Definition of AI and Major Generative AI Models
In our modern world where Artificial Intelligence (AI) has deeply integrated into our daily lives, the term Generative AI has become a familiar part of our everyday vocabulary.
In this lesson, we will explore the concept of AI, the history of generative AI, and the major generative AI models.
What is AI?
AI (Artificial Intelligence) refers to computer programs mimicking human intelligence to perform specific tasks.
For example, AI can recognize faces in photos, devise winning strategies in complex games, and perform a variety of other tasks.
Concept and History of Generative AI
Generative AI is a field of AI that has the ability to create new content based on given data.
It can generate various forms of content such as text, images, and music, significantly transforming creative activities.
The history of generative AI began with the advancements in deep learning and artificial neural networks.
In 2014, the introduction of GAN (Generative Adversarial Networks) marked the opening of new possibilities for generative AI.
GAN operates with two neural networks that compete against each other, demonstrating outstanding performance in generating more realistic data.
Lessons in this chapter ยท Introduction to AI Basics and Prompt Engineering
- 1. Definition of AI and Major Generative AI Models
- 2. How Generative AI Works
- 3. What Does It Mean When AI Learns?
- 4. What is a Prompt and How Does AI Understand It?
- 5. Components of a Prompt
- 6. Fill-in-the-blank quiz
- 7. Prompt Engineering for Optimal AI Responses
- 8. How to Use Delimiters to Structure Prompts
- 9. Mistakes in Generative AI, Hallucination
- 10. The Unit of Understanding Prompts in Generative AI (Token)
- 11. 4 Key Tips for Writing Great Prompts
- 12. Multiple-choice quiz
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