Manuscript received September 17, 2024; revised November 6, 2024; accepted November 14, 2024; published December 27, 2024.
Abstract—In recent years, there has been a rapid development in artificial intelligence, bringing hope to the world’s technological advancements, especially with the popularization and application of effective AI. This paper delves into affective computing, a branch of artificial intelligence focused on understanding and replicating human emotions. It highlights the field’s growth, particularly in AI’s ability to interpret and mimic human emotions, and explores its integration in areas like healthcare, education, and entertainment. Emphasis is placed on technologies such as Deep Neural Networks (DNN) and large language models like ChatGPT, which are crucial for emotion detection and sentiment analysis. Despite advancements, challenges in accurately depicting human emotions and ethical concerns like privacy and AI biases are discussed. The paper reviews effective AI applications, contrasting various methods and noting their strengths and limitations. It advocates for future research toward more sophisticated, multimodal, and ethically sound emotional AI models. Overall, the study provides a comprehensive survey of affective computing, evaluating its current state, potential improvements, and the need for responsible development in AI that understands human emotions.
Keywords—affective Computing, Artificial Intelligence, Deep Neural Networks (DNN), Large Language Models (LLM), multimodal AI Model
Cite: Hanze Wan, "Affective AI: Navigating the Journey from Present Achievements to Future Innovations,"
International Journal of Engineering and Technology, vol. 16, no. 4, pp. 269-276, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
