Behind the Scenes: Introduction to Artificial Intelligence with Brian Yu - Chapter 4 - Sensing

Artificial intelligence systems interpret sensory data by converting complex inputs into numerical formats that neural networks can analyze. Images are represented as grids of pixel values, where convolutional layers identify hierarchical patterns—from simple edges to complex shapes—while pooling layers reduce data dimensionality to improve computational efficiency. Video analysis extends this approach by processing sequences of frames to detect temporal motion. Similarly, audio is transformed into spectrograms, which map frequency intensity over time, allowing AI to apply visual pattern recognition techniques to speech. Effective model training relies on representative datasets to ensure generalization and mitigate bias, with transfer learning and fine-tuning enabling the adaptation of pre-trained networks to new tasks without requiring exhaustive computational resources from scratch.