AI from Concrete to Abstract

Artificial intelligence (AI) has been adopted in a wide range of domains. This shows the imperative need to contribute to making citizens insightful actors in debates and decisions involving the adoption of AI 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. Combining block-based programming and WiSARD weightless artificial neural networks, this article presents the conceptualization and design of a new methodology, AI from concrete to abstract (AIcon2abs), to endow general people (including children) with a minimum understanding of what AI means. The main strategy adopted was to include AI training and classification primitives as blocks that compose an otherwise conventional computer program. This way, the programmer can use these blocks as he/she uses other programming constructs. In order to achieve this purpose, we also propose BlockWiSARD, a block-based programming environment designed to promote the demystification of artificial intelligence via practical activities related to the development of learning machines, as well as through the observation of their learning process. As a beneficial side effect of BlockWiSARD, 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.