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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