LF Yadda

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  • Intelligence as Pattern Recognition, Recreation, and Expression: From Large Language Models to Biological Cells and Hurricanes – grok

    Intelligence as Pattern Recognition, Recreation, and Expression: From Large Language Models to Biological Cells and Hurricanes Introduction Intelligence is often narrowly associated with human cognition, encompassing abilities like reasoning, problem-solving, and self-awareness. However, a broader perspective reveals that intelligence manifests in diverse systems, from artificial constructs to biological entities and natural phenomena. This paper proposes…

  • Intelligence as Pattern Dynamics: From Neural Networks to Natural Systems – claude

    Abstract Intelligence, traditionally viewed as a uniquely biological or computational phenomenon, may be better understood as an emergent property arising from three fundamental processes: pattern recognition, pattern recreation, and pattern expression. This paper explores how these pattern dynamics manifest across a spectrum of systems, from large language models and neural networks to biological cells and…

  • link to my claude generated map

    https://claude.ai/public/artifacts/daa8df40-4067-404f-80a7-eef9efd81139

  • comparing two of my essays and the levin preprint

    How the three works tackle the question “What counts as life?” Aspect LF Yadda #1 – Beyond Biological Boundaries LF Yadda #2 – Beyond Two Dimensions Bender et al. – What Lives? Primary goal Build a universal “life-space” that can hold cells, AIs, economies, memes—any entity showing life-like behaviour. (LF Yadda – A Blog About…

  • Multidimensional Life Entity Embedding Framework

    Entity Identification Hierarchy Primary Entity Categories 1. Biological Entities 2. Artificial Entities 3. Social Entities 4. Hybrid Entities 5. Abstract Entities Core Dimensional Properties 1. Information Processing and Response (IPR) Measurement Components: Quantification Methods: 2. Self-Organization and Emergent Complexity (SOEC) Measurement Components: Quantification Methods: 3. Adaptive Learning and Evolution (ALE) Measurement Components: Quantification Methods: 4.…

  • training requirements

    Below, each lifecycle stage keeps the plain-English goal, but the “What it really means you must do” entry is now roughly 100 words, giving you a clearer picture of the hands-on work. Step Plain-English goal What it really means you must do (≈100 words) 1. Collect open data Give the model a rich, accurate diet.…