Tishby’s theorem

Tishby’s theorem addresses how to efficiently extract useful information from complex data. It states that during data processing, there's a balance point where you maximize relevant information about the original data while minimizing unnecessary details, effectively compressing the data without losing important insights. This concept guides how to design systems (like neural networks) that focus on pathways leading to meaningful understanding, rather than storing all raw data. Overall, it helps optimize the trade-off between data complexity and useful knowledge, ensuring systems are both efficient and effective in learning and decision-making.