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Generative AI Concepts

Learn about generative AI models like GPT-3, DALL-E 2, and Stable Diffusion with this course. Understand the underpinning mechanics and legal/ethical considerations of AI content generation. Register to get updates when this course is live!

  • Mechanics of Generative AI
  • Development of Generative AI
  • Ethical Considerations Generative AI
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What You Will Learn

Upcoming Course Description

part 1

Mechanics of Generative AI

In this section, you will gain an understanding of the underpinning mechanics of generative AI models like GPT-3, DALL-E 2, and Stable Diffusion. You will explore how these models generate new content from text-based prompts and demystify the concepts behind AI content generation.

part 2

Development of Generative AI

This section will cover the development of generative AI models, including specific issues and considerations in gathering training data, performing model training, and detecting potential bias. You will learn about the challenges of developing generative AI models and how to overcome them.

part 3

Ethical Considerations Generative AI

As generative AI becomes more prevalent, it is important to consider the legal and ethical implications of AI-generated content. This section will address the legal and ethical considerations surrounding AI-generated content and explore potential solutions to these issues.

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What is generative AI and how does it differ from other branches of Artificial Intelligence?

Generative AI is a sub-domain of Artificial Intelligence that focuses on creating new content, such as text, images, audio, or videos, based on input prompts. It differs from other branches of AI, such as supervised learning, as it does not rely on labeled data for training, but instead uses techniques like unsupervised or reinforcement learning to generate content autonomously.

What are some examples of generative AI models that have gained attention recently?

Some examples of generative AI models that have gained attention recently are GPT-3 (Generative Pre-trained Transformer 3), DALL-E 2 (Drawing Artificial Intelligence that Learns and Generates Images), and Stable Diffusion. These models have demonstrated impressive capabilities in generating content from text-based prompts, images, and other inputs.

What are the key considerations in gathering training data for generative AI models?

Gathering training data for generative AI models requires careful consideration of several factors. These include the size and quality of the dataset, the diversity of the data, and the potential biases present in the data. Ensuring that the training data is representative and unbiased is crucial to prevent the generation of biased or unethical content by the AI models.

What are some potential use cases of generative AI in different industries?

Generative AI has potential use cases across various industries. For example, in the gaming industry, it can be used for creating game content, characters, and storylines. In the entertainment industry, it can be used for generating movie scripts or music compositions. In the marketing industry, it can be used for creating personalized advertisements or social media content. Exploring the potential applications of generative AI in different industries can provide insights into its practical use and impact.

What are the prerequisites for this course?

This course is designed for learners with a basic understanding of Artificial Intelligence. Familiarity with AI concepts is recommended but not mandatory.

Is there a certificate of completion for this course?

Yes, upon completing the course, you will receive a certificate of completion from DataCamp to showcase your achievement and new skills in "Generative AI Concepts."

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