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Course Outline
Introduction
- What is generative artificial intelligence (AI)?
- Comparison between generative AI and other types of AI
- Overview of primary techniques and models in generative AI
- Applications and use cases of generative AI for government
- Challenges and limitations of generative AI
Creating Images with Generative AI
- Generating images from text descriptions
- Using Generative Adversarial Networks (GANs) to create realistic and diverse images
- Utilizing Variational Autoencoders (VAEs) to generate images with latent variables
- Applying style transfer techniques to impart artistic styles to images
Creating Text with Generative AI
- Generating text from textual prompts
- Leveraging transformer-based models to produce contextually coherent text
- Using text summarization methods to create concise summaries of lengthy texts
- Employing text paraphrasing techniques to express the same meaning in different ways
Creating Audio with Generative AI
- Generating speech from textual input
- Converting speech into written text
- Producing music from textual or audio inputs
- Creating speech with a specific voice profile
Creating Other Content with Generative AI
- Generating code from natural language descriptions
- Producing product sketches from textual input
- Generating video content from text or images
- Creating 3D models from textual or visual data
Evaluating Generative AI
- Assessing the quality and diversity of generated content
- Utilizing metrics such as inception score, Fréchet Inception Distance, and BLEU score
- Conducting human evaluations through crowdsourcing and surveys
- Implementing adversarial evaluation methods like Turing tests and discriminators
Understanding Ethical and Social Implications of Generative AI
- Ensuring fairness and accountability in the use of generative AI for government
- Preventing misuse and abuse of generative AI technologies
- Respecting the rights and privacy of content creators and consumers
- Promoting creativity and collaboration between human users and AI systems
Summary and Next Steps
Requirements
- A foundational knowledge of artificial intelligence (AI) concepts and terminology
- Practical experience with Python programming and data analysis
- Proficiency with deep learning frameworks, such as TensorFlow or PyTorch
Audience for Government
- Data scientists
- AI developers
- AI enthusiasts
14 Hours
Testimonials (1)
I liked that trainer had a lot of knowledge and shared it with us
