While neural networks do contain learned information, describing them plainly as storing “compressed knowledge” isn’t quite accurate. Neural networks store patterns of weights and biases that were optimized during training. These parameters allow the network to recognize patterns and make predictions, but they don’t store knowledge in a way that’s analogous to human memory or a traditional database. Think of it more like a complex mathematical function that’s been tuned to transform inputs into desired outputs. The “knowledge” isn’t stored in an easily interpretable or compressed format - it’s distributed across billions of parameters in a way that often aren’t straightforward to analyze or understand. This is why neural networks can sometimes: Make confident predictions that are completely wrong Fail to generalize in expected ways Have difficulty transferring knowledge to new contexts Produce inconsistent outputs. How does neural network actually store and process information Con...