Supervised machine learning (ML) algorithms are aimed at maximizing classification performance under available energy and storage constraints. They try to map the training data to the corresponding labels while ensuring generalizability to unseen data. However, they do not integrate meaning-based relationships among labels in the decision process. On the other hand, natural language processing (NLP) algorithms emphasize the importance of semantic information. In this article, we synthesize the complementary advantages of supervised ML and NLP algorithms into one method that we refer to as SECRET (Semantically Enhanced Classification of REal-world Tasks). SECRET performs classifications by fusing the semantic information of the labels with the available data: it combines the feature space of the supervised algorithms with the semantic space of the NLP algorithms and predicts labels based on this joint space. Experimental results indicate that, compared to traditional supervised learning, SECRET achieves up to 14.0 percent accuracy and 13.1 percent F1 score improvements. Moreover, compared to ensemble methods, SECRET achieves up to 12.7 percent accuracy and 13.3 percent F1 score improvements. This points to a new research direction for supervised classification based on incorporation of semantic information.

Most machine learning (ML) models are geared toward improving some desired metric like classification accuracy or inference latency. Given the significant successes of such models, especially supervised ones, in addressing such metrics, the time has come to ask if they can also be put to direct use in understanding the users and addressing basic human needs, e.g., subsistence, protection, affection, understanding, participation, leisure, creation, identity, and freedom. A prerequisite to addressing such human needs is that the ML models exhibit a basic grasp of human psychology. In this article, we present ML models that can be embedded in our smartphone and enable it to understand us. We call this system your smartphone understands you (YSUY). YSUY uses wearable medical sensors to understand our physical, mental, and four-class (two-class) emotional states with 90.0%, 90.3%, and 98.4% (99.5%) accuracy, respectively. After verifying YSUY’s ability to understand the human condition from various perspectives, we evaluate the relationship between the different states and discuss how YSUY can be taken one step further to start playing a helpful role in addressing human needs of the types mentioned above from four different perspectives: “being,” “doing,” “having,” and “interacting.” We show that YSUY is a a promising candidate for adapting ML models to human-centric needs. We view this only as an initial step, hopefully, spurring other researchers to investigate the relationship between ML models and fulfilling human needs in much greater depth.