Discriminative AI
Discriminative AI refers to models that classify or score existing inputs rather than generating new content, learning the boundary between classes from labelled data.
What is Discriminative AI?
Discriminative models include logistic regression, gradient-boosted trees, support vector machines, and discriminative neural networks. They are trained to predict a label or score given an input, optimising metrics like accuracy and AUC. Compared to generative AI, discriminative systems are usually smaller, faster, cheaper to run, and easier to evaluate. Many enterprise workflows that look like AI to the buyer (fraud scoring, churn prediction, document routing) are discriminative under the hood.
How does Discriminative AI apply to enterprise AI?
Discriminative AI is the right tool for high-volume, high-stakes scoring with clear ground truth and a stable label distribution. Generative AI complements it for drafting and explanation, but the decision itself is often discriminative.
Related terms
- Machine Learning - Machine Learning (ML) is a subfield of AI in which systems learn statistical patterns from data rather than being explicitly programmed with rules.
- Generative AI - Generative AI is the class of AI systems that produce new content (text, images, audio, video, code) rather than only classifying or scoring existing inputs.
- Evaluation Harness - An evaluation harness is the test framework used to measure an AI system against a fixed set of inputs, expected outputs, and metrics, run on every change.
- Model Drift - Model drift is the gradual or sudden degradation of a model's performance in production caused by changes in input data, target distribution, or operating context.
External references
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