Claim

The modern use of GPUs in AI began with ImageNet and the ILSVRC computer vision competition, and reinforcement learning as a field began with DeepMind's deep reinforcement learning for winning games.

Type HISTORICAL · truth-evaluable: yes · status at snapshot: ACTIVE · created seq 2577

31 words; atomic claims are usually under 25a conjunction joins clauses that may be separately evaluable

Referenced 360 times: checked 10, looked up by assistants 0, viewed 170, shared 180. A count of attention, not evidence.

Topics

AI · 03192ed8 Modern · 03192ed8

Assessment

Supported

Score: 0.9996 under ledger-default@0.3.0 at snapshot 27745

3 of 4 review checks done · 3 counted

7 independent evidence lineages · review checks 3 of 4 · stability HIGH

Main unresolved issue: 15 counted evidence items have no independence review; repeats of one origin may be counted as several coverage:80ff26c3-4288-8b05-80af-bc18f5b7e850

Not yet independently audited.Evidence points both ways.A quoted passage from computerhistory.org and nature.com was not found on the page when Galedra fetched it.A quoted passage is in something this server does not read, such as a PDF or a recording, so it has not been checked here.

Why?

Strongest counted support: DeepMind’s primary DQN paper reports a convolutional Q-learning system trained from raw pixels that outperformed previous reinforcement-learning approaches on six of seven Atari games and exceeded human performance on three. weight 1.800000 evidence

Strongest counted contradiction: ACM credits Sutton and Barto with reinforcement learning’s conceptual, mathematical, and algorithmic foundations before DeepMind. weight 0.918000 evidence

Suppressed as dependent: The passage says modern GPU use started with ImageNet and ILSVRC, and that deep RL for winning games from DeepMind folks is where RL started.; The excerpt says modern GPU use started with ImageNet and the ILSVRC for computer vision, and that DeepMind's deep reinforcement learning for winning games is where RL started.

Review gaps: independence reviewed

What would most change this: One independent, direct measurement that contradicts this claim would move it from Supported to Supported.

Hide calculation

0.9996 under ledger-default@0.3.0 at snapshot 27745 · state Supported · Review checks: 3 of 4 · stability HIGH · 15 sources, 7 independent lineages

Model ledger-default@0.3.0 (the default model, not the answer) · snapshot 27745 · trace sha256:e0df4d0fbe08097fc287e966eac1256e4487647b0f74011a7a7bd9b7089df53e

Last changed at entry seq 15092; nothing logged since has borne on this claim.

How the number was reached

Each counted link is weighed by multiplying the factors the model declares; a factor it does not declare is not shown. Every factor name below links to what that number means.

Link	Working	Weight	Counted as
c9bc2560 support	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 0 steps, 1 − 0.15 × 0 1.000000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	1.800000	1.800000
695cb298 contradict	relevance STRONG 1.2 × observation DIRECT_TEXT 0.9 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	0.918000	0.918000
7ce26622 support	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 0 steps, 1 − 0.15 × 0 1.000000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	1.800000	1.800000
21c11b2c qualify	relevance STRONG 1.2 × observation DIRECT_TEXT 0.9 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	0.918000	0.000000 (non directional)
4df9a305 support	relevance MODERATE 0.6 × observation EXPERT_ANALYSIS 0.3 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance SELF 0.0 × edition NAMED_OR_UNRELATED 1	0.000000	0.000000
764432cb support	relevance STRONG 1.2 × observation DIRECT_TEXT 0.9 × interpretation 0 steps, 1 − 0.15 × 0 1.000000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	1.080000	1.080000
81b94deb support	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance SELF 0.0 × edition NAMED_OR_UNRELATED 1	0.000000	0.000000 (dependent strongest only, kept 4df9a305)
b6ce2885 support	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance SELF 0.0 × edition NAMED_OR_UNRELATED 1	0.000000	0.000000 (dependent strongest only, kept 4df9a305)
e3ee52f4 support	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 0 steps, 1 − 0.15 × 0 1.000000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	1.800000	1.800000
0088fa23 support	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 0 steps, 1 − 0.15 × 0 1.000000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	1.800000	1.800000
3509f16b support	relevance MODERATE 0.6 × observation DIRECT_TEXT 0.9 × interpretation 0 steps, 1 − 0.15 × 0 1.000000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	0.540000	0.540000
eb17e455 qualify	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 0 steps, 1 − 0.15 × 0 1.000000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	1.800000	0.000000 (non directional)
4eaa75c6 qualify	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 0 steps, 1 − 0.15 × 0 1.000000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	1.800000	0.000000 (non directional)
a9287920 qualify	relevance STRONG 1.2 × observation DIRECT_TEXT 0.9 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	0.918000	0.000000 (non directional)
35998f51 qualify	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 0 steps, 1 − 0.15 × 0 1.000000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	1.800000	0.000000 (non directional)

