CURRENT AFFAIRS
CURRENT AFFAIRS
Description
As AI applications become more widespread, a recent analysis published in the journal Joules warns that the global electricity required by AI might increase by 85-134 Terawatt-hours (TWh) every year by 2027. This consumption level is comparable to annual electricity use in nations such as the Netherlands, Argentina, and Sweden.
According to Alex de Vries, a doctorate candidate at Vrije Universiteit Amsterdam, the rising demand for AI services makes it highly likely that AI-related energy usage will skyrocket in the next years. The report emphasizes that as AI develops faster, there are growing concerns about the environmental impact and electricity usage, particularly in data centers that enable these technologies.
The Rise of Generative AI
Generative AI, used to create new content such as text, images or videos, enjoys great popularity. Notable examples include ChatGPT and DALL-E, which use natural language processing to understand and generate human-like text and spoken language. These AI models go through two main phases: training and inference.
The training phase consumes a lot of energy, especially for large language models such as GPT-3. For example, GPT-3 would have used 1,287 MWh of electricity for training. However, the paper highlights the need for further research into the relative energy consumption of the inference phase following training.
For example, ChatGPT consumes 564 MWh of electricity per day during the inference phase, compared to 1,287 MWh for training. Understanding the power requirements of each phase is key to assessing the overall power consumption of the AI.
AI in Everyday Searches
The report notes that daily electricity consumption could reach 80 GWh if generative AI was used in every Google search. In 2021, Google's annual electricity consumption was 18.3 TWh, of which 10-15% came from AI. In a worst-case scenario, Google's AI power consumption could rival Ireland's 29.3 TWh if AI were adopted at large scale.
The Path Forward
As the rapid growth of AI poses challenges in energy consumption, the report suggests possible solutions. Innovations in model architectures and algorithms could help mitigate or even reduce AI-related energy consumption in the long term. Alex de Vries highlights the need for mindfulness when using AI, especially in applications where its energy-intensive nature may not be necessary. As AI continues to transform various industries, a balanced approach between technological advancement and sustainability becomes increasingly important.
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