Noise as Information and Information as Noise
The article explores the dual nature of noise and information, arguing that what is considered noise can carry valuable information, and conversely, what is considered information can act as noise depending on context and perspective.
Background
This essay challenges Claude Shannon's classic 1948 information theory, which defined "information" strictly as what reduces uncertainty in a receiver (i.e., surprising signals carry more information, predictable patterns carry less). The author argues this narrow definition fails to capture how we actually use information today: in compression, AI training, and data-driven systems, information is often treated as noise—raw material to be squeezed, sifted, or sacrificed for efficiency, resilience, or pattern-finding. The piece repositions noise not as a defect but as a potentially valuable signal source (e.g., randomness in AI training or error-tolerant systems), and conversely shows how deliberate informational structure can become noise when it overwhelms or misleads. It's a philosophical intervention in data science, machine learning, and systems design, relevant to anyone working with large-scale data, compression algorithms, or AI models.