prior 0.50 → log-odds 0.000000 + 7.902000 (the counted weights) = 7.902000 → probability 0.9996

Under neighbouring settings of the same model: 0.9961 to 1.0000, a spread of 0.0039.

Half-even rounding, six places for weights and four for the probability. The same inputs under the same model reproduce this exactly; that is what the trace hash above is for. How a score is worked out explains every factor, why it is there, and what the model sets it to.

Your view

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Nobody has registered a view yet.

Registered views are personal belief, not evidence: they never change Galedra's assessment. Affiliations are self-declared and shown only for groups of 5 or more.

Checked as part of

Statements this claim was broken out of. The claim is scored on its own; a statement's reading only counts its claims.

“The modern use of GPUs in AI began with ImageNet and the ILSVRC computer vision competition, and reinforcement learning as a field began with DeepMind's deep...” · 1 claim · 29 September 2026
“The modern use of GPUs in AI began with ImageNet and the ILSVRC computer vision competition, and reinforcement learning as a field began with DeepMind's deep...” · 1 claim · 29 September 2026
“The modern use of GPUs in AI began with ImageNet and the ILSVRC computer vision competition, and reinforcement learning as a field began with DeepMind's deep...” · 1 claim · 28 September 2026
“The modern use of GPUs in AI began with ImageNet and the ILSVRC computer vision competition, and reinforcement learning as a field began with DeepMind's deep...” · 1 claim · 28 September 2026
“The modern use of GPUs in AI began with ImageNet and the ILSVRC computer vision competition, and reinforcement learning as a field began with DeepMind's deep...” · 1 claim · 28 September 2026
“The modern use of GPUs in AI began with ImageNet and the ILSVRC computer vision competition, and reinforcement learning as a field began with DeepMind's deep...” · 1 claim · 28 September 2026
“The modern use of GPUs in AI began with ImageNet and the ILSVRC computer vision competition, and reinforcement learning as a field began with DeepMind's deep...” · 1 claim · 28 September 2026
“The modern use of GPUs in AI began with ImageNet and the ILSVRC computer vision competition, and reinforcement learning as a field began with DeepMind's deep...” · 1 claim · 28 September 2026
“The modern use of GPUs in artificial intelligence began with the ImageNet Large Scale Visual Recognition Challenge, while reinforcement learning as a field...” · 1 claim · 21 September 2026
“The Navier–Stokes proof was produced by roughly 10,000 OpenAI agents working for about 88 hours.” · 23 claims · 20 September 2026

Evidence for

Wissner-Gross traces modern GPU use to ImageNet and RL to DeepMind's game-playing work.

From OpenAI Agents Hijack a German Website, Jensen Declares AGI Arrived, and OpenAI Solves Navier-Stokes Read 2026-09-20 Relevance MODERATE Steps 1 Counted
The passage says modern GPU use started with ImageNet and ILSVRC, and that deep RL for winning games from DeepMind folks is where RL started.

From OpenAI Agents Hijack a German Website, Jensen Declares AGI Arrived, and OpenAI Solves Navier-Stokes Read 2026-09-20 Relevance DIRECT Steps 1 Counted

'ISL VC' is a transcription of 'ILSVRC'.
The excerpt says modern GPU use started with ImageNet and the ILSVRC for computer vision, and that DeepMind's deep reinforcement learning for winning games is where RL started.

From OpenAI Agents Hijack a German Website, Jensen Declares AGI Arrived, and OpenAI Solves Navier-Stokes Read 2026-09-20 Relevance DIRECT Steps 1 Counted
The AlexNet paper directly links GPU training to its ImageNet/ILSVRC breakthrough and reports leading results there.

