Snowy Mountains

Towards Universal Applied Supervised Machine Learning: A Multi-Agent Framework For Systematic Pipeline Executions

This paper is the base for an open-source library designed to implement the principles outlined in our research. The library, MADS, will allow data scientists and machine learning engineers to develop and implement data pipelines in a more efficient and collaborative way, including functionalities for the integration and management of multiple agents, where it optimizes the execution of complex tasks in data science.

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Desert

The Future of Destruction: Analyzing the Potential Impact of Entropy on Technological Evolution in AGI, Autonomous Drones and Spplied Robotics

Exploring the intersection of Artificial General Intelligence (AGI), autonomous drones and applied robotics, highlighting their transformative potential across sectors while addressing critical ethical concerns, particularly in warfare.

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