COMPEL Glossary / deep-learning
Deep Learning
Deep learning is a subset of machine learning that uses artificial neural networks with many layers (hence 'deep') to automatically learn complex patterns and representations from large amounts of data.
What this means in practice
Deep learning powers most modern AI breakthroughs, including image recognition, natural language understanding, speech synthesis, and generative AI. For non-technical professionals, the key insight is that deep learning models learn from examples rather than being explicitly programmed with rules, which makes them powerful but also harder to explain and govern. In COMPEL, deep learning capabilities are assessed within the Technology pillar during Calibrate, and the governance challenges specific to deep learning, such as reduced interpretability and the need for large training datasets, are addressed in the Governance pillar design during the Model stage.
Why it matters
Deep learning powers most modern AI breakthroughs including image recognition, natural language understanding, and generative AI. For business leaders, the key insight is that deep learning models learn from examples rather than rules, making them powerful but harder to explain and govern. This trade-off between capability and interpretability directly affects governance requirements, regulatory compliance, and stakeholder trust.
How COMPEL uses it
Deep learning capabilities are assessed within the Technology pillar during Calibrate, evaluating the organization's ability to develop, deploy, and maintain deep learning models. During Model, governance challenges specific to deep learning, such as reduced interpretability and large dataset requirements, are addressed in the Governance pillar design. The Evaluate stage applies appropriate performance metrics accounting for deep learning's unique characteristics.
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Related Terms
Other glossary terms mentioned in this entry's definition and context.