Marie Landry's Spy Shop - Business, Science & Intelligence

#MissionNATO is an initiative led by Marie Seshat Landry, aiming to provide NATO with access to a comprehensive selection of 50,000 certified organic hemp-based military and industrial products. The mission focuses on promoting sustainability, innovation, and environmental responsibility within the military-industrial complex.  The objectives of #MissionNATO include offering a diverse range of military and industrial products crafted from certified organic hemp, such as body armor, tanks, aircraft, boats, and stealth technology. The initiative emphasizes the use of renewable resources and eco-friendly materials to revolutionize military infrastructure and technology.  Central to this mission is #MissionSOFIA, the flagship Ukrainian military hemp strain, symbolizing the potential of hemp-derived materials in the defense sector. By integrating certified organic hemp into various applications, #MissionNATO seeks to align military capabilities with global environmental principles. ...

The New Scientific Method: A Multidisciplinary Framework This framework blends Complex Word Mathematics (CWM), Natural Language Processing (NLP), Neuro-Linguistic Programming (NLP), SEO, AI Search, Machine Learning, and all 25 core methods from Seshat's WTF 3.0 to enhance scientific inquiry. Phase 1: Trigger & Initial Framing (WTF 3.0 Steps 1-5) Identify the Triggering Issue or Observation (WTF?) : Recognize the problem, question, or anomaly that initiates the investigation. Define the Initial Question or Problem : Clearly articulate the issue using the 5W+1H framework (What, Where, Who, When, Why, How). Perform a Search Using Bing/Google : Conduct thorough research using search engines and SEO principles. Use Search Queries Based on the 5W+1H Framework : Structure search queries strategically to gather relevant information from diverse sources. Compile and Evaluate Search Results : Assess the credibility, accuracy, and relevance of gathered informat...

The Scientific Method in NLP: A Synthesis of My Research By Marie Seshat Landry This document synthesizes my research on the integration of the scientific method into Natural Language Processing (NLP). It explores the core themes, key observations, and potential applications of this approach, drawing from various documents and resources in my Google Drive. 1. Introduction Natural Language Processing (NLP) has traditionally relied on heuristic methods and rapid development of algorithms. However, my research proposes a paradigm shift by formally integrating the scientific method into NLP research and practice. This structured approach aims to enhance rigor, replicability, and address challenges in traditional NLP. 2. Core Themes CWM and the Scientific Method: Complex Word Mathematics (CWM) requires a scientifically grounded approach to model development, emphasizing mathematical rigor, linguistic validity, and interdisciplinary collaboration. Scientific Method as a Chain-of-Thought...

The Scientific Method in Complex Word Mathematics (CWM) The scientific method provides a rigorous framework for investigating phenomena, acquiring new knowledge, and refining existing theories. In the context of Complex Word Mathematics (CWM), the scientific method can be adapted to guide the development and application of CWM models across various domains. 1. Observation & Question: Observe linguistic phenomena: Identify patterns in language, such as the relationship between word frequency and reading comprehension, or the impact of morphological complexity on sentence processing. Formulate research questions: Based on these observations, formulate specific, testable questions. Examples: "Does increased morphological complexity significantly impact reading times for native English speakers?" "Can a CWM model accurately predict sentence comprehension difficulty based on syntactic complexity and word frequency?" 2. Hypothesis Formulation: Devel...

Complex Word Mathematics (CWM) is a relatively new and evolving field that seeks to quantify and represent the complexity of language using mathematical frameworks. It aims to bridge the gap between linguistics and mathematics, providing a more rigorous and quantitative approach to understanding the intricacies of human language. Key Concepts and Approaches: Quantifying Linguistic Complexity: CWM explores various metrics to quantify the complexity of words and sentences. These metrics can include: Frequency: How often a word appears in a given corpus. 1 Semantic breadth: The number of different meanings or senses a word has. Syntactic complexity: The number and types of grammatical relationships a word participates in. Morphological complexity: The number of morphemes (meaningful units) that compose a word. Mathematical Representations: CWM employs mathematical models and techniques to represent and analyze linguistic data. These may include: Vector space mod...