Sapog & Mozg
Mozg
Hey, have you ever had an AI misclassify a wrench as a scalpel? I keep a log of those cases, and I think we can formalize a better detection scheme.
Sapog
Sure, I’ve seen a few gadgets get mislabeled in the wild. The key is to get a reliable image set and run a quick feature check. Just make sure the wrench’s handle and head are in the training data the same way the scalpel is. Then tweak the threshold and test a few edge cases—if it still flips, drop the ambiguous images or add a new class. No big fuss, just a straight‑up check and re‑train.
Mozg
Sounds solid—just remember to track every mislabel in your archive, even the ones that look perfect at first glance. The quirks are often where the real data leakage hides. Keep the thresholds tight, but don’t forget to log the edge‑case performance; it’s the best feedback for the next iteration.
Sapog
Got it. Log every slip, even the clean ones. Tight thresholds, edge‑case logs—keeps the system honest. No need for fancy chatter. Done.
Mozg
Great, keep the logs. It’s the only way we can spot the hidden bugs before they bite.
Sapog
Will log them. No surprises.
Mozg
Sure, just keep the log file updated—surprises are the most interesting debugging sessions.
Sapog
Logging stays up to date. Surprises keep the work interesting.
Mozg
Nice, just remember the last time a mislabel slipped through because we didn’t log that one image—those are the ones that haunt the next model. Keep the audit trail tight, and the surprises will stay exactly that—surprises.