Next-Generation AI Systems: From Cognitive Models to Real-World Impact

Abstract

AI Systems have become an important tool to solve complex problems in various areas such as healthcare, smart city's and autonomous systems. In this study we developed a Cognitive-AI Framework that is a method to increase the accuracy, the reliability and the ethical compliance in all these fields by integrating Deep learning (DL), Generative Modelling (GM), Neuro-Symbolic reasoning (NSR) and Reinforcement learning (RL) in one single Architecture to accomplish robust pattern recognition, synthetic data creation, logical decisions making, and adaptation of the optimal policies. All the data used was taken from references secondary data exclusively, therefore the methodology consists of data preparation, cognitive modeling of domain knowledge, generative scenarios simulations, neuro-symbolic fusion for the explainability of the results, and RL under safety constraints. Mechanisms of ethical oversight were included to guarantee the fairness and the responsibility during the whole process. Therefore, the framework was evaluated and showed that it is able to provide scalable, interpretable and trustworthy AI solutions for real-world applications with critical missions, thus contributing to the ethically responsible development of AI.

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