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Course Outline
Introduction
- Defining the fundamentals of generative artificial intelligence
- Distinguishing generative models from other AI paradigms
- Surveying core methodologies and model architectures in the generative domain
- Identifying operational applications and use cases for government
- Analyzing inherent constraints and operational challenges
Image Synthesis via Generative AI
- Translating textual descriptions into visual outputs
- Leveraging Generative Adversarial Networks (GANs) for high-fidelity image creation
- Utilizing Variational Autoencoders (VAEs) for latent space image generation
- Implementing style transfer to apply specific artistic or aesthetic modifications
Text Generation via Generative AI
- Producing textual content from prompt inputs
- Employing transformer architectures to ensure contextual accuracy and coherence
- Applying summarization techniques to distill extensive documents into concise briefs
- Using paraphrasing tools to reformulate policy language or reports for clarity
Audio Production via Generative AI
- Converting written text into synthetic speech
- Transcribing spoken audio into text formats
- Composing musical elements from textual or audio cues
- Synthesizing speech with specific vocal characteristics
Advanced Content Creation via Generative AI
- Generating code snippets from natural language specifications
- Producing conceptual sketches from textual descriptions
- Creating video content from text or image inputs
- Generating 3D models from textual or visual references
Assessment of Generative AI Performance
- Evaluating the fidelity and variety of generated outputs
- Applying standard metrics such as Inception Score, Fréchet Inception Distance, and BLEU
- Incorporating human-centric evaluation through structured review processes
- Utilizing adversarial testing methods, including Turing tests and discriminator models
Ethical and Societal Considerations in Generative AI
- Establishing frameworks for fairness and institutional accountability
- Mitigating risks associated with misuse and unauthorized applications
- Protecting intellectual property rights and data privacy interests
- Promoting human-AI collaboration in creative and operational tasks
Conclusion and Strategic Next Steps
Requirements
- Foundational knowledge of core AI concepts and terminology
- Proficiency in Python programming and data analysis methodologies
- Familiarity with deep learning frameworks, such as TensorFlow or PyTorch
Target Audience
- Data Scientists
- AI Engineers
- Technical Policy Analysts and AI Stakeholders
14 Hours
Testimonials (2)
the tips and recommended prompts that we can take away from this training
Lee Mei Lin - ST Engineering IHQ Pte Ltd
Course - InVideo AI: Creating Engaging Short-Form Videos
use of proper and effective prompt