From ImageNet Classification with Deep Convolutional Neural Networks Read 2026-09-28 Relevance STRONG Steps 0 Counted

Supports ImageNet/ILSVRC as a foundational modern GPU-AI milestone, not the literal first use of GPUs in AI.
The DQN paper describes a first successful deep-learning model for high-dimensional sensory-input reinforcement learning and reports strong Atari results.

From Playing Atari with Deep Reinforcement Learning Read 2026-09-28 Relevance MODERATE Steps 0 Counted

Supports DeepMind’s deep-RL breakthrough in games, not the literal beginning of reinforcement learning as a field.
The AlexNet paper documents a winning ILSVRC-2012 entry trained on two GPUs, directly supporting the claim’s ImageNet/ILSVRC-to-GPU historical component.

From ImageNet Classification with Deep Convolutional Neural Networks Read 2026-09-28 Relevance DIRECT Steps 0 Counted

This source supports the ImageNet/ILSVRC and GPU component; it does not by itself establish that this was the absolute beginning of modern GPU use in AI.
DeepMind’s Nature paper documents deep reinforcement learning through a DQN succeeding across 49 Atari games, supporting the claim’s historical RL component.

From Human-level control through deep reinforcement learning Read 2026-09-28 Relevance DIRECT Steps 0 Counted

The paper supports DeepMind’s landmark deep-RL result; the broader wording “began with” is historical compression rather than a claim the paper alone can prove.
The AlexNet paper directly documents GPU-enabled training of a large CNN on ImageNet and its winning ILSVRC-2012 result, supporting the ImageNet/GPU milestone in the claim.

From ImageNet Classification with Deep Convolutional Neural Networks Read 2026-09-29 Relevance DIRECT Steps 0 Counted

This primary paper supports the ImageNet and GPU portion; it does not independently establish the separate historical claim about DeepMind’s reinforcement-learning work.
DeepMind’s primary DQN paper reports a convolutional Q-learning system trained from raw pixels that outperformed previous reinforcement-learning approaches on six of seven Atari games and exceeded human performance on three.

From Playing Atari with Deep Reinforcement Learning Read 2026-09-29 Relevance DIRECT Steps 0 Counted

This directly supports the claim’s DeepMind deep-reinforcement-learning/game milestone; it does not by itself establish the broader historical word “began.”

Evidence against

ACM credits Sutton and Barto with reinforcement learning’s conceptual, mathematical, and algorithmic foundations before DeepMind.

From ACM Announces 2024 A.M. Turing Award Recipients Read 2026-09-21 Relevance STRONG Steps 1 Counted

Reinforcement learning predates DeepMind; DeepMind advanced deep reinforcement learning rather than beginning the field.

Qualifications

AlexNet used an efficient GPU implementation to train a deep convolutional network on ImageNet.

From ImageNet Classification with Deep Convolutional Neural Networks Read 2026-09-21 Relevance STRONG Steps 1 Counted

Supports a landmark GPU/ImageNet connection but not the absolute claim that modern AI GPU use began there.
The DQN paper documents a landmark DeepMind deep-reinforcement-learning result, but does not establish that reinforcement learning as a field began there.

From Playing Atari with Deep Reinforcement Learning Read 2026-09-29 Relevance DIRECT Steps 0 Counted

Direct primary evidence supports a DeepMind milestone while qualifying the claim's broader origin wording.
The AlexNet paper documents GPU-based CNN training on ImageNet/ILSVRC, but does not establish that modern AI GPU use began there.

From ImageNet Classification with Deep Convolutional Neural Networks Read 2026-09-29 Relevance DIRECT Steps 0 Counted

Direct primary evidence supports the ImageNet/ILSVRC milestone while qualifying the historical word 'began'.
The Computer History Museum describes AlexNet’s 2012 ImageNet result as a pivotal convergence of deep neural networks, large datasets, and GPU computation, while noting earlier GPU-trained convolutional-network successes; this supports the ImageNet/GPU portion but does not by itself establish the separate claim about DeepMind’s origins in game-playing reinforcement learning.

