Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning. Human perception-inspired machine perception, in the context of AI, refers to large-scale pattern recognition from raw data using neural networks trained using self-supervised learning objectives such as next-word prediction or object recognition. On the other hand, machine cognition encompasses more complex computations, such as using knowledge of the environment to guide reasoning, analogy, and long-term planning. Humans can also control and explain their cognitive functions. This seems to require the retention of symbolic mappings from perception outputs to knowledge about their environment. For example, humans can follow and explain the guidelines and safety constraints driving their decision-making in safety-critical applications such as healthcare, criminal justice, and autonomous driving. This article introduces the rapidly emerging paradigm of Neurosymbolic AI combines neural networks and knowledge-guided symbolic approaches to create more capable and flexible AI systems. These systems have immense potential to advance both algorithm-level (e.g., abstraction, analogy, reasoning) and application-level (e.g., explainable and safety-constrained decision-making) capabilities of AI systems.

翻译：人类通过与环境的互动，结合感知（将感官输入转化为符号）和认知（将符号映射为环境知识以支持抽象、类比推理和长期规划）来运作。在人工智能领域，受人类感知启发的机器感知，指利用神经网络通过自监督学习目标（如下一个词预测或物体识别）从原始数据中进行大规模模式识别。另一方面，机器认知涉及更复杂的计算，例如利用环境知识引导推理、类比和长期规划。人类还能控制并解释自身的认知功能。这似乎需要保留从感知输出到环境知识的符号映射。例如，在医疗、刑事司法和自动驾驶等安全关键应用中，人类能够遵循并解释指导其决策的准则和安全约束。本文介绍了快速兴起的神经符号人工智能范式，该范式将神经网络与知识引导的符号方法相结合，以创建更强大、更灵活的人工智能系统。这类系统在提升人工智能的算法层面能力（如抽象、类比、推理）和应用层面能力（如可解释且受安全约束的决策）方面具有巨大潜力。