论文标题
神经组成计算：从认知的中心悖论到新一代AI系统
Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems
论文作者
论文摘要
是什么解释了从20世纪到21世纪AI的急剧进步，如何克服当前AI的其余局限性？广泛接受的叙事归因于这一进步归因于可用的计算和数据资源的数量，可用于支持深人造神经网络中的统计学习。我们表明，另一个至关重要的因素是开发新型计算。神经组成计算采用了两种必须同时尊重的原则，以实现人类水平的认知：组成性和连续性的原理。这些似乎是不可调和的，直到最近的数学发现是，不仅可以通过符号计算的离散方法来实现构图，还可以通过新型的连续神经计算形式来实现。 AI的最新进展是由于使用有限形式的神经组成计算而产生的。神经组件计算的新形式更深，创建了更健壮，准确和可理解的AI系统。
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.
