This paper introduces a new method to maximize Central Processing Units (CPUs) through the use of a micro-containerization concept. The proposed approach theoretically dissects the CPU cores into isolated, efficient processing units called 'micro containers', making an effort to simulate GPU capabilities for parallel processing.

This paper argues against the rhetoric of unlimited AI potential and looks at the serious constraints currently facing models of AI and machine learning, particularly the finitude of the data on which they rely.

We propose Retrieval-Native Language Models (RLLMs), a new paradigm that treats vector-based memory as a first-class component of the model with a unified architecture featuring three channels of knowledge.

Analyzes the relationship between rising global temperatures and aviation efficiency, presenting evidence of technological adaptation outpacing climate impacts over a three-decade period.