Geoffrey Hinton

Assembled from 22 blocks · 2 evidence · 18 related

1. Story
2. The computer scientist whose group produced the 2012 ImageNet result that made deep learning the default method in vision, and who is also the author of the most-quoted wrong prediction in the field: that radiologist training should stop because deep learning would supersede human image reading within about five years, said publicly in 2016. Both facts belong in the same record, and keeping them together is most of what this Journey is for. The technical judgement was correct and early; the institutional judgement about what would follow from it was not, and the person best placed to make it got it wrong in the direction of speed and completeness. He has since acknowledged the prediction missed on timing and on breadth. He shared the 2024 Nobel Prize in Physics for foundational work on machine learning with neural networks.
3. Knowledge
4. Machine learning in medical imaging
5. Geoffrey Hinton
6. A deep convolutional network wins the ImageNet challenge
7. Connections
8. Machine learning in medical imaging
9. A deep convolutional network wins the ImageNet challenge
10. A deep convolutional network wins the ImageNet challenge
11. Deep learning
12. European Union Artificial Intelligence Act
13. Geoffrey Hinton
14. A randomised screening trial reports what AI-supported mammography actually changes
15. Geoffrey Hinton
16. Deep learning
17. ImageNet
18. Geoffrey Hinton
19. The AI accelerator
20. Evidence
21. An eight-layer convolutional network — five convolutional and three fully connected layers — trained on graphics processors with dropout regularisation achieved a top-5 error rate of 15.3% at ILSVRC 2012 against 26.2% for the second-placed entry, learning hierarchical features rather than using hand-designed ones. V55 verification basis: the paper was not fetched. The 15.3% and 26.2% figures and the architecture description were carried in multiple retrieved summaries and in a hosted copy of the paper itself, but the attribution in those summaries is to secondary explainers as often as to the paper, and a curator should confirm both error rates against the ILSVRC 2012 results table before publication.
22. An ACR workforce study published in February 2025 using CMS data for 2014–2023 found 37,482 radiologists enrolled to provide care to Medicare patients in 2023 and projected growth of 25.7% to 40.3% by 2055 depending on residency expansion; United States employment in the occupation is projected to grow 5% from 2024 to 2034, above the 3% all-occupation average. In 2016 Geoffrey Hinton publicly advised that training of radiologists should stop, on the expectation that deep learning would supersede human image reading within about five years; he has since acknowledged the prediction was wrong on timing and stated too broadly. V55 verification basis: neither the ACR study nor the BLS projection page was fetched. The 37,482 count, the projection range, the 5% growth figure and the account of Hinton's remark and his later acknowledgement were carried in search results attributing them to the ACR study, to BLS projections and to contemporary reporting; the wording and occasion of the 2016 remark were NOT confirmed from a primary record.