Course Outline

Introduction to Multi-Modal AI for Government

  • What is 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 government operations
  • Understanding prompt engineering for multi-modal tasks to enhance public services
  • Fine-tuning text models for domain-specific applications within government agencies

Image Recognition and Generation for Government

  • Processing images with AI: classification, captioning, and object detection for government tasks
  • Generating images with diffusion models (Stable Diffusion, DALLE) for government use cases
  • Integrating image data with text-based models to support governmental workflows

Speech and Audio Processing for Government

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

Integrating Multi-Modal Inputs for Government

  • Building AI pipelines for processing multiple input types to support governmental operations
  • Fusion techniques for combining text, image, and speech data in government applications
  • Real-world applications of multi-modal AI agents in the public sector

Deploying Multi-Modal AI Agents for Government

  • Building API-driven multi-modal AI solutions for government use
  • Optimizing models for performance and scalability in governmental systems
  • Best practices for deploying multi-modal AI in production environments within government agencies

Ethical Considerations and Future Trends for Government

  • Bias and fairness in multi-modal AI for government applications
  • Privacy concerns with multi-modal data in the public sector
  • Future developments in multi-modal AI for government use

Summary and Next Steps for Government

Requirements

  • A comprehensive understanding of machine learning fundamentals
  • Proficiency in Python programming
  • Experience with deep learning frameworks (e.g., TensorFlow, PyTorch)

Audience for Government

  • Artificial Intelligence developers
  • Researchers
  • Multimedia engineers
 21 Hours

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