Integrating Cognitive Computing and Machine Learning Techniques in Artificial Intelligence Systems
Keywords:
Cognitive computing, Machine learning, Artificial intelligence, Integration, Decision-making, Self-learning, Data complexity, Adaptive systems, AI systems, Research, Applications, Algorithms, Cognitive models, Efficiency, Problem-solvingAbstract
Cognitive computing and machine learning (ML) represent two powerful paradigms transforming the landscape of artificial intelligence (AI) systems. This paper explores the integration of cognitive computing and ML techniques, focusing on their synergistic potential to enhance decision-making processes and problem-solving capabilities in AI systems. The paper presents a detailed examination of how combining these two fields can result in more adaptive, efficient, and intelligent systems. The integration is particularly relevant in the context of real-world applications, where data complexity and the need for self-learning models continue to rise. We discuss various approaches and architectures to combine cognitive computing and ML, alongside a review of their impact on AI solutions. The paper also evaluates current research directions and challenges in this area, providing insights into the future of cognitive computing and ML integration in AI systems.
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