Vector Embedding using AI
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The document discusses vector embeddings in conversational AI, highlighting their role in converting words into numerical vectors for better machine understanding. It covers the importance of embeddings for enhancing personalized interactions and the challenges faced, such as handling out-of-vocabulary words and biases. The presentation concludes that while vector embeddings are vital for modern AI applications, ongoing evolution is necessary to address existing challenges.
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Vector Embedding using AI
- 1. Vector Embeddings within Conversational AI wednesday.is Presented by: Saurabh Suman
- 2. 2 Content What we’ll be covering Introduction to Vector Embeddings Why Embeddings? Importance in ConversationalAI Challenges Conclusion & Q&A
- 3. wednesday.is Introduction to Vector Embeddings Representing words as numbers Converting words/phrases into vectors Machines understand numbers, not words
- 4. wednesday.is Why Embeddings? Text is vast and deep Machines find it challenging to navigate, embeddings act as a compass Enables mathematical operations on words
- 7. wednesday.is Importance in ConversationalAI Enhanced Understanding Personalised Interaction Seamless Conversations Conversing naturally with users
- 8. wednesday.is Types of Embeddings Word2Vec GloVe FastText BERT & Transformers
- 11. wednesday.is Challenges Handling Out-of-Vocabulary Words Cultural and Social Biases Language Nuances and Idioms Resource Constraints for Lesser-Known Languages Dynamic Meanings Scalability and Efficiency Interoperability
- 12. wednesday.is Conclusion & Q&A Vector embeddings: The language of machines. Pivotal in enhancingAI communication and understanding. Diverse techniques, each with unique strengths. Central to modernAI applications, yet challenges remain. Apromising future with continuous evolution.
- 13. Thank you for your time! wednesday.is