Knowledge Graph States
Debajyoti discusses how the states of a knowledge graph can extend beyond human interpretable formats. He highlights the potential of using middle layers of autoencoders to inform these states, emphasizing the versatility and depth of data representation in machine learning.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
AI for Content Creation with Debajyoti Ray - TWiML Talk #178
Related Questions
I'm thinking that classical or deep ML solutions have a flaw in that they cannot be extended. For example, if one builds a model and wants to introduce a new feature, typically the model has to be retrained from scratch. So, I'm considering building a Knowledge Graph using LLMs. This knowledge graph would have to include time-dependent data (for example, it should be able to retrieve the "current" President of the USA and also previous presidents if asked). I'm thinking this Knowledge Graph could be used for Retrieval Augmented Generation (RAG) to help with business goals or maybe used with more clever User Interfaces (UIs). I'm not sure how to build or populate this KG and also have a rough idea of how to use it. Can you help me with suggesting particular paths for building and populating this Knowledge Graph?
What is a knowledge graph?