Instructor
Doug Rose
Teaching Fortune 500s and professionals how to lead change
Related to this course
Course details
1h 17m
Intermediate
Updated: 5/16/2025
Generative AI is a hot topic that’s filled with a host of new legal, ethical, and technology issues. Generative AI’s development may seem sudden, but it’s still built upon decades of concepts and practices from traditional predictive AI. In this course Doug Rose looks at the differences between traditional and generative AI. Traditional concepts like supervised and unsupervised deep learning networks have inspired newer generative AI concepts like self-supervised learning, foundation models, diffusion models, and generative adversarial networks. To understand where a technology is heading, it’s important to know its story. These generative AI tools are a big leap, but they’re still just another chapter in the exciting story of artificial intelligence.
Learning objectives
Recognize the most viable use cases for supervised and unsupervised learning.
Discuss the core components of the OpenAI GPT model.
Explain what generative adversarial networks (GANs) are and identify their use cases.
Define what self-supervised learning systems are and identify their use cases.
Describe what variational autoencoders (VAEs) are and identify their use cases.
Recognize the risks of generative model hallucinations and explain how to reduce hallucinations.
Skills covered
Generative AI
Artificial Intelligence (AI)
Traditional AI
Student Ratings & Reviews
