Parallel programming is an essential approach to addressing the growing demand for computational performance and efficiency in the modern computing era. As problems become increasingly complex and datasets grow exponentially, parallel programming emerges as a promising solution for accelerating processing and overcoming the limitations of individual computing capabilities. Unlike conventional sequential programming, where instructions are executed in a linear fashion, parallel programming involves dividing tasks into smaller parts that can be executed simultaneously by different processors, cores, or even networked computers. This approach allows computational resources to be utilized more efficiently, resulting in significant performance gains and reduced processing time.
Parallel programming is particularly relevant in areas such as data science, artificial intelligence, computational simulations, graphics rendering, big data analysis, and many other domains dealing with massive volumes of data and intensive calculations. By distributing the workload among multiple processors or devices, faster results can be achieved, larger problems can be solved, and scientific and technological advancements can be made more effectively.
Artificial Intelligence (AI) is a relatively new concept that has gained significant momentum in recent years. While the idea of machines imitating human intelligence dates back centuries, the term "AI" as we know it today was introduced in 1956 at a conference in Dartmouth University. AI technologies have rapidly advanced, transforming various aspects of our lives and interactions. These technologies are now capable of complex tasks like understanding natural languages and generating texts that resemble human writing. However, such advancements raise important societal questions about the trustworthiness of technology in making critical decisions and the implications of AI for our understanding of human nature.
In the article "Feeling our way to machine minds: People's emotions when perceiving mind in artificial intelligence" by Daniel B. Shank, the relationship between human emotions and the perception of AI's capabilities is explored. The author highlights three conditions that influence this relationship. The first condition occurs when AI systems produce results perceived as extraordinary, which can evoke emotions of admiration or surprise. The second condition arises when AI assumes social roles, leading to intense emotional reactions regarding the perception of AI having a mind. Lastly, the third condition arises when AI exhibits human-like qualities, which can elicit both admiration and fear.
Considering these perspectives, the improvement of AI systems heavily relies on their ability to interpret human emotions. This work proposes a preliminary step in acquiring data on human emotions by developing an algorithm capable of recognizing and storing facial images. Subsequently, an algorithm that interprets emotions will be proposed. Understanding the perception of AI's capabilities in relation to human emotions is crucial for ethical considerations, as it holds significant implications for morality and how we interpret various situations, as discussed by Carla Woyciekoski in her article "Emotional intelligence: theory, research, measurement, applications, and controversies."
To develop the algorithms, the following bibliographic references will be utilized: "A real-time face emotion classification and recognition using deep learning model" by Shaik Asif Hussain and the documentation of the OpenCV library for Python.