Recent progress in machine learning techniques have revived interest in building artificial general intelligence using these particular tools. There has been a tremendous success in applying them for narrow intellectual tasks such as pattern recognition, natural language processing and playing Go. The latter application vastly outperforms the strongest human player in recent years. However, these tasks are formalized by people in such ways that it has become "easy" for automated recipes to find better solutions than humans do. In the sense of John Searle's Chinese Room Argument, the computer playing Go does not actually understand anything from the game. Thinking like a human mind requires to go beyond the curve fitting paradigm of current systems. There is a fundamental limit to what they can achieve currently as only very specific problem formalization can increase their performances in particular tasks. In this paper, we argue than one of the most important aspects of the human mind is its capacity for logical thinking, which gives rise to many intellectual expressions that differentiate us from animal brains. We propose to model the emergence of logical thinking based on Piaget's theory of cognitive development.
翻译:机器学习技术的最近进展重新唤起人们对利用这些特定工具建立人工通用智能的兴趣。 在应用这些技术来完成狭隘的知识任务方面已经取得了巨大成功,例如模式识别、自然语言处理和玩游戏。 后者的应用大大优于近年来最强的人类玩家。 然而,这些任务由人们正式化, 使得自动化配方“容易”找到比人类更好的解决方案。 在约翰·西尔勒的中国房间的争论中, 玩游戏的计算机实际上无法理解游戏中的任何内容。 像人类的思维需要超越当前系统的曲线组合范式。 目前他们所能实现的目标有一个根本的局限性, 因为只有非常具体的问题正规化才能增加他们特定任务的表现。 在本文中,我们比人类思想的最重要方面更是其逻辑思维能力, 也就是产生许多将我们与动物大脑区分开来的知识表达方式。 我们提议根据Piaget的认知发展理论来模拟逻辑思维的出现。