Course Outline

Introduction to Conversational AI and Small Language Models (SLMs)

  • Fundamentals of conversational AI
  • Overview of SLMs and their advantages
  • Case studies of SLMs in interactive applications

Designing Conversational Flows

  • Principles of human-AI interaction design
  • Crafting engaging and natural dialogues
  • User experience (UX) considerations

Building Customer Service Bots

  • Use cases for customer service bots
  • Integrating SLMs into customer service platforms
  • Handling common customer inquiries with AI

Training SLMs for Interaction

  • Data collection for conversational AI
  • Training techniques for SLMs in dialogue systems
  • Fine-tuning models for specific interaction scenarios

Evaluating Interaction Quality

  • Metrics for assessing conversational AI
  • User testing and feedback collection
  • Iterative improvement based on evaluation

Voice-Enabled and Multimodal Interactions

  • Incorporating voice recognition with SLMs
  • Designing multimodal interactions (text, voice, visuals)
  • Case studies of voice assistants and chatbots

Personalization and Contextual Understanding

  • Techniques for personalizing interactions
  • Context-aware conversation handling
  • Privacy and data security in personalized AI

Ethical Considerations and Bias Mitigation

  • Ethical frameworks for conversational AI
  • Identifying and mitigating biases in interactions
  • Ensuring inclusivity and fairness in AI communication

Deployment and Scaling

  • Strategies for deploying conversational AI systems
  • Scaling SLMs for widespread use
  • Monitoring and maintaining AI interactions post-deployment

Capstone Project

  • Identifying a need for conversational AI in a chosen domain
  • Developing a prototype using SLMs
  • Testing and presenting the interactive application

Final Assessment

  • Submission of a capstone project report
  • Demonstration of a functional conversational AI system
  • Evaluation based on innovation, user engagement, and technical execution

Summary and Next Steps

Requirements

  • Basic understanding of Artificial Intelligence and Machine Learning
  • Proficiency in Python programming
  • Experience with Natural Language Processing concepts

Audience

  • Data scientists
  • Machine learning engineers
  • AI researchers and developers
  • Product managers and UX designers
 14 Hours

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