AI for Semantic Understanding, Contextual AI, and NLU

• Experience in natural language processing (NLP)
• Basic understanding of machine learning and AI concepts

Audience

• NLP researchers
• AI specialists
• Machine learning engineers

This course focuses on advanced models for language comprehension, including transformers, pre-trained language models, and their role in understanding semantics and context in natural language. It delves into the principles of Natural Language Understanding (NLU) and its applications in advanced AI systems.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to explore the cutting-edge techniques in NLU and semantic understanding.

By the end of this training, participants will be able to:

• Understand the principles of semantic understanding and contextual AI.
• Work with state-of-the-art models like transformers and pre-trained language models.
• Implement NLU techniques for improved AI language comprehension.
• Develop applications that leverage semantic analysis and contextual AI.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Semantic Understanding and Contextual AI

• Overview of NLU and its role in AI
• Semantic understanding in AI systems
• Contextual AI and its applications

Advanced Models for NLU

• Transformers and their architecture
• Pre-trained models: BERT, GPT, T5
• Fine-tuning models for semantic understanding

Contextual AI Techniques

• Understanding context in language processing
• Contextual embedding techniques
• Applications of contextual AI in real-world scenarios

Semantic Analysis in AI

• Techniques for semantic parsing
• Using AI to understand meaning and intent
• Challenges in semantic analysis

NLU Applications in AI Systems

• Improving chatbot interactions with semantic understanding
• AI systems for language translation and summarization
• Sentiment analysis and intent recognition in NLU

Ethical Considerations and Challenges in NLU

• Bias in language models and semantic understanding
• Ethical issues in deploying contextual AI
• Addressing limitations in NLU systems

Future Directions in Semantic Understanding and Contextual AI

• Emerging trends in NLU research
• Advances in deep learning for contextual AI
• Building more sophisticated and interpretable NLU models

Summary and Next Steps