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Heard a data scientist at a coffee shop in Austin explain why AI models fail
I was grabbing a latte last Tuesday and overheard this guy telling his friend that most AI models break because the training data has too much noise, not because the math is wrong. He said something like 'garbage in, garbage out still holds even with fancy neural nets.' It made me wonder how many companies are dumping messy data into these tools and expecting perfect results. Anyone else notice this disconnect in real world AI projects?
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olivia_hernandez1d ago
The part about "garbage in, garbage out still holds even with fancy neural nets" really got me. I honestly can't believe companies are throwing millions at AI projects without cleaning their data first. It's like building a house on a swamp and wondering why it sinks. I've seen this mess firsthand where people think the algorithm will magically fix bad data, but it just amplifies the errors. That's not how math works, no matter how many layers you stack on it. Are these companies even hiring people who understand basic data hygiene before they start coding?
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