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Duration 21 hours
Course Outline
Introduction to AI-Powered Natural Language Generation (NLG)
- Fundamentals of Natural Language Generation (NLG)
- The function of NLG within conversational AI architectures
- Distinctions between Natural Language Understanding (NLU) and NLG
Deep Learning Methodologies for NLG
- Transformer architectures and pre-trained language models
- Training models for robust dialogue generation
- Managing long-term dependencies in conversational flows
Chatbot Frameworks and NLG Integration
- Incorporating NLG capabilities into chatbot platforms (e.g., Rasa, BotPress)
- Creating personalized responses for chatbots
- Enhancing user engagement through contextual AI
Advanced NLG Models for Virtual Assistants
- Utilization of GPT-3, BERT, and other leading-edge models
- Generating multi-turn dialogues with AI
- Refining fluency and naturalness in virtual assistant outputs
Ethical and Operational Considerations
- Addressing bias in AI-generated content and mitigation strategies
- Ensuring transparency and trustworthiness in chatbot interactions
- Privacy and security standards for virtual assistants
Evaluation and Optimization of NLG Systems
- Assessing NLG quality via BLEU, ROUGE, and human evaluation
- Tuning and optimizing NLG performance for real-time operations
- Adapting NLG for domain-specific use cases
Future Trends in NLG and Conversational AI
- Emerging techniques in self-supervised learning for NLG
- Leveraging multimodal AI for enhanced interactive conversations
- Progress in context-aware conversational AI
Summary and Next Steps
Requirements
- A strong foundation in Natural Language Processing (NLP) concepts
- Experience with machine learning and AI model development
- Proficiency in Python programming
Audience
- AI developers
- Chatbot designers
- Virtual assistant engineers
