My AI translation bot kept saying the wrong thing for 4 days straight

I was building a simple chatbot that translates product descriptions for a small shop in Austin. Thought it would take me like 2 hours tops. Nope. The thing kept swapping Spanish and Portuguese randomly, even though I double checked the language codes. Turns out the training data had some mixed up labels from a public dataset I grabbed off GitHub. Took me 4 full evenings of testing and retraining to find that one bad batch of 200 entries. I finally fixed it by filtering out everything that wasn't manually verified. Has anyone else run into sneaky dataset errors that took forever to catch?

3 comments

3 Comments

logan70527d ago

Had a similar thing happen with a weather bot I was messing with back in 2021. Used a public dataset for historical temps in Texas and the thing would randomly spit out "72 degrees and sunny" for places like Amarillo in January. Took me three weeks to realize the dataset had 500 entries where someone just copy pasted the same line from a summer day in Corpus Christi. I only caught it because I was manually checking the worst outliers and saw a cluster of identical timestamps. Ended up writing a script to flag any row where the temperature didn't change for more than 12 hours straight. Found like 800 more bad entries after that.

wyattrobinson27d ago

Wait, did you check if the timestamps were all the same day too? I had a buddy who ran a traffic bot for a city project and found out half his data was from one Tuesday in August because the guy who compiled it just hit copy paste on a spreadsheet. He told me @logan705's idea about flagging no change for 12 hours would've saved him months of debugging.

susana6612d ago

Those 800 extra bad entries sound like a lot, but I wonder how much it actually mattered in the end. If the bot was just for messing around, not some official weather service, why spend three weeks hunting down bad data? I had a similar thing with a gardening app that kept saying it was 80 degrees in December in Minnesota. I just laughed and moved on. Feels like sometimes people get too caught up in making everything perfect when the real world is full of bad data anyway.