Original Post: AI quality: Garbage in, garbage out
The article underscores the concept of “Garbage In, Garbage Out” (GIGO) in both culinary and AI contexts. Using poor-quality ingredients in cooking results in unappetizing dishes; similarly, poor data inputs in AI systems lead to flawed outputs. An example from an AI course is provided, where students build an expert system to identify animals based on user-provided attributes. If incorrect data is input (e.g., a cat barks), the system fails to produce accurate results. The article emphasizes the importance of good data for AI systems and highlights real-world consequences of poor data quality. It points out that over three-quarters of developers bypass protocols to use code completion tools, risking errors. The piece concludes with references to resources on AI vulnerabilities and security education.
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