论文标题

AI从具体到抽象

AI from concrete to abstract: demystifying artificial intelligence to the general public

论文作者

Queiroz, Rubens Lacerda, Sampaio, Fábio Ferrentini, Lima, Cabral, Lima, Priscila Machado Vieira

论文摘要

人工智能(AI)已在广泛的领域中采用。这表明需要发展手段的需求,以使普通人对AI的含义有最低限度的了解。本文结合了视觉编程和WISARD失重的人工神经网络,提出了一种新方法,即从混凝土到抽象(Aicon2abs)的AI,使一般人(包括儿童)能够实现这一目标。采用的主要策略是通过与学习机的发展以及通过观察他们的学习过程有关的实践活动来促进人工智能的神秘化。因此,可以为受试者提供技能,从而有助于使他们在涉及采用人工智能机制的辩论和决定中有见地的演员。当前,通过编程将机器智能视为外部元素/模块,现有的基本AI概念教学方法。经过培训后,该模块与学习者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块,就像其他编程构造一样。作为AICON2ABS的有益副作用,能够从数据学习的程序与常规计算机程序的程序之间的差异变得更加明显。此外,WISARD失重的人工神经网络模型的简单性可以轻松可视化和理解训练和分类任务内部实现。

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.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源