What are the downsides of optimization in AI?

What are the downsides of optimization in AI?

While optimization can enhance performance, it may lead to overfitting, reduced generalization, and increased complexity.

Can you explain overfitting in more detail?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern, which harms its performance on new data.

What are some examples of increased complexity?

Increased complexity can manifest as longer training times, more intricate models that are harder to interpret, and greater resource consumption.

How can we mitigate these downsides?

Mitigation strategies include using regularization techniques, simplifying models, and employing cross-validation to ensure generalization.