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首页> 外文会议>IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion >Awareness without Neural Networks: Achieving Self-Aware AI via Evolutionary and Adversarial Processes
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Awareness without Neural Networks: Achieving Self-Aware AI via Evolutionary and Adversarial Processes

机译：没有神经网络的意识：通过进化和对抗过程实现自我意识的人工智能

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摘要

A key difficulty in achieving self-aware artificial intelligence (AI) is the achievement of epistemological knowledge, i.e. a machine that “knows what it knows” and “knows what it does not know” with respect to some model of itself or its surroundings. Given a nonlinear dynamical system with known algebraic structure expressible as differential equations, with sensors able to create a time-series of measurements of sufficient variables to create a suitable partial state vector, then novel forms of evolutionary machine learning and adversarial processes are sufficient to create a form of AI that is “aware” of its knowledge set regarding this system, and can use a form of differential Game Theory and adversarial processes to “think” about its knowledge set to address ambiguities and achieve objectives, including moving beyond its original training data. This may itself constitute a form of “self-awareness”. Results from successful use of these techniques in medical and engineering problems are outlined. This AI architecture does not involve neural networks or their derivative architectures, but instead is inspired by evolutionary ecosystems. Implications for self-aware operating systems are discussed.
机译：实现自我意识的人工智能（AI）的关键困难是认识论知识的获得，即，关于自身或周围环境的某种模型，“知道其所知道的”和“知道其所不知道的”的机器。给定具有表示为微分方程式的已知代数结构的非线性动力学系统，并且传感器能够创建足够变量的时间序列测量结果以创建合适的部分状态向量，那么进化机器学习和对抗过程的新颖形式就足以创建一种“意识到”其关于该系统的知识集的人工智能形式，并且可以使用差分博弈论和对抗过程的形式来“思考”其知识集，以解决歧义并实现目标，包括超越其原始训练数据。这本身可能构成“自我意识”的一种形式。概述了在医疗和工程问题中成功使用这些技术的结果。这种AI体系结构不涉及神经网络或其派生体系结构，而是受到进化生态系统的启发。讨论了对自我意识操作系统的影响。

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