"According to tech analyst Gartner, AI data is rapidly becoming a classic Garbage In/Garbage Out (GIGO) problem for users. That's because organizations' AI systems and large language models (LLMs) are flooded with unverified, AI‑generated content that cannot be trusted."
"You know this better as AI slop. While annoying to you and me, it's deadly to AI because it poisons the LLMs with fake data. The result is what's called in AI circles " Model Collapse." AI company Aquant defined this trend: "In simpler terms, when AI is trained on its own outputs, the results can drift further away from reality." However, I think that definition is much too kind. It's not a case of "can" -- with bad data, AI results "will" drift away from reality."
"The analyst argued that enterprises can no longer assume data is human‑generated or trustworthy by default, and must instead authenticate, verify, and track data lineage to protect business and financial outcomes."
AI systems and large language models face a growing Garbage In/Garbage Out problem as unverified, AI-generated content proliferates in corporate and public sources. Self-reinforcing AI outputs poison model training and lead to model collapse that drifts results away from reality. Enterprises will need a zero-trust data governance posture, authenticate and verify data sources, and track data lineage to protect business and financial outcomes. Implementing these protections requires specialized skills, company-wide effort, and improved AI literacy to ensure that data used for training and decision-making is accurate and trusted.
Read at ZDNET
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