If you are interested, take a look at our abstracts which will be released on Google Scholar sometime in the near future.

Abstract 1:

Predicting Future Events Through Mined Web Text Data

The ever-expanding digital footprint of human activity provides a vast repository of textual data that reflects trends, behaviors, and emergent societal patterns. This research introduces an innovative approach to predicting future events by mining and analyzing web-based textual data using a bespoke algorithm. Inspired by earlier predictive projects, our system leverages natural language processing (NLP) and large language models (LLMs) to identify latent signals in online content. By analyzing recurring patterns, sentiment trajectories, and contextual relationships in language, we aim to uncover predictive markers for significant global events across various domains, including politics, economics, and social behavior.

Our unique algorithm advances prior methodologies by integrating probabilistic modeling with temporal context analysis to refine the accuracy of event prediction. We also address key challenges such as distinguishing meaningful signals from noise, adapting to evolving linguistic trends, and accounting for the dynamic nature of internet lexicons. This study not only enhances our understanding of collective online consciousness but also provides a framework for leveraging web-based textual data as a tool for foresight in the digital age.

Abstract 2:

Exploring Causality and Conscious Influence in the Vibratory Universe Through NLP
This research investigates the hypothesis that the universe operates as an electric and vibratory system, where sound, as a frequency carrier, interacts with the magnetosphere. We posit that human thought, intention, and language—expressed through a lexicon—may directly influence the magnetosphere, potentially impacting the temporal and spatial outcomes within the observed universe. Our work explores whether specific word frequencies and linguistic constructs, encoded with intention, can shape reality by aligning with the vibratory patterns of the magnetosphere.

Central to this study is the development of a novel NLP-based model that mines large-scale textual datasets to extract and measure intention-driven language patterns. By correlating these patterns with future events, we aim to determine whether linguistic intention serves as a causal mechanism in shaping outcomes on a quantum and macro scale. The model employs advanced algorithms inspired by early predictive systems, integrating them with a modern understanding of time-space dynamics and causality in language.

This research contributes to the growing exploration of consciousness as an interactive force in the universe, seeking to bridge the gap between linguistics, vibratory physics, and causal models of reality creation. If successful, our findings could have profound implications for how humanity understands its role in shaping future timelines and influencing the broader vibratory field of existence.