MASTERMIND

MASTERMIND

Here are some key aspects of MASTERMIND:

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aGLM

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 […]

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Symbolic Logic

LogicTables Class: Managing Logic and Beliefs

The LogicTables class in logic.py is designed to handle logical expressions, evaluate their truth values, and manage beliefs as valid truths. It integrates with the SimpleMInd or similar neural network system to process and use truths effectively. Key Features: Initialization and Logging The LogicTables class initializes with logging configuration to capture debug information: Adding Variables and Expressions Truth tables are generated to evaluate logical expressions: Expressions are evaluated using logical operators: def evaluate_expression(self, expr, values):allowed_operators […]

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MASTERMIND aGLM with RAGE

Building a rational Autonomous General Learning Model with Retrieval Augmented Generative Engine to create a dynamic learning loop with machine.dreaming for machine.learning as a self-healing architecture. MASTERMIND uses the Autonomous General Learning Model (aGLM) enhanced by the Retrieval Augmented Generative Engine (RAGE) to create a sophisticated AI system capable of intelligent decision-making and dynamic adaptation to real-time data. This combination leverages the strengths of both components to ensure that responses are not only based on […]

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