Course Outline

Introduction to Multimodal Learning

  • Overview of multimodal AI for government
  • Challenges in processing multimodal data within public sector workflows
  • Benefits of utilizing multimodal large language models (LLMs) for government applications

Understanding Large Language Models

  • Architecture of state-of-the-art LLMs and their relevance to governmental operations
  • Training LLMs with multimodal data to enhance public sector services
  • Case studies: Successful applications of multimodal LLMs in government settings

Processing Multimodal Data

  • Data preprocessing techniques for text, image, and audio in a government context
  • Feature extraction and representation learning tailored for public sector data
  • Integrating multimodal data into LLMs to support governmental tasks

Developing Multimodal LLM Applications

  • Designing user interfaces for effective multimodal interaction in government systems
  • Leveraging LLMs in virtual assistants and chatbots for improved citizen services
  • Creating immersive experiences with LLMs to enhance public engagement

Evaluating and Optimizing Multimodal Systems

  • Performance metrics specific to multimodal LLMs in government applications
  • Optimization strategies for enhancing accuracy and efficiency of governmental systems
  • Addressing bias and fairness issues in multimodal systems to ensure equitable public services

Hands-on Lab: Building a Multimodal LLM Project

  • Setting up a multimodal dataset for government use cases
  • Implementing a multimodal LLM for a specific governmental application
  • Testing and refining the system to meet public sector standards

Summary and Next Steps

Requirements

  • An understanding of machine learning and neural networks for government applications
  • Experience with Python programming in a public sector context
  • Familiarity with data preprocessing techniques for various data types, including text, image, and audio, to support government workflows

Audience

  • Data scientists working in the public sector
  • Machine learning engineers focused on government projects
  • Software developers supporting governmental systems
  • Researchers concentrating on AI and natural language processing for government use cases
 14 Hours

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