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Course Outline
Introduction to Conversational AI and Small Language Models (SLMs)
- Fundamentals of conversational artificial intelligence
- Overview of small language models and their benefits
- Case studies of SLMs in interactive applications for government
Designing Conversational Flows
- Principles of human-AI interaction design for government
- Crafting engaging and natural dialogues for public sector use
- User experience (UX) considerations in government applications
Building Customer Service Bots
- Use cases for customer service bots in government agencies
- Integrating SLMs into customer service platforms for enhanced public interaction
- Handling common customer inquiries with AI for improved efficiency and satisfaction
Training SLMs for Interaction
- Data collection methods for conversational AI in government
- Training techniques for SLMs in dialogue systems for public sector applications
- Fine-tuning models for specific interaction scenarios in governmental services
Evaluating Interaction Quality
- Metrics for assessing conversational AI performance in government
- User testing and feedback collection methods for public sector applications
- Iterative improvement based on evaluation for enhanced service delivery
Voice-Enabled and Multimodal Interactions
- Incorporating voice recognition with SLMs in government services
- Designing multimodal interactions (text, voice, visuals) for enhanced user experience
- Case studies of voice assistants and chatbots in governmental applications
Personalization and Contextual Understanding
- Techniques for personalizing interactions in government services
- Context-aware conversation handling to improve public engagement
- Privacy and data security considerations in personalized AI for government
Ethical Considerations and Bias Mitigation
- Ethical frameworks for conversational AI in government
- Identifying and mitigating biases in interactions to ensure fairness
- Ensuring inclusivity and fairness in AI communication for all users
Deployment and Scaling
- Strategies for deploying conversational AI systems in government agencies
- Scaling SLMs for widespread use across governmental services
- Monitoring and maintaining AI interactions post-deployment to ensure reliability
Capstone Project
- Identifying a need for conversational AI in a chosen government domain
- Developing a prototype using SLMs for public sector applications
- Testing and presenting the interactive application to stakeholders
Final Assessment
- Submission of a capstone project report detailing the development process
- Demonstration of a functional conversational AI system for government use
- Evaluation based on innovation, user engagement, and technical execution in governmental applications
Summary and Next Steps
Requirements
- A fundamental understanding of Artificial Intelligence and Machine Learning for government applications.
- Proficiency in Python programming, a key skill for developing robust solutions for government use.
- Experience with Natural Language Processing concepts to enhance data analysis and communication for government projects.
Audience
- Data scientists working on public sector initiatives.
- Machine learning engineers focused on government projects.
- AI researchers and developers contributing to government solutions.
- Product managers and UX designers ensuring user-friendly and effective government services.
14 Hours
