Emotional Artificial Intelligences are currently one of the most anticipated developments of AI. If successful, these AIs will be classified as one of the most complex, intelligent nonhuman entities as they will possess sentience, the primary factor that distinguishes living humans and mechanical machines. For AIs to be classified as "emotional," they should be able to empathize with others and classify their emotions because without such abilities they cannot normally interact with humans. This study investigates the CNN model's ability to recognize and classify human facial expressions (positive, neutral, negative). The CNN model made for this study is programmed in Python and trained with preprocessed data from the Chicago Face Database. The model is intentionally designed with less complexity to further investigate its ability. We hypothesized that the model will perform better than chance (33.3%) in classifying each emotion class of input data. The model accuracy was tested with novel images. Accuracy was summarized in a percentage report, comparative plot, and confusion matrix. Results of this study supported the hypothesis as the model had 75% accuracy over 10,000 images (data), highlighting the possibility of AIs that accurately analyze human emotions and the prospect of viable Emotional AIs.

翻译：情感人工智能是目前人工智能领域最受期待的发展方向之一。若此类人工智能研制成功，它们将被归类为最复杂、最智能的非人类实体之一，因为它们将具备感知能力——这是区分有生命人类与机械装置的核心要素。要被称为"情感型"人工智能，它们需要能够共情并分类人类情绪，因为缺乏这些能力，人工智能就无法正常地与人类互动。本研究探讨了CNN模型识别和分类人类面部表情（积极、中性、消极）的能力。为本研究构建的CNN模型使用Python编程，并利用芝加哥面孔数据库的预处理数据进行训练。该模型有意设计得较为简单，以进一步探究其能力。我们假设模型在对输入数据的每个情绪类别进行分类时，表现将优于随机水平（33.3%）。使用新图像测试模型准确率，并通过百分比报告、比较图和混淆矩阵汇总准确率。本研究结果支持了该假设：模型在10,000张图像（数据）上达到了75%的准确率，凸显了人工智能准确分析人类情绪的可能性，以及开发可行性情感人工智能的前景。