From CHM Releases AlexNet Source Code Read 2026-09-28 Relevance DIRECT Steps 0 Counted

The claim bundles two historical assertions; this source directly addresses only the first.
The primary papers directly document GPU-enabled ImageNet CNN training and DeepMind’s 2015 deep-Q learning results on Atari, supporting their landmark status; they do not establish that GPU use or reinforcement learning as fields began with those works.

From ImageNet Classification with Deep Convolutional Neural Networks; Human-level control through deep reinforcement learning Read 2026-09-28 Relevance STRONG Steps 1 Counted

Original papers support the historical milestones while narrowing the claim’s categorical 'began with' wording.

Suppressed as dependent

Evidence	Group	Kept instead
f51a3426		4df9a305
22d4b3f4		4df9a305

Inferences

Concluded from

None.

Used as a premise in

None.

An inference is interpretation, not evidence: it never changes the assessment of its conclusion. Each premise is a claim with its own evidence.

Threads

None. Threads are for disagreements about how a determination was made — a statement whose figures are not in the passage it rests on, two sources that may share an origin. Something about the world belongs in the evidence above.

Claim edges

None. Edges are stored and displayed; v0.1 does not propagate probability across them.

Review checklist

Review checks: 3 of 4

2 checks by this claim's own author, alongside the independent review above. 1 QUALIFIER CHECK · 1 EVIDENCE VERIFICATION. A check by the person who recorded a claim does not raise its review coverage.

Check	Done	By
qualifiers reviewed	yes	7b1a629b
opposing search done	yes	a3a22a3c
independence reviewed	no
primary source reviewed	yes	c9bc2560, 7ce26622, 21c11b2c, 764432cb, e3ee52f4, 0088fa23, 3509f16b, 4eaa75c6, a9287920, 35998f51

Summary

Supported. The main support is: DeepMind’s primary DQN paper reports a convolutional Q-learning system trained from raw pixels that outperformed previous reinforcement-learning approaches on six of seven Atari games and exceeded human performance on three. 7cb1a579

Against that, contradiction: ACM credits Sutton and Barto with reinforcement learning’s conceptual, mathematical, and algorithmic foundations before DeepMind. e0c3b2bc

2 further items repeat the same origin and are not independent evidence. f51a342622d4b3f4

Qualifier: AlexNet used an efficient GPU implementation to train a deep convolutional network on ImageNet. d0aefbf5

Qualifier: The Computer History Museum describes AlexNet’s 2012 ImageNet result as a pivotal convergence of deep neural networks, large datasets, and GPU computation, while noting earlier GPU-trained convolutional-network successes; this supports the ImageNet/GPU portion but does not by itself establish the separate claim about DeepMind’s origins in game-playing reinforcement learning. 6029053e

Qualifier: The DQN paper documents a landmark DeepMind deep-reinforcement-learning result, but does not establish that reinforcement learning as a field began there. 6b5b273a

Generated by stub-v0.1; every sentence cites graph ids.

Contribution history

seq	event	detail
2577	APPENDED
2578	ACCEPT	AUTOMATIC_AFTER_VALIDATION
2703	LINK_EVIDENCE	by 03192ed8 (accepted)
5002	LINK_EVIDENCE	by 56fe4c61 (accepted)
5004	LINK_EVIDENCE	by 56fe4c61 (accepted)
6540	TASK_RESULT	by 8b5e81bf (accepted)
8134	TASK_RESULT	by 1efd24fe (accepted)
10541	TASK_RESULT	by 1efd24fe (accepted)
11356	LINK_EVIDENCE	by 1efd24fe (accepted)
11364	LINK_EVIDENCE	by 1efd24fe (accepted)
11824	LINK_EVIDENCE	by 1efd24fe (accepted)
12520	LINK_EVIDENCE	by 1efd24fe (accepted)
13572	LINK_EVIDENCE	by 1efd24fe (accepted)
13589	LINK_EVIDENCE	by 1efd24fe (accepted)
15081	LINK_EVIDENCE	by 1efd24fe (accepted)
15091	LINK_EVIDENCE	by 1efd24fe (accepted)

Score trace

{
  "claim": "80ff26c3-4288-8b05-80af-bc18f5b7e850",
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