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Course Outline

Foundations of Conversational AI and Small Language Models (SLMs)

  • Core mechanics of conversational AI
  • Overview of SLMs and their operational benefits
  • Real-world case studies of SLMs in interactive systems

Architecting Conversational Flows

  • Key principles in human-AI interaction design
  • Constructing natural and engaging dialogue structures
  • Integrating User Experience (UX) best practices

Developing Customer Service Bots

  • Practical applications for customer support automation
  • Embedding SLMs into existing service platforms
  • Managing standard customer queries with AI assistance

Training SLMs for Dynamic Interaction

  • Sourcing and preparing data for conversational models
  • Advanced training methodologies for dialogue systems
  • Fine-tuning models for specific interaction contexts

Assessing Interaction Quality

  • Defining key metrics for evaluating conversational AI
  • Conducting user testing and gathering qualitative feedback
  • Iterating models based on performance evaluation

Voice and Multimodal Interactions

  • Integrating voice recognition capabilities with SLMs
  • Designing multimodal experiences (text, audio, visual)
  • Analysis of voice assistants and advanced chatbots

Personalization and Context Awareness

  • Methods for tailoring AI interactions to individual users
  • Handling context-dependent conversation flows
  • Addressing privacy and data security in personalized AI

Ethics and Bias Mitigation

  • Ethical frameworks for responsible conversational AI
  • Detecting and reducing algorithmic bias in dialogues
  • Promoting fairness and inclusivity in AI communication

Deployment and Scalability

  • Best practices for launching conversational AI systems
  • Scaling SLM infrastructure for broad user access
  • Post-launch monitoring and maintenance of AI interactions

Capstone Project

  • Identifying a specific conversational AI need in a selected domain
  • Building a functional prototype using SLMs
  • Testing and showcasing the interactive application

Final Evaluation

  • Submission of the capstone project documentation
  • Live demonstration of the conversational AI system
  • Assessment focused on innovation, engagement, and technical quality

Summary and Future Directions

Requirements

  • Foundational knowledge of Artificial Intelligence and Machine Learning concepts
  • Strong 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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