Arrange the following examples of Artificial Intelligence (AI) in the order of increasing complexity:
A. Spam email detection using rule-based systems.
B. Handwritten digit recognition using shallow neural networks.
C. Image classification using convolutional neural networks.
D. Autonomous driving using reinforcement learning algorithms.
Choose the correct answer from the options given below:

Correct option is A

To determine the order of increasing complexity among the given AI examples, we need to understand the complexity involved in each task:
A. Spam email detection using rule-based systems: This is the simplest form of AI among the given options. It uses predefined rules to classify emails as spam or not. The complexity is relatively low as it relies on straightforward, manually created rules.
Complexity: Low
B. Handwritten digit recognition using shallow neural networks: This task involves recognizing handwritten digits using a neural network with a few layers. It requires more computation and data than rule-based systems but is still relatively simple compared to deeper networks.
Complexity: Medium-Low
C. Image classification using convolutional neural networks: This task uses convolutional neural networks (CNNs) to classify images. CNNs are more complex than shallow neural networks because they have multiple layers and can learn hierarchical features from images. This requires significant computation power and data.
Complexity: Medium-High
D. Autonomous driving using reinforcement learning algorithms: Autonomous driving is one of the most complex AI tasks. It involves perceiving the environment, making decisions, and controlling the vehicle. Reinforcement learning algorithms enable the system to learn from interaction with the environment, requiring advanced computation, large datasets, and complex algorithms.
Complexity: High
Given this analysis, the order of increasing complexity is:
A: Spam email detection using rule-based systems.
B: Handwritten digit recognition using shallow neural networks.
C: Image classification using convolutional neural networks.
D: Autonomous driving using reinforcement learning algorithms.