See the key differences between generative and traditional AI.
Overview
Syllabus
Introduction
- Explore generative AI vs. traditional AI
- Machine learning
- Supervised and unsupervised learning
- Artificial neural networks
- Data models
- Foundation models
- Large language models (LLMs)
- Image diffusion models
- Generative pre-trained transformer (GPT)
- Prompt engineering
- Generative adversarial networks (GANs)
- Self-supervised learning
- Variational autoencoder (VAE)
- Building a generative AI system
- Traceable decision-making
- Hallucination liability
- Copyright training
- Mass data collection and privacy
- The expertise death spiral
- Next steps for AI
Taught by
Doug Rose
