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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