Autonomous General Learning Model

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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SimpleMind

SimpleMind: A Neural Network Implementation in JAX

The SimpleMind class is a powerful yet straightforward implementation of a neural network in JAX. It supports various activation functions, optimizers, and regularization techniques, making it versatile for different machine learning tasks. With parallel backpropagation and detailed logging, it provides an efficient and transparent framework for neural network training.

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easyAGI: Augmenting the Intelligence of Large Language Models

easy augmented general intelligence In the rapidly evolving field of artificial intelligence, the concept of Autonomous General Intelligence (AGI) represents a significant milestone. However, the journey towards AGI is complex and requires innovative approaches to streamline and simplify the development process. Enter easyAGI, a transformative framework designed to augment the intelligence of existing Large Language Models (LLMs). This article explores the core aspects of easyAGI and its impact on the landscape of AGI and LLMs. […]

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