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    Emergence III
    Emergence III
    Emergence III
    Ebook81 pages46 minutes

    Emergence III

    By Larry Matthews

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    About this ebook

    In "Emergence III: Unified Theories," the journey into artificial intelligence's future continues, as it explores groundbreaking frameworks that could one day enable AI to think, learn, and perceive like humans. This book is a visionary piece, synthesizing the Value Assessment Computational Framework (VACF), the Multisensory Computational Framework (MSCF), and the Repetition Duration Intensity (RDI) System into a cohesive narrative.

    Readers will be taken on an intellectual odyssey, discovering how these frameworks can lead to AI that's more intuitive, responsive, and intelligent. "Emergence III" is more than a book; it's a roadmap to the future, illustrating the practical applications of these theories in fields ranging from healthcare to finance.

    Whether you're a scholar, a practitioner, or simply a tech enthusiast, "Emergence III" offers both the stimulus for intellectual curiosity and the practical knowledge to grasp the untapped potentials of AI. Join us in paving the way for the next wave of computational innovation.

    LanguageEnglish
    PublisherLarry Matthews
    Release dateOct 15, 2023
    ISBN9798223656920
    Emergence III

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      Book preview

      Emergence III - Larry Matthews

      CHAPTER 1

      A COMPUTATIONAL FRAMEWORK FOR STUDYING UNCONSCIOUS BIASES

      "How can we computationally study human biases?"

      Unconscious biases have been an object of extensive study in psychology, largely because of their pervasive impact on human decision-making and behavior. The Implicit Association Test (IAT), developed by researchers at Harvard University in 1998, has become a widely used tool for measuring such biases (Greenwald, McGhee, Schwartz, 1998). It reveals how individuals associate positive or negative attributes with certain social groups, even when they may consciously reject such biases. Applying IAT in various societal contexts, such as healthcare, academia, and law enforcement, reveals the grim consequences of biased behavior. For example, recent research by Lai suggests that implicit biases exist and significantly influence people’s actions, even overriding their explicit beliefs and intentions (Lai et al., 2014).

      In addition to individual biases, researchers like Clara Wilkins have delved into how biases are embedded and manifested within groups, particularly in hierarchical structures where power differentials exist (Wilkins, 2017). Wilkins’ research illustrates how certain beliefs can lead to discriminatory actions, especially when the existing social hierarchy is perceived as being threatened.

      Mathematical Modeling of Bias

      Understanding bias in human cognition and decision-making often involves complex, multivariate analyses. While qualitative research provides insightful narratives, a quantitative approach, such as Bayesian statistical models, can offer rigorous ways to identify and measure biases (Gelman et al., 2013). Mathematically, one might represent a generic bias in decision-making as follows:

      Here, Bias i is the bias exerted in the ith decision-making context, F ij represents different features or variables influencing that context, and w j are the weights representing the importance of each feature. These weights can be positive or negative, and their absolute value indicates the strength of the feature’s influence.

      Translating Human Bias into AI Systems

      Our aim in this book is to develop a computational framework that allows for the encoding of biases or preferences in AI systems, analogous to unconscious biases in human cognition. Though AI systems don’t possess cognition, understanding human biases provides invaluable insights for developing algorithms that can simulate preference-based behavior. This can allow AI systems to develop traits and characteristics, as well as preferences that enable them to favor one thing over another. For instance, a recommendation algorithm can be designed to favor newer content over older content, without requiring explicit rules to do so.

      Toward a Computational Framework

      With this context, our goal is to construct algorithms that can capture, analyze, and even adopt biases or preferences, with potential applications ranging from machine learning models in natural language processing to decision-making algorithms in autonomous systems. It’s important to note that this book will not delve into the ethical implications of introducing bias into AI systems, but rather focus on the technical framework for achieving this.

      We will employ statistical methods, machine learning algorithms, and other computational techniques to translate the study of human bias into the realm of AI. Future chapters will elaborate on these methods and offer examples of how to implement them.

      By linking the study of unconscious biases in human behavior with computational methods, this book seeks to lay the groundwork for a novel, interdisciplinary approach to understanding and developing biased or preference-based AI systems. This introductory chapter serves as a foundation, upon which the ensuing chapters will build to create this comprehensive framework.

      CHAPTER 2

      ABSTRACTION MECHANISMS IN AI SYSTEMS

      "How do AI systems simplify complex information?"

      The categorization process is an indispensable part of human cognition, aiding us in organizing and simplifying our complex environment. This capability is not exclusive to humans; even mice have been shown to exhibit categorization skills, as research from the Max Planck Institute of Neurobiology demonstrates (Reinert et al., 2021). Researchers have identified neurons that are specialized in storing categories, an example of how abstract information can be encoded at the neuronal level. This biological form of abstraction is essential,

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