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Generative AI vs. Traditional AI

Generative AI vs. Traditional AI

59mIntermediate2023-09-25

Authors

Doug Rose

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

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