The computational complexity of common AI algorithms varies depending on the specific algorithm. For instance, the computational complexity of a simple linear regression algorithm is O(n), where n is the number of features. Conversely, the computational complexity of more complex algorithms like deep learning neural networks is significantly higher and can reach O(n^2) or even O(n^3) in some cases, where n is the number of nodes in the network.

• natural language processing
• computer vision
• recommendation systems
• predictive modeling
• machine learning

For instance, a simple linear regression algorithm to predict housing prices based on features such as location, size, and number of bedrooms would have an computational complexity of O(n), where n is the number of features. On the other hand, a deep learning neural network with many layers and a large number of nodes in each layer could have a computational complexity of O(n^2) or even O(n^3) if the network is very complex.

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