understandin the ezAGI framework requires a fundamental comprehension of reasoning with SocraticReasoning.py disclaimer: ezAGI fundamental Augmented Generative Intelligence may or not be be fun. use at own risk. breaking changes version 1 To fully audit the behavior of how the premise field is populated in the SocraticReasoning class, we will: SocraticReasoning.py Audit Initialization and setup of SocraticReasoning class Adding Premises Programmatically Adding Premises Interactively Now, let’s look at the interactive part of the interact method: […]

aGLM, or Autonomous General Learning Model, is designed to operate as a core model for autonomous data parsing and learning from memory in the context of artificial intelligence systems. It’s a pivotal element within a broader system called RAGE (Retrieval Augmented Generative Engine). Key aspects and functionalities of aGLM: Autonomous Learning: aGLM is built to learn autonomously from interactions and data retrievals. It continuously updates its knowledge base, refining its capabilities based on new data […]

together.ai provides a cloud environment playground for a number of LLM including Mixtral8x7Bv1. This model was chosen for the 32k ++ context window and suitable point of departure dataset for deployment of aGLM Autonomous General Learning Model. aGLM design goals include RAGE with MASTERMIND controller for logic and reasoning. The following three screenshots show the first use of aGLM recognising aGLM and MASTERMIND RAGE components to include machine.dreaming and knowledge as THOT from aGLM parse. […]