Deep learning
In more depth
Deep learning networks pass data through stacked layers of simple mathematical units, with each layer learning progressively more abstract features—edges become shapes, words become meanings. The approach became dominant in the 2010s as computing power and available data grew, and it is the technology behind transformers and large language models. Its results are powerful but difficult to interpret, which is one reason explaining exactly why an AI system reached a particular conclusion can be hard.
Further reading: Wikipedia.
Related terms
Educational information, not legal advice. AI terminology and tools change quickly; definitions reflect usage as of the last-updated date. For what bar associations and courts actually require of lawyers using AI, see legalaicompliance.help and consult a licensed attorney in your jurisdiction.