Generative AI: Creating Novel Content with AI Models

• An understanding of basic AI concepts and terminology
• Experience with Python programming and data analysis
• Familiarity with deep learning frameworks such as TensorFlow or PyTorch

Audience

• Data scientists
• AI developers
• AI enthusiasts

Generative AI (GenAI) is a category of AI algorithms that can create new and realistic content from existing data, such as images, text, audio, and more. Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level data scientists, AI developers, and AI enthusiasts who wish to use generative AI to create novel and diverse content for various purposes.

By the end of this training, participants will be able to:

• Set up a development environment that includes generative AI models and tools.
• Create images, text, audio, and other content from text prompts using generative AI.
• Use generative AI in different domains such as art, design, entertainment, and education.
• Evaluate the quality and diversity of the content generated by generative AI.
• Understand the ethical and social implications of generative AI.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction

• What is generative AI?
• Generative AI vs other types of AI
• Overview of main techniques and models in generative AI
• Applications and use cases of generative AI
• Challenges and limitations of generative AI

Creating Images with Generative AI

• Generating images from text descriptions
• Using GANs to create realistic and diverse images
• Using VAEs to create images with latent variables
• Using style transfer to apply artistic styles to images

Creating Text with Generative AI

• Generating text from text prompts
• Using transformer-based models to create text with context and coherence
• Using text summarization to create concise summaries of long texts
• Using text paraphrasing to create different ways of expressing the same meaning

Creating Audio with Generative AI

• Generating speech from text
• Generating text from speech
• Generating music from text or audio
• Generating speech with a specific voice

Creating Other Content with Generative AI

• Generating code from natural language
• Generating product sketches from text
• Generating video from text or images
• Generating 3D models from text or images

Evaluating Generative AI

• Assessing content quality and diversity in generative AI
• Using metrics like inception score, Fréchet inception distance, and BLEU score
• Utilizing human evaluation through crowdsourcing and surveys
• Applying adversarial evaluation methods such as Turing tests and discriminators

Understanding Ethical and Social Implications of Generative AI

• Ensuring fairness and accountability
• Avoiding misuse and abuse
• Respecting the rights and privacy of content creators and consumers
• Fostering creativity and collaboration of human and AI

Summary and Next Steps