This paper evaluates AIcon2abs (Queiroz et al., 2021), a recently proposed method that enables awareness among the general public on machine learning. Such is possible due to the use of WiSARD, an easily understandable machine learning mechanism, thus requiring little effort and no technical background from the target users. WiSARD is adherent to digital computing; training consists of writing to RAM-type memories, and classification consists of reading from these memories. The model enables easy visualization and understanding of training and classification tasks' internal realization through ludic activities. Furthermore, the WiSARD model does not require an Internet connection for training and classification, and it can learn from a few or one example. This feature makes it easier to observe the machine, increasing its accuracy on a particular task with each new example used. WiSARD can also create "mental images" of what it has learned so far, evidencing key features pertaining to a given class. The assessment of the AIcon2abs method's effectiveness was conducted through the evaluation of a remote course with a workload of approximately 6 hours. It was completed by thirty-four Brazilian subjects: 5 children between 8 and 11 years old; 5 adolescents between 12 and 17 years old; and 24 adults between 21 and 72 years old. Data analysis adopted a hybrid approach. AIcon2abs was well-rated by almost 100% of the research subjects, and the data collected revealed quite satisfactory results concerning the intended outcomes. This research has been approved by the CEP/HUCFF/FM/UFRJ Human Research Ethics Committee.

翻译：本文评估了Queiroz等人（2021）最近提出的AIcon2abs方法，该方法旨在提升公众对机器学习的认知。这一目标的实现得益于WiSARD模型——一种易于理解的机器学习机制，使用者无需技术背景且仅需极少努力。WiSARD模型与数字计算高度契合：其训练过程相当于向RAM型存储器写入数据，而分类过程则相当于从这些存储器中读取数据。该模型通过游戏化活动，使训练与分类任务的内部实现过程得以直观可视化与理解。此外，WiSARD模型的训练与分类无需互联网连接，且能够通过少量甚至单个样本进行学习。这一特性使机器观察更为便捷：每新增一个训练样本，模型在特定任务上的准确率即会提升。WiSARD还能生成所学内容的“心理图像”，凸显各类别中的关键特征。本研究通过评估一门约6学时的远程课程来检验AIcon2abs方法的有效性。课程参与者为34名巴西受试者：包括5名8至11岁儿童、5名12至17岁青少年及24名21至72岁成年人。数据分析采用混合研究方法。结果显示，近100%的研究对象对AIcon2abs给予高度评价，所收集数据表明该方法在预期目标上取得了相当满意的成果。本研究已获CEP/HUCFF/FM/UFRJ人类研究伦理委员会批准。