Policy Document · 2026-08-01
2026 Electricity Statement of Opportunities for the National Electricity Market
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Scientific Paper
Energy and Policy Considerations for Deep Learning in NLP
Strubell, Emma; Ganesh, Ananya; McCallum, Andrew · 2019-06-05
What it covers
Training large neural networks improves NLP accuracy but requires significant computational resources, leading to high financial and environmental costs.
Who it helps
Intended audience has not been documented.
Limitations
Source-specific limitations have not been reviewed. Inclusion and topic labels do not establish effectiveness or reliability.
Show citation
Strubell, Emma; Ganesh, Ananya; McCallum, Andrew. 2019-06-05. Energy and Policy Considerations for Deep Learning in NLP. https://arxiv.org/abs/1906.02243 (AI & Environment Resource Hub; record paper-002; collection snapshot 2026-10-04).
Classification and context
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
lifecycle
Available source metadata
- Author or creator
- Strubell, Emma; Ganesh, Ananya; McCallum, Andrew
- Publisher
- arXiv
- Publication date
- 2019-06-05
- Date precision
- day
- Geographic scope
- Unknown
- Language
- unknown
- Peer review
- unknown
- Source access
- Reachable at last check
- Metadata confidence
- Required metadata corroborated
- Last verified
- 2026-09-28T20:11:33.060Z
- Snapshot import
- 2026-09-06
- Legacy domain
- Energy
- Legacy subdomain
- AI Energy Consumption in NLP
- journal
- arXiv
Provenance and review
- Source access
- Reachable at last check
- Metadata confidence
- Required metadata corroborated
- Editorial relevance
- Relevance has not been separately reviewed.
- Evidence quality
- Evidence quality has not been separately assessed.
Source sheet: Scientific Paper · Row 6 · Original ID: paper-002.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-28. Source identity and required metadata verified. The import date is not the original date added.
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Related through shared environmental topics
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Energy and electricity
Shared topics: Energy and electricity
Policy Document · 2026-05-01
24/7 Renewables: The Economics of Firm Solar and Wind
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Shared topics: Energy and electricity
Scientific Paper · 2026-09-15
A Strategic Roadmap for Sustainable Data Center Deployment
This article proposes a five-step state policy roadmap for data center development: conduct environmental and community impact studies, use the findings in siting decisions, reassess incentives, require large facilities to pair with off-grid clean energy and storage, and mandate community-benefit sharing. It focuses on protecting grid reliability, electricity affordability, environmental integrity, and host communities.
Shared topics: Energy and electricity
Scientific Paper · 2025-05-26
A Study on Energy Consumption in AI-Driven Medical Image Segmentation
This study evaluates the energy consumption of AI models used in medical image segmentation and finds that Depthwise Convolution with Mixed Precision offers the most energy-efficient training configuration, highlighting the need for GPU-aware, sustainable AI practices.