Generative AI vs. Traditional AI
59mIntermediate2023-09-25
Authors

Doug Rose
Teaching Fortune 500s and professionals how to lead change
Course details
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.
Skills covered
Generative AIArtificial Intelligence (AI)One-Off
Concepts
0. Introduction
- 01 - Explore generative AI vs. traditional AI
1. Predictive AI Architecture
- 02 - Machine learning
- 03 - Supervised and unsupervised learning
- 04 - Artificial neural networks
- 05 - Data models
2. Generative AI Models
- 06 - Foundation models
- 07 - Large language models (LLMs)
- 08 - Image diffusion models
- 09 - Generative pre-trained transformer (GPT)
3. Generative AI Architecture
- 10 - Prompt engineering
- 11 - Generative adversarial networks (GANs)
- 12 - Self-supervised learning
- 13 - Variational autoencoder (VAE)
- 14 - Building a generative AI system
4. Generative AI Legal and Ethical Issues
- 15 - Traceable decision-making
- 16 - Hallucination liability
- 17 - Copyright training
- 18 - Mass data collection and privacy
- 19 - The expertise death spiral
Conclusion
- 20 - Next steps for AI
Related courses
- Introduction to Agentic AI in 5G: Smarter Autonomous Networks
- Foundations of AI and Machine Learning for Java Developers
- Become a Generative AI Power User and Content Designer
- AI Toolkit Essentials for Visual Studio Code
- Spec-Driven Development with GitHub Spec Kit
- Azure AI for Developers: LLMs and SLMs
- Running AI Locally: Tools, Assistants, and Coding on Your Own Hardware
- Building Generative AI with AWS: Amazon Q Developer, Bedrock Inference, and SageMaker Canvas
Related learn paths
- Understanding Generative AI for Tech Leaders
- Building Generative AI Skills for Web Developers
- Master Digital Transformation
- Build AI Products Using Azure AI Services in Your Development Lifecycle
- Technical Literacy and Future Readiness for Senior Managers and Senior Leaders
- Data Engineering Professional Certificate by Snowflake
- Working Globally as an Individual Contributor
- Master GitHub Copilot