Limits and Cautions in AI Learning
Overfitting explained:
Models can memorize data instead of learning general patterns.
Example: a model trained only on one year’s data fails to predict trends in another year.
Bad data produces bad AI:
Biased data leads to biased decisions.
Example: hiring algorithms based on discriminatory historical data.
Practical examples:
Hybrid and data-driven AI: recommendations and searches.
Example: Google Search, Netflix, Amazon.
Limits of machine learning:
Narrow AI performs only specific tasks and does not understand cultural context or irony.
Example: automatic translators misinterpret idiomatic expressions.
Why AI doesn’t learn like humans:
It learns statistical patterns, not meaning or context. Example: automatic translators understand words, but not complex idiomatic expressions.
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