It allowed a model to reuse features learned on one problem when training for another, potentially reducing the amount of new task-specific data needed. The report illustrated this with image-recognition features adapted to skin-lesion classification.
These are research examples from 2018, not clinical recommendations or a measurement of general intelligence. Transfer performance depends on the relationship between the original and new tasks; adversarial examples illustrate vulnerabilities rather than normal-use error frequency.
Evidence you can use
Historical snapshot: June 2018. Dates and populations are specified per row.
| Measure | Reported value | Definition and source |
|---|---|---|
| Transfer-learning example | Skin-lesion classification | Repurposing features from the InceptionV3 image-recognition network.2018 report, PDF page 8 |
| Robustness example | Adversarial image perturbations | Illustrated failure mode of image classifiers; not a population-wide error rate.2018 report, PDF page 48 |
These are research examples from 2018, not clinical recommendations or a measurement of general intelligence. Transfer performance depends on the relationship between the original and new tasks; adversarial examples illustrate vulnerabilities rather than normal-use error frequency.
Historical snapshot published June 29, 2018. This web edition was prepared on 2026-10-11 from the online deck and original launch posts. Findings and forecasts retain their original time frame.
Benaich, Nathan, and Ian Hogarth. “Why did transfer learning matter in 2018?” State of AI Report 2018. Historical report snapshot; web edition prepared 2026-10-11.