active learning
What It Means
Active learning is a smart approach where AI systems identify what they don't know well and specifically request more training data in those weak areas, rather than being fed random examples. It's like a student who can ask for extra practice problems in subjects where they're struggling, making the learning process more efficient and targeted.
Why Chief AI Officers Care
This dramatically reduces the cost and time needed to train accurate AI models by requiring 50-90% less labeled training data, while often achieving better performance than traditional methods that use massive random datasets.
Real-World Example
A fraud detection system that initially struggles to identify new types of credit card scams can automatically flag uncertain transactions for human review, learn from those expert decisions, and quickly improve its accuracy in those specific fraud patterns without needing millions of additional random transactions.
Common Confusion
People often think this means the AI learns continuously from all user interactions, when it actually means the AI strategically selects specific examples where human input would be most valuable for improving its performance.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, active learning enables diagnostic models to identify uncertain cases and request expert annotations f...
Finance: In finance, active learning enables AI models to intelligently request additional training data for challenging cases li...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"A proposed method for modifying machine learning algorithms by allowing them to specify test regions to improve their accuracy. At any point, the algorithm can choose a new point x, observe the output and incorporate the new (x, y) pair into its training base. It has been applied to neural networks, prediction functions, and clustering functions."Source: Raynor
"Active learning (also called “query learning,” or sometimes “optimal experimental design” in the statistics literature) is a subfield of machine learning and, more generally, artificial intelligence. The key hypothesis is that, if the learning algorithm is allowed to choose the data from which it learns—to be “curious,” if you will—it will perform better with less training. "Source: settles_active_2009
"the process of learning through activities and/or discussion in class, as opposed to passively listening to an expert. "Source: Freeman_et_al_2014
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