This special issue entitled “Deep Learning and Symbol Emergence” presents discussion of technology beyond deep learning toward human intelligence. In recent years, deep learning has achieved phenomenal success in various fields such as image recognition, speech recognition, game-playing, and natural language processing. Nevertheless, discussion persists among researchers on the limitations of deep learning. The most popular criticism is that what deep learning has accomplished to date is mere pattern recognition. To realize more advanced intelligence toward achieving human intelligence requires greater integration with symbol manipulation such as logical reasoning and causal reasoning [1]. Bengio recently delivered an invited talk at the NuerIPS 2019 conference entitled “From system 1 deep learning to system 2 deep learning,” which emphasized the necessity of integrating causal reasoning with current deep learning methods. A dichotomy of system 1 and system 2 was presented by Karneman, a Nobel laureate in the area of economy and psychology [2]: System 1 refers to “fast thinking” as pattern recognition by current deep learning. System 2 refers to “slow thinking,” which is typically done as conscious and logical thinking. Integration of current deep learning with symbol manipulation is not an easy path. Several hurdles remain. First, we must produce some means of simulating the environment of an agent, a so-called world model. Several studies have examined world models [3, 4], which attempt to model the environment around an agent using deep learning. To date, most studies have specifically examined prediction based on sensory data, but integration of sensor and motor data are important. Second, it is necessary to integrate the world model with symbols. Some concepts or features learned in the world model can be associated with linguistic patterns such as a word or a phrase. Prediction of the utterances from other people might serve as a strong prior to promote learning. This special issue includes three papers addressing the direction laid out above. The first paper, by Ito et al. [5], targets modeling of the sensory-motor signals of a robot. This task is fundamentally important to produce a world model so that a

中文翻译：

---

深度学习和符号出现的特点

这期题为“深度学习和符号出现”的特刊讨论了超越深度学习的技术对人类智能的讨论。近年来，深度学习在图像识别、语音识别、游戏和自然语言处理等各个领域取得了惊人的成功。尽管如此，研究人员仍然在讨论深度学习的局限性。最流行的批评是，深度学习迄今为止所取得的成就仅仅是模式识别。为了实现更高级的智能以实现人类智能，需要与符号操作（例如逻辑推理和因果推理）进行更大程度的整合[1]。Bengio 最近在 NuerIPS 2019 会议上发表了题为“从系统 1 深度学习到系统 2 深度学习，”，强调了将因果推理与当前的深度学习方法相结合的必要性。经济和心理学领域的诺贝尔奖获得者卡尼曼提出了系统 1 和系统 2 的二分法 [2]：系统 1 将“快速思维”称为当前深度学习的模式识别。系统 2 指的是“慢速思考”，这通常是作为有意识和逻辑的思考完成的。将当前的深度学习与符号操作相结合并非易事。仍然存在几个障碍。首先，我们必须产生一些模拟代理环境的方法，即所谓的世界模型。几项研究已经检查了世界模型 [3, 4]，这些模型试图使用深度学习对代理周围的环境进行建模。迄今为止，大多数研究都专门研究了基于感官数据的预测，但传感器和电机数据的集成很重要。其次，需要将世界模型与符号相结合。在世界模型中学到的一些概念或特征可以与语言模式（例如单词或短语）相关联。对他人话语的预测可能是促进学习的有力先验。本期特刊包括三篇关于上述方向的论文。伊藤等人的第一篇论文。[5]，目标是对机器人的感觉运动信号进行建模。这项任务对于生成世界模型非常重要，以便 对他人话语的预测可能是促进学习的有力先验。本期特刊包括三篇关于上述方向的论文。伊藤等人的第一篇论文。[5]，目标是对机器人的感觉运动信号进行建模。这项任务对于生成世界模型至关重要 对他人话语的预测可能是促进学习的有力先验。本期特刊包括三篇关于上述方向的论文。伊藤等人的第一篇论文。[5]，目标是对机器人的感觉运动信号进行建模。这项任务对于生成世界模型非常重要，以便