Rumelhart, Hinton & Williams popularize backpropagation
Confirmed
Importance: historic (5 of 5)The takeaway
The Nature paper ‘Learning representations by back-propagating errors’ showed that multi-layer neural networks trained with backpropagation learn useful internal representations, reviving neural network research.
Status
- Claim
Confirmed
- Our reporting
- High confidence
- Importance
- Historic (5 of 5)
- Last verified
- 29 September 2026
Your AI and this story
- GPT-6 AstraIn its training data
- Claude Opus 5.5In its training data
- Gemini 4 HydrogenIn its training data
- Grok 4.7In its training data
It happened before the cutoffs of all four assistants, so it can be in their training data.
Key facts
- Published in Nature vol. 323, 9 October 1986
- Authors: David Rumelhart, Geoffrey Hinton, Ronald Williams
- Showed hidden units learn features not present in inputs
- Earlier related work includes Seppo Linnainmaa (1970) and Paul Werbos (1974)
What happened
The paper demonstrated gradient-based training of networks with hidden layers by propagating error derivatives backwards through the network.
Why it matters
Backpropagation is still how essentially all neural networks, including today’s LLMs, are trained.
Sources
2 sources from 2 sites. Numbers match the chips in the text.
2 sources: 1 primary, 1 reaction
Primary
Reactions
- Wikipedia: Backpropagationen.wikipedia.org, discussion
Changes
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