Sustainability & AI
Deep learning
Machine learning using multi-layered neural networks; the dominant approach behind modern AI and its rising compute demand.
Definition
A family of machine-learning methods based on neural networks with many layers, which learn hierarchical representations directly from raw data.
Quick reference
At a glance
- Subject
- Sustainability & AI
- Editorial status
- Editorial draft
- Definition status
- Established
- Last updated
- 21 August 2026
References
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Overview
What it means
Deep learning displaced hand-engineered features across vision, speech and language from the early 2010s, a turning point summarised in the 2015 Nature review by LeCun, Bengio and Hinton. Its accuracy scales with data and computation — which is also the source of its energy and hardware demands.
How it is used
Deep learning underpins most AI used in sustainability: interpreting satellite imagery, forecasting power systems, identifying species and parsing reports. Training large deep networks is compute-intensive, which sparked the 'Green AI' critique of ever-scaling research.
Why it matters
It is the engine of modern AI's usefulness and of its footprint. Understanding deep learning's appetite for data and compute is a prerequisite for judging both what AI can do for sustainability and what it costs.
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