Advancing AI Understanding
Peter and Kyle discuss the importance of knowledge bases and reasoning in AI systems, highlighting the challenge of question interpretation. They explore iterative processes in algorithms to enhance understanding and the need for multi-hop reasoning for more accurate answers.In this clip
From this podcast
Data Skeptic
The Imitation Game
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?
How does relevance realization relate to AI?
Is using AI in learning and to fetch and summarize information for purposes of learning the best way to use it?