High Schooler Discovers 1.5 Million Celestial Bodies Using Old Data: Engineering Bottlenecks in the AI Paradigm Revolution

My daily job involves staring at alerts generated by anomaly detection models and battling false positive rates in rule engines. Last week, a colleague excitedly told me that our team's newly deployed graph neural network model could detect criminal rings we'd previously missed, but at the cost of the false positive rate skyrocketing from 0.5% to 2.3%. I told him not to rush the launch; let's backtest on historical data for three months first. It's not that I don't trust new tech, but in risk control, everyone in engineering knows which hurts more: missing one black-market ring or falsely flagging a thousand normal users.