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Course Outline

Introduction to Multi-Modal Artificial Intelligence

  • Definition and scope of multi-modal AI systems
  • Primary operational challenges and government applications
  • Survey of prominent multi-modal model architectures

Text Processing and Natural Language Comprehension

  • Utilizing Large Language Models (LLMs) for text-driven AI agents
  • Principles of prompt engineering for multi-modal contexts
  • Adapting text models for specialized domain requirements

Image Recognition and Synthetic Generation

  • Analyzing visual data through classification, captioning, and object detection
  • Generating imagery using diffusion-based models (e.g., Stable Diffusion, DALL-E)
  • Synchronizing visual inputs with textual model frameworks for government use cases

Speech and Audio Analysis

  • Implementing automated speech recognition via Whisper ASR
  • Methods for high-fidelity text-to-speech synthesis
  • Improving accessibility and engagement through voice-enabled AI interfaces

Integration of Multi-Modal Inputs

  • Designing data pipelines to manage diverse input streams
  • Techniques for fusing textual, visual, and auditory data modalities
  • Practical implementations of multi-modal AI agents in public sector environments

Deployment of Multi-Modal AI Agents

  • Developing application programming interface (API) architectures for multi-modal solutions
  • Enhancing model efficiency, performance, and system scalability
  • Standards and protocols for secure production deployment of multi-modal AI

Ethical Frameworks and Emerging Trends

  • Addressing algorithmic bias and ensuring equitable outcomes in multi-modal AI
  • Mitigating privacy risks associated with multi-modal data processing
  • Anticipated advancements in the field of multi-modal artificial intelligence

Executive Summary and Strategic Next Steps

Requirements

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

Audience

  • Artificial intelligence developers
  • Scientific researchers
  • Multimedia engineering specialists
 21 Hours

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