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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