"This is not solely about data/analysis reliability, although that is an issue. These models are often trained on data that is outdated or of low reliability, - and let's not get started on the never-ending hallucination issues. Then there are problems with these systems accurately understanding the intent behind queries as well as improperly analyzing data. And the models have limited abilities to detect when a user is using the wrong prompts for the information they truly need."
"Nope, this is something else. Vendors like to pitch these smart capabilities as being akin to having the most brilliant administrative assistant on the planet. But when people actually interact with these tools in real-world situations, they're not seeing the "smart." It's not just because of the complexity of Al technology (though it's almost unfathomably complex). Even the simplest tools come up short."
AI models frequently rely on outdated or low-reliability training data, which contributes to frequent hallucinations and incorrect outputs. Systems often misinterpret user intent and improperly analyze data, reducing accuracy and usefulness. Models exhibit limited ability to detect when users are using inappropriate or imprecise prompts for their true information needs. Vendor marketing positions these capabilities as exceptionally smart administrative assistance, but real-world interactions reveal a gap between expectations and performance. The complexity of AI technology contributes to shortcomings, and even simple tools commonly fail to meet practical requirements.
Read at Computerworld
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