Loss functions in AI measure an algorithm's error relative to the 'ground truth' of the data, helping adjust parameters until the error is minimized.
Selecting the wrong loss function in AI can lead to misleading results, contradicted observations, and obscured experiment outcomes due to mishandling.
Constructing personalized loss functions is becoming common among scientists to avoid these pitfalls, emphasizing the importance of error assessment and selection.
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