Course Outline

Introduction to Multi-Modal AI for Government

  • Definition of multi-modal AI
  • Key challenges and applications in the public sector
  • Overview of leading multi-modal models for government use

Text Processing and Natural Language Understanding for Government

  • Leveraging large language models (LLMs) for text-based AI agents in public sector operations
  • Techniques for prompt engineering to enhance multi-modal tasks for government applications
  • Fine-tuning text models for domain-specific needs within the government context

Image Recognition and Generation for Government

  • Utilizing AI for image processing: classification, captioning, and object detection in public sector scenarios
  • Generating images with advanced diffusion models (Stable Diffusion, DALLE) for government use cases
  • Integrating image data with text-based models to enhance governmental operations

Speech and Audio Processing for Government

  • Speech recognition using Whisper ASR for public sector applications
  • Text-to-speech (TTS) synthesis techniques tailored for government communication
  • Enhancing user interaction with voice-based AI in governmental services

Integrating Multi-Modal Inputs for Government

  • Building AI pipelines to process multiple input types for efficient public sector operations
  • Fusion techniques to combine text, image, and speech data for comprehensive analysis
  • Real-world applications of multi-modal AI agents in government agencies

Deploying Multi-Modal AI Agents for Government

  • Developing API-driven multi-modal AI solutions for government systems
  • Optimizing models to ensure performance and scalability in public sector environments
  • Best practices for deploying multi-modal AI in production within governmental frameworks

Ethical Considerations and Future Trends for Government

  • Addressing bias and fairness in multi-modal AI applications for government
  • Managing privacy concerns with multi-modal data in the public sector
  • Anticipating future developments in multi-modal AI for governmental use

Summary and Next Steps for Government

Requirements

  • An understanding of machine learning fundamentals for government applications
  • Experience with Python programming
  • Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch)

Audience

  • AI developers for government projects
  • Researchers in public sector institutions
  • Multimedia engineers working on government initiatives
 21 Hours

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