Artificial Intelligence (AI) is widely used in various applications, but it often makes mistakes. The core issue lies in AI's lack of understanding; it identifies patterns in data without true comprehension. AI operates by processing numbers and patterns, not feelings or instincts. Bad data used for training AI can lead to poor outcomes, as it learns from flawed examples. This can drastically affect important areas like safety and trust, especially when the training data is outdated or biased.
AI is not a smart robot; it’s a computer program trained to spot patterns in large piles of data without understanding. It simply sees numbers, patterns, and predictions.
If the training data for AI is flawed, the AI’s performance will reflect those errors, resulting in significant mistakes that can affect safety, trust, and financial well-being.
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