Generative AI at UM-Dearborn
Generative AI (GenAI) is a general term for artificial intelligence that creates new content by generating new data samples that are similar to the training set. GenAI can be used to quickly produce or analyze various types of content, including text, imagery, audio and synthetic data from fairly simple user interfaces. While this technology is not brand-new (GenAI was introduced in the 1960s in chatbots), advances in computing devices and algorithms now mean that GenAI can create convincingly authentic text, images, videos and audio of real people.
More about Generative AI
One of the most well-known GenAI applications, ChatGPT developed by OpenAI, is a sophisticated chatbot that has been trained on an enormous collection of text data to develop an understanding of the patterns and structures of human language. The model behind ChatGPT, which relies on that vast collection of text, is called a large language model (LLM). LLMs have been trained on digitized books, articles, websites, and other types of text to learn patterns in natural and written language. In response to prompts from users, ChatGPT can generate text that is coherent and convincingly human-like in seconds. It can summarize historical events, write an essay or basic computer code, translate a passage, or even compose poetry and songs. Examples of uses that have already emerged include using it as a research assistant, a proofreader, a brainstorming aid, a calculator, and many more. However, outputs fall short of demonstrating the higher levels of learning needed to succeed in a rigorous academic setting. Additionally, it’s also sometimes wrong, and with great confidence.
While ChatGPT has been the most discussed machine learning tool of late, alternatives are available or in development (e.g. Google Bard, etc.). Tools like Stable Diffusion or DALL-E 2 have been used to generate surprisingly beautiful, detailed, original, and realistic images based on text prompts. New tools are emerging on a daily basis and so it can be hard to keep up.
Generative AI Guidance
UM-Dearborn’s Generative AI Task Force has developed some guidance on specific topics related to GenAI for faculty, staff, and students:
In addition to these UM-Dearborn specific resources, the U-M Generative AI site has information and resources for all faculty, staff, and students from all three U-M campuses.
U-M GenAI Tools
U-M has created a few tools for students, faculty, and staff to make AI services accessible in a low cost (or free) way that respects data privacy and security.
U-M GPT
Maizey
U-M GPT Toolkit
About Us
UM-Dearborn's Generative AI guidance was created by the 2022-2023 Generative AI Task Force and is maintained/updated by the Generative AI working group. If you have any questions or comments about generative AT at UM-Dearborn, please email [email protected].
The Generative AI Working Group is compromised of the following members:
- Coordinator of Digital Education - Christopher Casey, chair
- Instructional Designer - Autumm Caines
- Faculty (one from each college)
- CASL - Tom Fiore
- CECS - Paul Watta
- CEHHS - Stein Brunvand
- COB - Jun He
The 2022-2023 Generative AI Task Force was comprised of the following members:
- Coordinator of Digital Education - Christopher Casey, chair
- Faculty (one from each college)
- CASL - Pamela Todoroff
- CECS - Paul Watta
- CEHHS - Stein Brunvand
- COB - Jun He
- Instructional Designer - Autumm Caines
- Librarian - Anne Dempsey
- Director of Student Conduct & Dearborn Support - Ryan Neloms
- Writing Center Coordinator - John Taylor
- Business Communications Lab Director - Jennifer Coon
- START Director - Andrew Beverly