理论证明无限制AI终将以概率1超越人类智能
Artificial Intelligence without Restriction Surpassing Human Intelligence with Probability One: Theoretical Insight into Secrets of the Brain with AI Twins of the Brain
- 构建脑智能孪生模型,实现对大脑功能的高精度逼近
- 证明无约束AI可概率性超越人类智能,支持罗森布拉特猜想
- 为神经科学与低能耗可解释AI提供新范式
人工智能(AI)已成为人类历史上最重要的技术之一,而人脑则被公认为宇宙中最复杂的系统之一。一个关乎人类存续的根本问题仍未解决:人工智能未来能否超越人类智能?本文从理论上证明,通过新型细胞级AI技术构建的脑智能孪生体,可对大脑及其感知、认知等功能系统以任意小误差逼近;且无限制的AI最终将以概率1超越人类智能。该结果间接验证了弗兰克·罗森布拉特70年前关于人工智能潜力的猜想,尤其在人工神经网络领域。智能是自然偶然但精妙的产物,尚未被完全揭示。如同数学与物理,无限制的人工智能将发展为具有自洽体系与原则的新学科。本文展望其推动:1)脑智能孪生用于细胞级神经动态与功能分析及脑疾病解决方案;2)全球跨学科团队协同建模各类神经元、突触及脑功能子系统;3)借助基础神经科学特性发展低功耗AI技术;4)实现可控、可解释、安全且具备自然规律发现能力的推理型AI。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) has apparently become one of the most important techniques discovered by humans in history while the human brain is widely recognized as one of the most complex systems in the universe. One fundamental critical question which would affect human sustainability remains open: Will artificial intelligence (AI) evolve to surpass human intelligence in the future? This paper shows that in theory new AI twins with fresh cellular level of AI techniques for neuroscience could approximate the brain and its functioning systems (e.g. perception and cognition functions) with any expected small error and AI without restrictions could surpass human intelligence with probability one in the end. This paper indirectly proves the validity of the conjecture made by Frank Rosenblatt 70 years ago about the potential capabilities of AI, especially in the realm of artificial neural networks. Intelligence is just one of fortuitous but sophisticated creations of the nature which has not been fully discovered. Like mathematics and physics, with no restrictions artificial intelligence would lead to a new subject with its self-contained systems and principles. We anticipate that this paper opens new doors for 1) AI twins and other AI techniques to be used in cellular level of efficient neuroscience dynamic analysis, functioning analysis of the brain and brain illness solutions; 2) new worldwide collaborative scheme for interdisciplinary teams concurrently working on and modelling different types of neurons and synapses and different level of functioning subsystems of the brain with AI techniques; 3) development of low energy of AI techniques with the aid of fundamental neuroscience properties; and 4) new controllable, explainable and safe AI techniques with reasoning capabilities of discovering principles in nature.
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