Today, we live in the midst of a surge in the use of artificial intelligence in many scientific and technological applications, including the Search for Extraterrestrial Intelligence (SETI). However, human perception and decision-making is still the last part of the chain in any data analysis or interpretation of results or outcomes. One of the potential applications of artificial intelligence is not only to assist in big data analysis but to help to discern possible artificiality or oddities in patterns of either radio signals, megastructures or techno-signatures in general. In this study, we review the comparative results of an experiment based on geometric patterns reconnaissance and a perception task, performed by 163 human volunteers and an artificial intelligence convolutional neural network (CNN) computer vision model. To test the model, we used an image of the famous bright spots on the Occator crater on Ceres. We wanted to investigate how the search for techno-signatures or oddities might be influenced by our cognitive skills and consciousness, and whether artificial intelligence could help or not in this task. This article also discusses how unintentional human cognitive bias might affect the search for extraterrestrial intelligence and techno-signatures compared with artificial intelligence models, and how such artificial intelligence models might perform in this type of task. We discuss how searching for unexpected, irregular features might prevent us from detecting other nearside or in-plain-sight rare and unexpected signs. The results strikingly showed that a CNN trained to detect triangles and squares scored positive hits on these two geometric shapes as some humans did.