Course Outline
Introduction
- What is generative artificial intelligence (AI)?
- Comparing generative AI with other types of AI
- Overview of key techniques and models in generative AI
- Applications and use cases of generative AI for government
- Challenges and limitations of generative AI for government operations
Creating Images with Generative AI
- Generating images from text descriptions
- Utilizing Generative Adversarial Networks (GANs) to create realistic and diverse images
- Employing Variational Autoencoders (VAEs) to generate images with latent variables
- Applying style transfer techniques to add artistic styles to images
Creating Text with Generative AI
- Generating text from textual prompts
- Using transformer-based models to produce contextually coherent text
- Employing text summarization techniques to create concise summaries of lengthy texts
- Utilizing text paraphrasing to express the same meaning in different ways
Creating Audio with Generative AI
- Generating speech from text inputs
- Converting spoken words into written text
- Producing music from textual or audio inputs
- Creating speech with a specific voice profile
Generating Other Content with Generative AI
- Generating code from natural language descriptions
- Producing product sketches based on textual inputs
- Creating video content from text or images
- Generating 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
- Fostering creativity and collaboration between human experts and AI systems for government initiatives
Summary and Next Steps
Requirements
- An understanding of fundamental artificial intelligence concepts and terminology
- Experience with Python programming and data analysis techniques
- Familiarity with deep learning frameworks, such as TensorFlow or PyTorch
Audience for Government
- Data scientists
- AI developers
- AI enthusiasts
Testimonials (3)
it went through language right up to automation and made me aware of what capabilities I have.
Declan Glennon - Teleflex Medical Europe Ltd
Course - Copilot for Finance and Accounting Professionals
Excellect knowledge level of the trainer
Pawel Dykowski - LKQ Polska Sp. z o. o.
Course - Microsoft 365 Copilot Chat for Word, Excel, PowerPoint, and Outlook
1. Structured prompts and related technique to have it like this 2. working with practical examples - prompts already well prepared 3. Learned how to create an AI agent
