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shallow learning

What It Means

Shallow learning refers to AI systems where humans or simple algorithms first identify and extract the important features from raw data, then feed those pre-processed features into a learning algorithm. Unlike deep learning which automatically discovers patterns in raw data, shallow learning requires explicit feature engineering upfront.

Why Chief AI Officers Care

Shallow learning systems are often more transparent and easier to explain to regulators and stakeholders since the features are manually defined and understood. They typically require less computational power and training data than deep learning, making them more cost-effective for many business problems, though they may not capture complex patterns that deep learning can find.

Real-World Example

A bank uses shallow learning to detect credit card fraud by having analysts manually identify 20 key indicators like transaction amount, merchant type, and time of day, then feeding these specific features into a machine learning model. The system works well because experts predetermined what signals matter most for fraud detection.

Common Confusion

People often think shallow learning is inferior or outdated compared to deep learning, but it's actually the better choice for many business problems where you need explainable results, have limited data, or want faster, cheaper solutions.

Industry-Specific Applications

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See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.

Healthcare: In healthcare, shallow learning typically involves clinical experts manually identifying relevant features from patient ...

Finance: In finance, shallow learning typically involves human experts manually crafting features like financial ratios, moving a...

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Technical Definitions

NISTNational Institute of Standards and Technology
"Techniques that separate the process of feature extraction from learning itself."
Source: Reznik,_Leon

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