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.

通过使用神经组合计算的有限形式，人工智能系统正在变得更加强大、准确和易理解，而新的、更深层次的神经组合计算形式将进一步提高人工智能系统的表现。