Why Your AI Model is Blind to Networks: Graphs Explained Topic Overview What is Graph Representation? Graph representation is the mathematical translation of irregular, relational data into strict matrix formats that a computer can process. It defines how we map a network of entities (nodes) and their relationships (edges) into tensors. Why does it exist? Standard deep learning architectures (CNNs, MLPs) require data to exist on a fixed, Euclidean grid. An image is a rigid \(H \times W\) grid of pixels. A sentence is a rigid 1D sequence of words. But real-world data—molecules, social networks, transportation grids—has no fixed structure. Node A might have 2 friends; Node B might have 10,000. There is no “up” or “down” neighbor. Graph representation exists to capture this arbitrary, non-Euclidean topology in a way that allows us to apply linear algebra and neural networks to it. What problem does it solve? It solves the “array mismatch” probl...
Why Your AI Model is Blind to Reality: Causal Inference Topic Overview What is Causal Inference in ML? It is the mathematical framework that allows machine learning models to distinguish between correlation and causation, enabling them to predict the outcome of interventions rather than just observations . Why does it exist? Standard deep learning is fundamentally an advanced form of curve-fitting. A neural network minimizes a loss function by finding any statistical shortcut in the data. If your training data for cows contains mostly green grass, the network will learn that green pixels cause the classification “cow.” This is a spurious correlation. Causal inference exists to explicitly forbid the model from using these shortcuts by mathematically modeling the true data-generating process. What problem does it solve? It solves the Out-of-Distribution (OOD) generalization problem. When a model relies on spurious correlations, it fails catastrop...