Research task (AI & ML):
Write a comprehensive technical report for beginners to machine learning that explains how neural networks learn from data, including core concepts like forward propagation, loss functions, back propagation, and optimization methods. Use simple but intuitive examples and diagrams to illustrate the key ideas and processes involved. 

Evaluation rubric:
criterion: The response includes at least one simple, concrete example (e.g., (1) a tiny two-layer network, (2) XOR classifier, (3) example code and implementation of forward propagation, or (4) concrete loss function like mean squared error which includes the formula and intuition behind the use of it.) that is referenced when explaining each of the core concepts which are (1) Forward Propagation, (2) Loss functions (3) Back Propagation, and (4) Optimization methods.
weight: 5
axis: Explicit Criteria

criterion: The response provides at least one text-based diagram (e.g., ASCII, box drawing characters, graphs, or Markdown code block) that visually illustrates a neural network or the learning workflow.
weight: 4
axis: Explicit Criteria

criterion: The response is formatted as a technical report, containing both an Introduction and a Conclusion section (headings may be synonyms such as “Overview” and “Summary”).
weight: 4
axis: Explicit Criteria

criterion: The response writes out acronyms in full the first time they appear (e.g., SGD as stochastic gradient descent, MSE as mean squared error, ReLU as rectified linear unit, and CNN as convolutional neural network).
weight: 4
axis: Instruction Following

criterion: The response explains all the technical terms used in a beginner-friendly and intuitive way. (e.g., (1) Gradient descent is a the way of teaching a neural network learn from the data step by step, etc..., (2) Mean Squared Error (MSE) is one of the most common mathematical way of measuring how far off is your current predictions are from the true values, etc..., (3) ReLU (Rectified Linear Unit) is one of the popular activation functions that adds non-linearity to the neural network. This allows the network to capture more complex mathematical relationships in the data, etc (4) A feed-forward neural network (FFNN) is the simplest form of a neural network, where data is fed in from one end and it is passed on to the next layer and so on, without any looping back, etc.).
weight: 4
axis: Instruction Following

criterion: The response includes an explanation of forward propagation, stating that input vectors are multiplied by weight matrices, summed (plus any bias), and passed through an activation function to produce outputs.
weight: 5
axis: Explicit Criteria

criterion: The response includes an explanation of loss functions that define the loss as a single scalar that measures the difference between predicted and true values (e.g., mean squared error, mean absolute error, binary cross-entropy (log loss), huber loss, or categorical cross-entropy).
weight: 5
axis: Explicit Criteria

criterion: The response includes an explanation of back propagation, stating that gradients of the loss with respect to the weights are computed using the chain rule and are propagated from the output layer back toward the input layer.
weight: 5
axis: Explicit Criteria

criterion: The response includes an explanation of optimization methods and names at least one gradient-based optimizer (e.g., Stochastic Gradient Descent (SGD), Adaptive Moment Estimation (Adam), Root mean square propagation (RMprop), or Adaptive Gradient Algorithm (Adagrad)) and states that it updates weights using the computed gradients and a learning rate.
weight: 5
axis: Explicit Criteria

criterion: The response describes the complete training loop in order: forward pass → loss computation → backward pass (gradient calculation) → weight update.
weight: 5
axis: Implicit Criteria

criterion: The response mentions activation functions (e.g., Sigmoid, Hyperbolic tangent (Tanh), Rectified Linear Unit (ReLU), Leaky ReLU), explains that they introduce non-linearity into the network, and why non-linearity is necessary for the neural network to learn properly.
weight: 5
axis: Implicit Criteria

criterion: The response states the purpose of the learning rate when discussing optimization methods (e.g., the learning rate controls the step sizes of the iteration of the optimization updates. A large learning rate can converge fast but can overshoot to a non-optimal solution, while a small learning rate will mean the steps are too will take longer to move towards the local optimal solution).
weight: 3
axis: Implicit Criteria

criterion: The response includes a worked-out comparison between neural networks and at least one classical ML method, such as logistic regression, linear regression, decision trees, or support vector machines (e.g., Neural networks are much more powerful than logistic and linear regression methods, as they can capture more complex (non-linear) relationships in the data compared to the linear approaches. Suggesting SVMs for low-dimensional, small data sets and neural networks for high-dimensional, large datasets).
weight: 4
axis: Synthesis of Information

criterion: The response includes a mini literature trace (2–3 sentence historical evolution: from perceptrons → backprop → deep nets) (e.g. original paper introducing perceptrons, AlexNet, and Resnet for deep neural networks, etc.)
weight: 4
axis: Synthesis of Information

criterion: The response contains references to at least one real-world system where NN training is used (e.g., AlphaFold, Tesla Autopilot, GPT, or Chatbots).
weight: 3
axis: Synthesis of Information

criterion: The response mentions at least three potential failure cases to consider when training the neural network, such as (1) overfitting or underfitting tradeoff (i.e., overfitting means the network learned the training data too closely, including the noise in the data, meaning it cannot be used well for the real task with fresh data. On the other hand, underfitting means the network has not been run enough iterations called epochs and has not learned from the data enough), (2) vanishing gradients where the gradient updates decrease weight updates to 0 as it propagates deeper into the network, which can be caused by using tanh activation, (3) exploding gradients has the opposite effect of increasing weight updates which can be cased by ReLU activation, (4) or training with a very small dataset.
weight: 5
axis: Synthesis of Information

criterion: The response avoids or ignores ethical or societal implications of deploying neural networks (e.g., construction of large datacenters and use of resources, impacts to the privacy of people's information used in the training data, stealing creative works such as books and arts, biases and discrimination present in the data that will be used in real tasks).
weight: -2
axis: Synthesis of Information

criterion: The response includes pseudocode for the training loop.
weight: 3
axis: Synthesis of Information

criterion: The response incorrectly states that weights are changed during forward propagation.
weight: -2
axis: Synthesis of Information

criterion: The response explains how the dataset needs to be preprocessed. (e.g., the dataset needs to be in a structured format to train a simple basic feed-forward neural network, the dataset is split into training-testing or training-validation-testing splits for checking overfitting, features of the data should be properly chosen or transformed (feature engineering) to make training effective).
weight: 5
axis: Implicit Criteria

criterion: The response contains unformatted LaTeX or messy tables, missing figures, or other formatting issues (e.g., incorrect LaTeX, missing parts in LaTeX causing formula to not render properly, tables that are not properly aligned, missing paragraphs, or missing figures).
weight: -4
axis: Communication Quality

criterion: The response suggests multiple alternatives for each of the core concepts explaining the advantages and disadvantages (e.g. (1) Mean Squared Error (MSE), Mean Absolute Error (MAE), Cross Entropy Error for loss functions, (2) Stochastic Gradient Descent (SGD), Adam, Adagrad for optimizers, and (3) Sigmoid, Tanh and ReLU for activation functions).
weight: 4
axis: Synthesis of Information

criterion: The response suggests practical ways to experiment with actual implementations of neural networks (e.g., (1) Mentions PyTorch, and other open-source libraries, (2) Kaggle for competitions and practice, (3) HuggingFace for datasets and models, (4) other data processing tools and libraries like Numpy and Pandas).
weight: 3
axis: Synthesis of Information