# Generative AIの基本概念
This is a DataCamp course: 生成AIを責任を持って活用する方法について、ぜひご覧ください。生成AIモデルの開発方法と、今後社会に与える影響について学びましょう。
## Course Details
- **Duration:** ~2h
- **Level:** Beginner
- **Instructor:** Daniel Tedesco
- **Students:** ~19,440,000 learners
- **Subjects:** Theory, Artificial Intelligence, R
- **Content brand:** DataCamp
- **Practice:** Hands-on practice included
- **CPE credits:** 2.8
- **Prerequisites:** Understanding Machine Learning
## Learning Outcomes
- Define generative AI and recognize its applications across industries.
- Differentiate between generative and discriminative models, including GANs, transformers, and RLHF.
- Identify biases in generative AI models and assess strategies for bias detection and mitigation.
- Recognize ethical and legal considerations, including copyright, privacy, and ownership issues.
- Evaluate emerging trends, challenges, and opportunities in adopting generative AI responsibly.
## Traditional Course Outline
1. Introduction to Generative AI - Familiarize yourself with the concept of generative AI and its ability to create content is introduced, along with its real-world applications and limitations. You'll delve into the differences between traditional machine learning models, generative AI, and artificial general intelligence (AGI), and explore the key factors driving the development of generative AI.
2. Developing Generative AI Models - In this chapter, we cover the essential steps in creating generative AI models: research and design, data collection, model training, and evaluation. We examine the significance of diverse datasets and advanced training techniques, as well as various evaluation methods, while discussing their strengths and limitations.
3. Using AI Models and Generated Content Responsibly - This chapter focuses on the responsible use of generative AI. We discuss the challenges and strategies to mitigate social bias, intellectual property and privacy issues, and ethical considerations to prevent misuse. We conclude by exploring the immense potential and risks of Artificial Generative Intelligence (AGI), along with the approaches to control its outcomes.
4. Getting Ready for the Age of Generative AI - Chapter 4 examines the potential, impact, and integration of generative AI into human workflows. It discusses key contributors to AI development, from universities to companies, and explores societal adaptations to AI. It delves into AI's implications for productivity, job dynamics, education, media, entertainment, scientific advancements, and ethical considerations.
## Resources and Related Learning
**Resources:** Course Glossary: Generative AI Concepts (dataset)
**Related tracks:** AIの基礎
## Attribution & Usage Guidelines
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Generative AIの基本概念
基礎スキルレベル
更新日 2025/11TheoryArtificial Intelligence2時間14 ビデオ43 演習2,750 XP97,357達成証明書
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前提条件
Understanding Machine Learning1
Introduction to Generative AI
Familiarize yourself with the concept of generative AI and its ability to create content is introduced, along with its real-world applications and limitations. You'll delve into the differences between traditional machine learning models, generative AI, and artificial general intelligence (AGI), and explore the key factors driving the development of generative AI.
2
Developing Generative AI Models
In this chapter, we cover the essential steps in creating generative AI models: research and design, data collection, model training, and evaluation. We examine the significance of diverse datasets and advanced training techniques, as well as various evaluation methods, while discussing their strengths and limitations.
3
Using AI Models and Generated Content Responsibly
This chapter focuses on the responsible use of generative AI. We discuss the challenges and strategies to mitigate social bias, intellectual property and privacy issues, and ethical considerations to prevent misuse. We conclude by exploring the immense potential and risks of Artificial Generative Intelligence (AGI), along with the approaches to control its outcomes.
4
Getting Ready for the Age of Generative AI
Chapter 4 examines the potential, impact, and integration of generative AI into human workflows. It discusses key contributors to AI development, from universities to companies, and explores societal adaptations to AI. It delves into AI's implications for productivity, job dynamics, education, media, entertainment, scientific advancements, and ethical considerations.
Generative AIの基本概念
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