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Course Outline
Introduction to Conversational AI and Small Language Models (SLMs)
- Core fundamentals of conversational AI
- Overview of SLMs and their unique advantages
- Analysis of case studies featuring SLMs in interactive applications
Designing Conversational Flows
- Key principles of human-AI interaction design
- Crafting natural and engaging dialogue structures
- Addressing User Experience (UX) considerations
Building Customer Service Bots
- Exploring use cases for customer service automation
- Integrating SLMs into existing customer service platforms
- Managing common customer inquiries using AI capabilities
Training SLMs for Interaction
- Best practices for data collection in conversational AI
- Training techniques for SLMs within dialogue systems
- Fine-tuning models for specific interaction scenarios
Evaluating Interaction Quality
- Selecting appropriate metrics for assessing conversational AI
- Conducting user testing and gathering feedback
- Implementing iterative improvements based on evaluation results
Voice-Enabled and Multimodal Interactions
- Combining voice recognition with SLMs
- Designing multimodal interactions involving text, voice, and visuals
- Reviewing case studies of voice assistants and chatbots
Personalization and Contextual Understanding
- Techniques for personalizing user interactions
- Managing context-aware conversation flows
- Ensuring privacy and data security in personalized AI systems
Ethical Considerations and Bias Mitigation
- Applying ethical frameworks to conversational AI
- Identifying and mitigating biases in AI interactions
- Promoting inclusivity and fairness in AI communication
Deployment and Scaling
- Strategies for successfully deploying conversational AI systems
- Scaling SLMs for broad and widespread usage
- Monitoring and maintaining AI interactions post-deployment
Capstone Project
- Identifying a specific need for conversational AI within a chosen domain
- Developing a functional prototype using SLMs
- Testing and presenting the interactive application
Final Assessment
- Submission of a comprehensive capstone project report
- Live demonstration of a working conversational AI system
- Evaluation based on innovation, user engagement, and technical execution
Summary and Next Steps
Requirements
- A foundational understanding of Artificial Intelligence and Machine Learning concepts
- Proficiency in Python programming
- Familiarity with Natural Language Processing (NLP) principles
Target Audience
- Data scientists
- Machine learning engineers
- AI researchers and developers
- Product managers and UX designers
14 Hours