ChatGPT in November 2022 marked the fastest recorded adoption of a technology tool to date. Over the following months, other multipurpose generative AI tools like Copilot (formerly Bing Chat) and Gemini (formerly Google Bard) were released, as were thousands of specialized AI tools. Many of these specialized AI tools are based on OpenAI’s GPT, and are optimized for a specific task from scripting an apology to crafting a meal plan to debugging code.
The rapid proliferation of tools and advancements in technology led to over one hundred leaders in AI technology to write an open letter urging a collective pause on AI developments more powerful than GPT-4, calling for security and safety features and the creation of regulation and governance structures. The need for such regulation extends globally, and also applies to specific sectors such as post-secondary education. Broader issues related to generative AI include privacy of personal data, risks of misinformation, existential risks, bias, environmental costs, labour exploitation, and copyright. Read more about these issues in the Capabilities and Limitations of AI Tools chapter.
Key Takeaways
- AI has a long history, and the capability of AI tools has grown exponentially over time.
- Generative AI tools turn plain language prompts into text, images, videos, code, and more.
Exercises
- Make a list of ways that AI tools may be used in your particular role. This may include enhancing teaching and learning, or assisting with administrative tasks.
- Experiment with an AI tool like Copilot, ChatGPT, or Gemini. Ask it to complete a few tasks related to your work context or out-of-work interests, reflecting on what it does well and what its weaknesses are.
Attributions
This chapter is an adaptation of “Generative Artificial Intelligence in Teaching and Learning at McMaster University” by Paul R MacPherson Institute for Leadership, Innovation and Excellence in Teaching and is used under a CC BY 4.0 license.
The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision making, and translation between languages.
The theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision making, and translation between languages.
Based on or adapted to a theory of probability; subject to or involving chance variation.
AI tools that can generate human-like text, based on predictions made after learning from vast amounts of written text.
A computer program designed to simulate conversation with human users, especially over the internet.
