Automated Detection at Scale
Machine learning evolves through phases: simple rules, image recognition, behavioral pattern analysis, and network mapping.
Vampire bats remember ~10 colony members individually. Humans can track ~150 relationships (Dunbar's number). Alibaba detects fraud among 800 million users. The scaling required automation.
Ant colonies maintain cooperation among millions of individuals who can't possibly remember each other individually. They use chemical markers - pheromones that mark colony members. An ant with wrong chemical signature gets attacked immediately. This is automated cheater detection: simple rules that scale beyond individual memory.
Business Application of Automated Detection at Scale
When transaction volume exceeds human observation capacity (Alibaba's 800 million users, 1 billion listings), platforms need automated detection - the digital equivalent of ant colony pheromone markers. Machine learning evolves through phases: simple rules, image recognition, behavioral pattern analysis, and network mapping. The arms race is permanent: as detection improves, cheaters evolve more sophisticated tactics.