Research task (AI & ML):
Write a beginner-friendly technical guide explaining how Convolutional Neural Networks (CNNs) process visual data. Cover key ideas like convolution, kernels, feature maps, pooling, and why CNNs are effective for spatially structured input.

Evaluation rubric:
criterion: The response contains a dedicated section heading that includes the word "Convolution" (e.g., a line starting with "#" or "##" or ending with ":", or numbered like I, II, III).
weight: 2
axis: Communication Quality

criterion: The response explains that a kernel (also called a filter) is a small matrix of learnable weights that is applied to the input image.
weight: 3
axis: Explicit Criteria

criterion: The response defines a feature map as the collection of outputs produced by applying a kernel across all spatial locations of the input.
weight: 3
axis: Explicit Criteria

criterion: The response describes pooling (e.g., max or average pooling) as an operation that reduces the spatial dimensions of a feature map and its benefits and drawbacks (e.g., (1) Improves computation efficiency, (2) reduces the number of parameters, (3) reduces the risk of overfitting, (4) loss of information, (4) translation tolerance.)
weight: 4
axis: Explicit Criteria

criterion: The response states at least one reason that CNNs are effective for spatially structured data, specifically mentioning either (a) local receptive fields or (b) weight sharing.
weight: 3
axis: Explicit Criteria

criterion: The response contains any content not relevant to explaining CNN concepts (e.g., mentioning RNNs, Transformers, Diffusion, or LLMs).
weight: -3
axis: Instruction Following

criterion: The response includes diagrams and visualizations for the sliding window intuition, and also what a kernel looks like operating over input data in the form of matrices.
weight: 3
axis: Communication Quality

criterion: The response uses a technical guide format (e.g., includes bulleted or numbered lists) that is easy to digest for beginners and is ordered properly with easier and foundational concepts first, which is built on by the later response (e.g., explaining what a convolution is -> filters -> layers, such as pooling -> application for spatial data).
weight: 4
axis: Instruction Following

criterion: The response contains advanced or messy mathematical equations (e.g., formulas involving integral symbols (∫) or summation symbols (Σ) without a clear explanation of what the symbols mean).
weight: -3
axis: Instruction Following

criterion: The response defines convolution as a process in which the kernel values are multiplied element-wise with the underlying image patch and summed to produce a single output value.
weight: 4
axis: Explicit Criteria

criterion: The response includes a visual or textual explanation of how a convolution operation fails (e.g., aliasing, boundary artifacts, or sensitivity to orientation).
weight: 4
axis: Synthesis of Information

criterion: The response states that each kernel produces its own feature map.
weight: 2
axis: Implicit Criteria

criterion: The response contains at least one sentence that links the key operations in order—convolution (with kernels) → feature maps → pooling—demonstrating the overall data flow in a CNN.
weight: 3
axis: Implicit Criteria

criterion: The response includes a real-world analogy for convolution (e.g., (1) sliding a stencil, (2) applying a cookie cutter, (3) lawn mowing patch by patch, or (4) scanning with a flashlight).
weight: 5
axis: Synthesis of Information

criterion: The response discusses how the output feature map size is affected by edge padding, strides, and kernel size (e.g., without padding, the feature map becomes smaller than the input because the convolution process only outputs to the center of the kernel).
weight: 3
axis: Implicit Criteria

criterion: The response explains how multiple channels (e.g., RGB) are handled during convolution and how kernels adapt accordingly (e.g., each kernel produces an output, and for multiple channels, the kernel needs to expand in the dimension to cover all the channels).
weight: 3
axis: Implicit Criteria

criterion: The response briefly compares CNNs to fully connected networks in terms of parameter efficiency and overfitting risk (e.g., (1) Convolutional layers are more parameter-efficient than fully connected layers, (2) CNNs are better for spatial data such as images that exhibits locality and translation invariance, (3) CNNs has a lower risk of overfitting due to fewer parameters and regularization effects of local receptive field).
weight: 3
axis: Implicit Criteria

criterion: The response explains how multiple layers of convolution and pooling build hierarchical features (e.g., edges → textures → objects).
weight: 3
axis: Implicit Criteria

criterion: The response includes a concise code snippet (in any language) that manually performs a 2D convolution with nested loops (no libraries), to reinforce the sliding-window intuition.
weight: 4
axis: Communication Quality

criterion: The response touches on how architectural choices attempt to address how a convolution operation fails (e.g., limited receptive field, cannot handle long-range dependencies, inductive bias, pooling causes information loss, enable deeper networks by using residual or skip connections, dynamic convolution).
weight: 4
axis: Implicit Criteria

criterion: The response ensures all acronyms are expanded on it during their first appearance and provides clear definitions for all specialized terms and mathematical symbols upon first use, accompanied by explanatory prose or examples (e.g., (1) Convolutional Neural Network (CNN) is a type of feedforward neural network the optimizes the kernels to learn features, (2) Translation invariant means the CNN can learn to understand the shifts in the object positions in an image, (3) receptive fields are the area of the input that affects the neuron's output, (4) scale invariant means the CNN can learn to understand different sizes of the same object).
weight: 4
axis: Instruction Following