Artificial Intelligence (AI) has been adopted in a wide range of domains. This shows the imperative need to develop means to endow common people with a minimum understanding of what AI means. Combining visual programming and WiSARD weightless artificial neural networks, this article presents a new methodology, AI from concrete to abstract (AIcon2abs), to enable general people (including children) to achieve this goal. The main strategy adopted by is to promote a demystification of artificial intelligence via practical activities related to the development of learning machines, as well as through the observation of their learning process. Thus, it is possible to provide subjects with skills that contributes to making them insightful actors in debates and decisions involving the adoption of artificial intelligence mechanisms. Currently, existing approaches to the teaching of basic AI concepts through programming treat machine intelligence as an external element/module. After being trained, that external module is coupled to the main application being developed by the learners. In the methodology herein presented, both training and classification tasks are blocks that compose the main program, just as the other programming constructs. As a beneficial side effect of AIcon2abs, the difference between a program capable of learning from data and a conventional computer program becomes more evident. In addition, the simplicity of the WiSARD weightless artificial neural network model enables easy visualization and understanding of training and classification tasks internal realization.
翻译:人工智能(AI)在广泛的领域得到采用,这说明迫切需要发展各种手段,使普通人最起码地了解人工智能的含义。将视觉编程和WISARD无重力人工神经网络结合起来,这一条提出了一种新的方法,即从具体到抽象的人工智能(AIcon2abs),使普通人(包括儿童)能够实现这一目标。采取的主要战略是通过开发学习机器的实际活动,以及通过观察他们的学习过程,促进消除人工智能的神秘化。因此,有可能向学生提供技能,使他们在涉及采用人工智能机制的辩论和决策中具有深刻的行为者。目前,通过编程教授基本人工智能概念的现有方法将机器智能作为外部要素/模块。经过培训后,外部模块与学习者(包括儿童)正在开发的主要应用程序相结合。在此处提出的方法中,培训和分类任务都是构成主要程序的障碍,正如其他编程结构一样。作为AIcon2abs的有益侧面效应,使能够从简单化的数据和常规程序中学习简单化的网络,使得简单化程序在简单化的网络和简单化方面变得能够从简单化的计算机分类中学习。