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 Duration 14 hours

Course Outline

Foundations of Conversational AI and Small Language Models (SLMs)

  • Core concepts in conversational AI
  • An introduction to SLMs and their key benefits
  • Real-world examples of SLMs in interactive contexts

Designing Dialogue Flows

  • Guidelines for human-AI interaction design
  • Creating compelling and natural conversations
  • Key aspects of user experience (UX)

Constructing Customer Service Bots

  • Practical applications for customer service bots
  • Embedding SLMs into customer service ecosystems
  • Addressing frequent customer queries with AI assistance

Training SLMs for Engagement

  • Gathering data for conversational AI systems
  • Training methodologies for SLMs in dialogue frameworks
  • Fine-tuning models for distinct interaction scenarios

Assessing Interaction Quality

  • Indicators for evaluating conversational AI performance
  • Conducting user tests and gathering feedback
  • Continuous refinement based on assessment results

Voice-Driven and Multimodal Interactions

  • Integrating voice recognition with SLMs
  • Designing interactions across multiple modalities (text, audio, visual)
  • Case studies of voice assistants and chatbots

Personalization and Contextual Awareness

  • Methods for tailoring interactions to individual users
  • Managing conversations with contextual sensitivity
  • Data privacy and security in personalized AI

Ethical Implications and Bias Reduction

  • Ethical principles guiding conversational AI
  • Detecting and reducing biases in interactions
  • Promoting inclusivity and fairness in AI communication

Deployment and Expansion

  • Approaches to deploying conversational AI architectures
  • Scaling SLMs for broad adoption
  • Overseeing and maintaining AI interactions after launch

Capstone Project

  • Identifying a conversational AI requirement within a specific field
  • Creating a prototype leveraging SLMs
  • Testing and showcasing the interactive application

Final Evaluation

  • Submission of the capstone project documentation
  • Demonstration of a working conversational AI system
  • Assessment criteria including innovation, user engagement, and technical delivery

Concluding Summary and Future Directions

Requirements

  • Foundational knowledge of Artificial Intelligence and Machine Learning
  • Strong command of Python programming
  • Familiarity with Natural Language Processing principles

Target Audience

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

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