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Generative Artificial Intelligence (GenAI): Home

Image reads: Generative Artificial Intelligence, Resources for the Stockton University community

Currency of Information

The landscape of ChatGPT and other AI tools is constantly changing. We strive to keep this guide accurate and current, updating it as new information becomes available. If you discover any discrepancies or outdated information, please email: cheyenne.riehl@stockton.edu

 

What is Generative AI?

Generative artificial intelligence (GenAI) is a type of advanced technology that uses complex algorithms to learn from large amounts of data. This data can include text, images, videos, music, and other content. By analyzing patterns and relationships within this information, GenAI can create new content that closely resembles human-made work.

If you are new to using generative AI tools, these short videos provide a useful introduction.

 
Practical AI for Instructors and Students (10 to 12 minutes each)
Wharton School, University of Pennsylvania
 
Generative AI Explained (3 Minutes)
Coursera

Ethical and Safety Considerations

Generative AI models have been trained on a vast amount of unstructured data without human oversight, which can lead to the creation of content that raises concerns. Since the data is unstructured, it hasn’t been carefully organized or vetted. Ethical and safety concerns include: 

  • Quality and Reliability of Data: Generative AI models trained on unstructured data may include information that is inaccurate, biased, or incomplete. This can lead to AI learning from flawed or misleading examples.

  • Bias and Ethics:  AI may learn and replicate biases present in the data. This could lead to the generation of content that is discriminatory, offensive, or unethical, reflecting the biases in the training data.

  • Lack of Transparency: It can be difficult to understand how the AI arrived at certain conclusions or outputs. This lack of transparency can be problematic, especially when AI is used in sensitive areas like healthcare, finance, or law.

  • Unpredictable Outputs: Without supervision, the AI can generate unpredictable or inappropriate content, as it may combine elements from the data in unexpected ways. This unpredictability can be a risk in applications where consistent and reliable results are crucial.

  • Legal and Copyright Issues: The unstructured data used for training may include copyrighted material or personal data, raising legal concerns about the ownership and use of the generated content. This can lead to disputes over intellectual property and privacy rights.