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

arXiv cs.AI Papers

Summary

This paper presents AIcon2abs, a methodology combining visual programming and WiSARD weightless neural networks to help general audiences, including children, understand AI concepts through hands-on learning activities. The approach integrates training and classification as first-class programming constructs to make the distinction between learning machines and conventional programs more intuitive.

arXiv:2006.04013v6 Announce Type: cross Abstract: 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.
Original Article
View Cached Full Text

Cached at: 06/05/26, 02:10 AM

# AI from concrete to abstract: demystifying artificial intelligence to the general public
Source: [https://arxiv.org/abs/2006.04013](https://arxiv.org/abs/2006.04013)
[View PDF](https://arxiv.org/pdf/2006.04013)

> Abstract: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\.

## Submission history

From: Rubens Lacerda Queiroz \[[view email](https://arxiv.org/show-email/c3c293e0/2006.04013)\] **[\[v1\]](https://arxiv.org/abs/2006.04013v1)**Sun, 7 Jun 2020 01:14:06 UTC \(2,480 KB\) **[\[v2\]](https://arxiv.org/abs/2006.04013v2)**Thu, 16 Jul 2020 14:23:37 UTC \(2,516 KB\) **[\[v3\]](https://arxiv.org/abs/2006.04013v3)**Tue, 11 Aug 2020 06:44:20 UTC \(3,103 KB\) **[\[v4\]](https://arxiv.org/abs/2006.04013v4)**Tue, 1 Sep 2020 16:03:43 UTC \(3,169 KB\) **[\[v5\]](https://arxiv.org/abs/2006.04013v5)**Thu, 15 Apr 2021 00:53:14 UTC \(3,306 KB\) **\[v6\]**Mon, 13 Jun 2022 02:10:25 UTC \(3,319 KB\)

Similar Articles

How do machines learn? Evaluating the AIcon2abs method

arXiv cs.AI

This study evaluates the AIcon2abs method, an educational approach using the WiSARD weightless neural network algorithm to teach machine learning concepts to diverse audiences including K-12 students. A six-hour remote course with 34 Brazilian participants showed high satisfaction and effectiveness in demystifying AI for non-technical users.

Teaching the foundations of AI in the classroom

YouTube AI Channels

Google DeepMind's 'A.I. in the Classroom' program teaches students foundational AI concepts like data needs, bias, and large language models, aiming to empower tomorrow's problem-solvers through interactive discussions.