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HomeHomework Helpartificial-intelligenceAI Development Exceptions

AI Development Exceptions

Exemptions in AI regulations for certain developers and uses.

intermediate
3 hours
Artificial Intelligence
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Overview

AI development exceptions are crucial to understand as they can lead to significant issues in AI systems. By recognizing common exceptions like overfitting and data bias, developers can create more robust models. Debugging and performance optimization are essential skills that help in addressing the...

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Key Terms

Exception
An event that disrupts the normal flow of a program's execution.

Example: A division by zero error in a calculation.

Overfitting
A modeling error that occurs when a model learns the noise in the training data instead of the actual pattern.

Example: A complex model that performs well on training data but poorly on new data.

Underfitting
A modeling error that occurs when a model is too simple to capture the underlying trend of the data.

Example: A linear model trying to fit a complex, non-linear dataset.

Data Bias
A systematic error that occurs when the data used to train a model is not representative of the real-world scenario.

Example: A facial recognition system trained mostly on images of one ethnicity.

Debugging
The process of identifying and removing errors from computer hardware or software.

Example: Using print statements to track variable values during program execution.

Algorithm Efficiency
A measure of the computational resources required by an algorithm to complete its task.

Example: An algorithm that sorts data in O(n log n) time is more efficient than one that sorts in O(n²) time.

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Key Concepts

Error HandlingDebuggingModel LimitationsPerformance Issues