This paper explores the concept of Fourth Generation Artificial Intelligence (AI-4) and its potential impact on various sectors. It begins with an overview of the evolution of AI, from symbolic AI to deep learning, and highlights the limitations of current AI approaches. The characteristics of AI-4, including contextual understanding, explainability, cognitive capabilities, and generalization, are then discussed. The integration of cognitive science and neuroscience principles into AI-4 is examined, along with the ethical implications and considerations associated with its development and deployment.
The paper explores the applications and impact of AI-4 in healthcare, transportation, robotics, and finance, emphasizing its potential to revolutionize these sectors. Furthermore, it identifies technical challenges such as scalability, data limitations, explainability, and contextual understanding that need to be addressed for the successful implementation of AI-4. Societal and ethical challenges, including bias and fairness, privacy, accountability, and employment disruption, are also discussed.
The contribution of this paper lies in providing a comprehensive overview of AI-4 and its implications. It emphasizes the need for responsible development and deployment of AI-4 systems, considering the ethical implications and societal impact. The paper concludes by highlighting future research directions, including scalable algorithms, addressing data limitations, and advancing explainability techniques. It also emphasizes the importance of interdisciplinary collaboration to ensure the responsible and beneficial deployment of AI-4 systems.
Overall, this paper serves as a valuable resource for researchers, policymakers, and practitioners interested in understanding the concept of Fourth Generation Artificial Intelligence and its potential applications, challenges, and implications.
Himanshi Jaiswal (2023), Toward the Fourth Generation Artificial Intelligence. Multidisciplinary International Journal of Research and Development (MIJRD), Volume: 02 Issue: 05, Pages: 26-35. https://www.mijrd.com/papers/v2/i5/MIJRDV2I50003.pdf