Garbage in, Garbage Out: The Importance of Data Quality when Training AI Models

Garbage in, garbage out: how bad data can fool AI

In the realm of artificial intelligence, the adage of "garbage in, garbage out" holds more weight than ever before.

The AI Race No One’s Talking About? Data Quality

In the AI race, data quality is the unsung hero, not algorithms or compute. Substandard data multiplies errors, undermines fairness, and risks real-world safety. Nfinite’s Alex de Vigan argues that robust, transparent, and human-influenced data pipelines are the bedrock of trustworthy AI, separating leaders from laggards in the next era of innovation.   SwissCognitive Guest Blogger: Alexandre De Vigan – “The AI Race No One’s Talking About? Data …

Garbage in, garbage out: The importance of data quality when training AI models

As every company moves to implement AI in some form or another, data is king. Without quality data to train on, the AI likely won’t deliver the results people are looking for and any investment made into training the model won’t pay off in the way it was intended.   “If you’re training your AI model on poor quality data, you’re likely to get bad results,” explained Robert Stanley, senior director of special projects at Melissa.  According to Sta…