MASTERMIND

MASTERMIND

Here are some key aspects of MASTERMIND:

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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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graphRAGE: knowledge as a graph of linked, retrievable nodes rather than one central store

cryptoAGI and the Emergence of Distributed Knowledge

cryptoAGI began in 2024 as a promise to decentralise intelligence. In 2026 it is a shelf of public, checkable parts: an engine, an officer, a voiceprint, a runtime. Knowledge that no one owns and anyone can verify. The rest reveals itself when ready.

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aGLM with enhanced RAGE from MASTERMIND

aGLM, or Autonomous General Learning Model, is a sophisticated machine learning model that integrates aspects of both supervised and unsupervised learning to analyze and interpret data across various applications like natural language processing, image recognition, and financial forecasting. This model is designed to efficiently handle large volumes of data and is particularly effective as a foundational tool for building more complex models. Key features of aGLM include: Dynamic Learning: aGLM can process and learn from […]

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