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fundamental AGI

putting the fun into a fundamental augmented general intelligence framework as funAGI funAGI is a development branch of easyAGI. easyAGI was not being easy and SimpleMind neural network was proving to not be simple. For that reason is was necessary to remove reasoning.py and take easyAGI back to its roots of BDI Socratic Reasoning from belief, desire and intention. So this back to basics release should be taken as a verbose logging audit of SocraticReasoning […]

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SimpleMind

blueprint for a SimpleMind Using easyAGI

Abstract: This article conceptualizes the creation of an advanced Autonomous General Intelligence (AGI) system, named “easyAGI,” integrating several cutting-edge AI components. Theoretical in nature, this blueprint outlines the essential modules required to construct such a system, emphasizing the principles behind each component without delving into implementation specifics. Introduction: The pursuit of AGI aims to create a machine capable of understanding, learning, and performing intellectual tasks across various domains, akin to human cognitive abilities. The easyAGI […]

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The 60-Second AOT Autotune Probe — How mindXtrain Pins MI300X Performance Before Training Starts

Day 2 of the AMD × lablab.ai Developer Hackathon. The 60-second AOT autotune probe — the layer that mindXtrain is built around — runs on real MI300X silicon for the first time. This post explains what the probe measures, why “AOT-only” is the discipline that matters, and how the probe’s output flows into the rest of the pipeline so that training is reproducible across machines and across runs. 1. What the probe is, and what […]

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