Cognitive computing is a subfield of artificial intelligence (AI) that aims to develop systems that can perform tasks that are typically associated with human cognition, such as understanding natural language, learning, and problem-solving. Cognitive computing systems are designed to be able to learn and adapt over time, allowing them to improve their performance on a given task without being explicitly programmed to do so.

One of the main differences between cognitive computing and traditional AI is that cognitive computing systems are designed to be more flexible and adaptable than traditional AI systems. While traditional AI systems are programmed to perform specific tasks, cognitive computing systems are designed to be able to learn and adapt to new situations and tasks, allowing them to handle a wider range of problems and contexts.

Another key difference is that cognitive computing systems are designed to be more human-like in their abilities and interactions. They are often designed to be able to understand and interpret natural language, allowing them to communicate with humans in a more intuitive and natural way.

Overall, the goal of cognitive computing is to create systems that are able to perform a wide range of tasks that are typically associated with human cognition, and to do so in a way that is flexible, adaptable, and human-like.