Environmental and climate-related impacts of AI-searching
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IntroductionLarge language models (LLMs) such as ChatGPT by OpenAI have significant environmental and climate-related impacts due to their computational power needs, and thus their carbon footprint. The impact of AI-powered searching on the environment will be significant in the future, given the energy demands of data centres and computational processes (see CO2 levels below). AI models, particularly LLMs, require a lot of electricity for training and operation. Data centres housing these systems consume power, sourced from fossil fuels, contributing to greenhouse gas emissions. Training a single large AI model can emit as much carbon dioxide as flying one person an airplane from New York to Paris. The near continuous querying and inference processes in AI services amplify energy consumption, as each search request engages resource-intensive algorithms. Cooling systems for servers add to global carbon footprints, exacerbating environmental issues and climate change concerns. Rapid growth of AI-driven technologies increases demand for hardware, leading to resource extraction and e-waste challenges. The scalability of AI search tools amplifies their environmental footprint as usage grows. However, advancements in energy-efficient algorithms and hardware could reduce impacts. Optimizing data center locations to leverage renewable energy and improving model efficiency are critical steps toward sustainability. The proliferation of AI searching could significantly contribute to climate change, underscoring the need for greener AI infrastructure. While renewable energy adoption in some data centers mitigates impacts, the global reliance on non-renewable sources persists. AI Carbon FootprintThe training of AI models often requires substantial computational power and large datasets, which in turn require significant energy consumption for processing. The deployment and operation of AI systems also contribute to their carbon footprint. AI applications that run on cloud infrastructure or data centres require a lot of power to operate, resulting in considerable energy consumption and carbon emissions. The computations required for deep learning research have increased by 300,000 fold from 2012 to 2018. Manufacturing and disposal of hardware components, AI-specific chips or servers, contribute to carbon footprint. A recent study found that training an off-the-shelf AI language-processing system produced around 600 Kg of emissions, about the amount produced by flying one person roundtrip between New York and San Francisco. There are initiatives to reduce the AI carbon footprint and improve its sustainability. Efforts include developing more energy-efficient algorithms and optimizing computing infrastructure to minimize energy consumption during training and operation. CodeCarbon is a software package that can be integrated into a Python codebase and estimates the amount of CO2 produced by the cloud or personal computing resources used to execute the code. Furthermore, using renewable energy sources to power data centers and adopting energy-efficient hardware designs can also help reduce the environmental impact of AI. AI queries' CO2 footprintWhile ChatGPT generates more carbon dioxide than many of its competitors, Elon Musk's Grok AI is the most environmentally friendly, according to new research. Source: https://www.cyberdaily.au/security/11942-chatgpt-produces-an-estimated-4-32-grams-of-co-for-every-query Here are the differences in AI expressed in grams of CO₂:
ChatGPT produces an estimated 4.32 grams of CO₂ for every query. Perplexity is not far behind with 4 grams of CO₂. Grok AI comes out on top thanks to an architecture that is designed around lower power consumption. Each Grok AI query is roughly on par with a single Google search. Librarian's point of view: if a single Google search requires 0.17 grams of C02, and ChatGPT-4 is 35-40x higher - imagine the power used by tools like Elicit.com or Undermind.ai? RAG tools are particularly computationally-intensive, leading to an environmental impact 25 times (or more) than a single Grok query. One ChatGPT query generates the equivalent amount of CO₂ as sending 21 emails or fully charging one smartphone. Libraries in the climate change eraLibraries are increasingly vital in addressing climate change issues by developing sustainable collections and engaging communities in discussion. Librarians face direct climate impacts on operations, necessitating sustainable practices like energy-efficient systems and expanded digital resources. Libraries can curate materials promoting environmental stewardship, climate literacy, and local resilience, acting as hubs for climate education and community initiatives. Challenges include limited funding, technological barriers, and the need for greater community involvement, especially in vulnerable regions. Integrating Indigenous knowledge and aligning with the United Nations Sustainable Development Goals (SDGs) can strengthen libraries’ role in fostering climate resilience and empowering communities. PresentationVideo asks: As tech companies increase their AI production, the environmental costs are coming to light. What are the resources fuelling the AI revolution? And how does AI impact the tech industry’s climate goals? Will AI drive or solve the climate crisis? References
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