Hybrid AI is a type of artificial intelligence that combines multiple AI techniques and approaches to solve complex problems. It is a combination of two or more AI systems, such as rule-based systems, expert systems, and connectionist systems (neural networks), to create a more powerful and flexible AI system.
One of the main advantages of hybrid AI is that it can combine the strengths of different AI systems to overcome their limitations. For example, a hybrid AI system can use a rule-based system to handle simple, well-defined tasks and a neural network to handle more complex, ill-defined tasks. This allows the system to perform well on a wide range of tasks and adapt to changing conditions.
Hybrid AI is often used in applications that require a high level of accuracy, such as image and speech recognition, natural language processing, and decision-making. It can also be used in applications that require a high level of explainability, such as medical diagnosis or financial fraud detection, where decision-making must be transparent.
Hybrid AI can also be used to combine different types of data, such as structured data and unstructured data, to make better predictions and decisions. This allows the system to consider a wide range of information when making decisions, which can improve the overall accuracy and performance of the system.
In summary, Hybrid AI is a type of Artificial Intelligence that combines multiple AI techniques and approaches to solve complex problems. It can combine the strengths of different AI systems to overcome their limitations, making it more powerful and flexible. It is often used in applications that require a high level of accuracy and explainability, such as image and speech recognition, natural language processing, and decision-making.
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