What explains the dramatic progress from 20th-century to 21st-century AI, and how can the remaining limitations of current AI be overcome? The widely accepted narrative attributes this progress to massive increases in the quantity of computational and data resources available to support statistical learning in deep artificial neural networks. We show that an additional crucial factor is the development of a new type of computation. Neurocompositional computing adopts two principles that must be simultaneously respected to enable human-level cognition: the principles of Compositionality and Continuity. These have seemed irreconcilable until the recent mathematical discovery that compositionality can be realized not only through discrete methods of symbolic computing, but also through novel forms of continuous neural computing. The revolutionary recent progress in AI has resulted from the use of limited forms of neurocompositional computing. New, deeper forms of neurocompositional computing create AI systems that are more robust, accurate, and comprehensible.
翻译:20世纪到21世纪的人工智能突飞猛进,如何克服当前人工智能的剩余限制? 广泛接受的叙述将这一进展归因于可用于支持深层人工神经网络统计学习的计算和数据资源的大量增加。 我们表明,另一个关键因素是新类型计算的发展。 内分层计算采用了两个必须同时得到尊重的原则,以使人类层面的认知得以实现:组成性和连续性原则。 直到最近数学发现不仅通过非同寻常的模拟计算方法,而且通过新型的连续神经计算,组成性也能够实现。 人工智能最近革命性的进展是使用有限形式的神经组合计算的结果。 新的、更深层的神经组合计算形式创造了更加健全、准确和易懂的AI系统。