Introduction: Why AI Model Layers Matter
Artificial Intelligence (AI) models are structured in layers, each serving a specific role in transforming raw data into meaningful decisions. To understand how AI models function, we must dissect their layered architecture. These layers form the computational hierarchy of an AI system, allowing it to extract, process, and utilize information efficiently.
This lecture will provide an information-theoretic perspective on AI model layers, correlating their function to data representation, transformation, and decision-making. By the end of this lecture, students will understand:
The conceptual framework of AI model layers. How each layer contributes to intelligent decision-making. How these ideas connect to the hands-on team activity. 1. The Input Layer: Where the Journey Begins
Concept:
The Input Layer is the entry point of any AI model. It receives raw data (e.g., text, images, numbers) and encodes it in a mathematical form that a model can process. In information-theoretic terms, this layer reduces entropy (randomness) by transforming diverse inputs into a structured numerical representation. Information Processing:
Data is preprocessed to remove noise, missing values, or irrelevant details. Feature extraction begins by identifying relevant patterns in the data. Words are tokenized (split into meaningful chunks), images are converted into pixel arrays, and categorical values are assigned numeric representations. Connection to Team 1:
Team 1 works on this phase by representing different data types using Play-Doh and mapping them to poker chips. They learn about data normalization and encoding, critical steps before AI processing. 2. Hidden Layers: The Feature Extraction Engine
Concept:
Hidden layers are the core computational engines of AI models. These layers apply mathematical transformations to extract hierarchical features from data. Information-theoretically, these layers perform progressive dimensionality reduction, filtering out noise and amplifying relevant signals. Information Processing:
Each hidden layer applies weights and biases to inputs, adjusting values through a process called forward propagation. Activation functions (ReLU, Sigmoid, Softmax) introduce non-linearity, enabling models to learn complex patterns. The deeper the network, the more abstract the features become. The first hidden layer detects edges in images or common word structures in text, while deeper layers recognize objects, concepts, or topics. Connection to Team 2:
Team 2 models this stage by connecting input tokens using pipe cleaners, showing how data moves through multiple transformations. They explore weight adjustment and feature extraction, essential steps in AI learning. 3. The Training Process: Learning from Data
Concept:
The model refines itself through training, where it adjusts internal parameters to minimize error. Backpropagation and gradient descent adjust weights to reduce prediction errors over time. In information theory, this phase is error correction, optimizing the signal-to-noise ratio. Information Processing:
The AI model compares outputs to known results and calculates errors. Gradient descent modifies weights in the opposite direction of error gradients to improve predictions. Overfitting and underfitting are key risks: a model that memorizes too much performs poorly on new data. Connection to Team 3:
Team 3 visualizes this by modifying playdough tokens based on corrections, simulating how models update their internal structure. 4. Decision Making: Generating an Output
Concept:
AI models make decisions by processing learned representations and computing the most likely or optimal outcome. Information-theoretically, this is the maximum likelihood estimation process. Information Processing:
The model computes final values based on input features and learned parameters. In classification tasks, the model chooses the category with the highest probability. In generative AI, the model predicts the next sequence or generates new content. Connection to Team 4:
Team 4 replicates this by making decisions based on their transformed data, showing how models "decide" what to output. 5. The Output Layer: Producing Meaningful Insights
Concept:
The Output Layer finalizes the computation by producing the model’s result. Depending on the model, the output could be a classification label, a numerical value, or generated text. Information Processing:
The AI model selects the best answer using a probability-based mechanism. Some models use reinforcement learning, continuously refining their predictions. This stage is where explainability and interpretability come into play. Connection to Team 5:
Team 5 visualizes this by mapping final results back to the input and interpreting the AI's decision-making. They analyze how well the model performed and discuss strategies for improvement. Conclusion: The AI Model as a Cognitive Machine
AI models are layered systems, each phase adding meaning, structure, and decision power to raw data. Information theory explains AI as a signal processor, refining information from noise to knowledge. Through our hands-on visual representation, students will understand how an AI model actually works beyond abstract code. By combining this theoretical grounding with the hands-on model-building activity, students will experience AI as both a scientific and engineering discipline, bridging abstract learning with tangible experimentation.
