Course Outline

Introduction to Conversational AI and Small Language Models (SLMs)

  • Fundamentals of conversational AI for government applications
  • Overview of SLMs and their advantages in public sector workflows
  • Case studies of SLMs in interactive applications for government services

Designing Conversational Flows

  • Principles of human-AI interaction design for government use
  • Crafting engaging and natural dialogues to enhance citizen engagement
  • User experience (UX) considerations in public sector interactions

Building Customer Service Bots

  • Use cases for customer service bots in government agencies
  • Integrating SLMs into government customer service platforms
  • Handling common citizen inquiries with AI-driven solutions

Training SLMs for Interaction

  • Data collection for conversational AI in government contexts
  • Training techniques for SLMs in dialogue systems tailored for government use
  • Fine-tuning models for specific interaction scenarios within the public sector

Evaluating Interaction Quality

  • Metrics for assessing conversational AI performance in government settings
  • User testing and feedback collection methods for government applications
  • Iterative improvement based on evaluation to enhance public service delivery

Voice-Enabled and Multimodal Interactions

  • Incorporating voice recognition with SLMs for government services
  • Designing multimodal interactions (text, voice, visuals) in public sector applications
  • Case studies of voice assistants and chatbots deployed by government agencies

Personalization and Contextual Understanding

  • Techniques for personalizing interactions to meet citizen needs
  • Context-aware conversation handling in public sector services
  • Privacy and data security considerations in personalized AI for government use

Ethical Considerations and Bias Mitigation

  • Ethical frameworks for conversational AI in the public sector
  • Identifying and mitigating biases in interactions to ensure fairness
  • Ensuring inclusivity and equity in government AI communication

Deployment and Scaling

  • Strategies for deploying conversational AI systems in government agencies
  • Scaling SLMs for widespread use across multiple departments and services
  • Monitoring and maintaining AI interactions post-deployment to ensure ongoing effectiveness

Capstone Project

  • Identifying a need for conversational AI in a chosen government domain
  • Developing a prototype using SLMs tailored for government applications
  • Testing and presenting the interactive application to demonstrate its potential impact on public services

Final Assessment

  • Submission of a capstone project report detailing the development process and outcomes
  • Demonstration of a functional conversational AI system designed for government use
  • Evaluation based on innovation, user engagement, and technical execution in a public sector context

Summary and Next Steps

Requirements

  • Fundamental knowledge of Artificial Intelligence and Machine Learning for government applications
  • Proficiency in Python programming for government projects
  • Experience with Natural Language Processing concepts for government use cases

Audience

  • Data scientists for government agencies
  • Machine learning engineers for government initiatives
  • AI researchers and developers for government projects
  • Product managers and UX designers for government solutions
 14 Hours

Number of participants


Price per participant

Upcoming Courses

Related Categories