Generative AI - Fundamentals

U1: Generative AI

→ What is generative AI?
→ How does generative AI work?
→ Evolution of generative AI
→ Discriminative AI versus Generative AI
→ Why is generative AI important?
→ How does generative AI affect industries?

U2: AI and the creative process

→ Content ideation
→ Creative process with generative AI
→ How AI improves the content ideation process?
→ Overcoming challenges with AI in content ideation

U3: Generative AI models

→ Perspectives on generative AI models
→ How neural networks are transforming generative AI
→ Neural Networks Excursion
→ How do generative AI models work?
→ How to evaluate generative AI models?
→ How to develop generative AI models?
→ Diffusion models
→ Variational autoencoders (VAE)
→ Generative adversarial networks (GAN)
→ LLM: Large Language Models
→ Transformer networks


U4: Generative AI Applications

→ What are the applications of generative AI?
→ Generative AI Use Cases
→ Most popular generative AI applications
→ Text sample (ChatGPT)
→ Image sample (Dall-E)
→ Video sample (Visla)

U5: Benefits, challenges and concerns of AI

→ What are the challenges of generative AI?
→ What are the benefits of generative AI?
→ What are the limitations of generative AI?
→ What are the concerns surrounding generative AI?
→ The main business challenges of generative AI
→ Ethics and bias in generative AI
→ Ethical approach to generative AI
→ Best practices for using generative AI

U6: The future of generative AI

→ The future of generative AI
→How could generative AI replace jobs?
→ Will AI ever gain consciousness?
→ Existential risk of AI: Is AI a threat to humanity?
→ What's next for generative AI?

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