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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

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