Intro to Generative AI

1. Introduction to Generative AI

   • Definition and overview of generative AI.
   • Historical context and evolution of the technology.
   • Key concepts and terminology.

2. Types of Generative AI Technologies

   • Deep Learning and Neural Networks.
   • Generative Adversarial Networks (GANs).
   • Variational Autoencoders (VAEs).
   • Transformer models (like GPT-3, BERT).
   • Case studies and real-world applications.

3. Ethical Considerations and Challenges

   • Bias in AI models.
   • Ethical use of generative AI.
   • Intellectual property and copyright issues.
   • Privacy concerns.

4. Practical Applications and Industry Use Cases

   • Creative industries (art, music, writing).
   • Business and marketing (ad generation, product design).
   • Science and research (data analysis, simulation).
   • Healthcare (drug discovery, patient data analysis).

5. Hands-On Sessions

   • Working with generative AI tools.
   • Building simple models.
   • Analyzing and interpreting AI-generated content.

6. Future of Generative AI

   • Emerging trends and technologies.
   • Potential impacts on society and industry.
   • The role of AI in shaping future innovations.

7. Conclusion and Q&A Session

   • Summarize key learnings.
   • Address participant questions.
   • Provide resources for further learning.

8. Post-Course Activities

   • Assignments or projects to apply learning.
   • Community or forum for ongoing discussion and support.

This course structure aims to provide a comprehensive understanding of generative AI, balancing theoretical knowledge with practical applications and ethical considerations.