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 Duration 21 hours

Course Outline

Introduction to Multi-Modal AI

  • Definition and scope of multi-modal AI
  • Strategic challenges and operational applications
  • Evaluation of prominent multi-modal models

Text Processing and Natural Language Understanding

  • Utilizing LLMs for text-centric AI agents
  • Prompt engineering strategies for multi-modal workflows
  • Fine-tuning language models for specialized government sectors

Image Recognition and Generation

  • AI-driven image analysis: classification, captioning, and detection
  • Image synthesis using diffusion models (Stable Diffusion, DALLE)
  • Integrating visual data with textual models for government use

Speech and Audio Processing

  • Speech recognition via Whisper ASR
  • Text-to-speech (TTS) synthesis methodologies
  • Enhancing citizen interaction through voice-based AI for government

Integrating Multi-Modal Inputs

  • Constructing AI pipelines for diverse input modalities
  • Fusion techniques for aggregating text, image, and audio data
  • Practical implementation of multi-modal AI agents for government

Deploying Multi-Modal AI Agents

  • Architecting API-driven multi-modal AI solutions
  • Model optimization for performance and scalability
  • Operational best practices for production deployment in public sector contexts

Ethical Considerations and Future Trends

  • Addressing bias and fairness in multi-modal AI for government
  • Data privacy standards and concerns
  • Emerging trajectories in multi-modal AI technology

Summary and Next Steps

Requirements

  • Foundational understanding of machine learning principles
  • Proficiency in Python programming
  • Working knowledge of deep learning frameworks (e.g., TensorFlow, PyTorch)

Target Audience

  • AI Developers
  • Researchers
  • Multimedia Engineers

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