Uploaded byDr. Salem Baidas
Green AI
The document discusses the intersection of artificial intelligence (AI) and environmental sustainability, highlighting strategies for reducing energy consumption and carbon footprint in AI operations. It also emphasizes the benefits of sustainable AI practices, including increased efficiency, improved social impact, and compliance with emerging policies. Various organizations are working to promote awareness and develop initiatives to support sustainability in AI, while a significant proportion of companies are actively adopting these practices.
Green AI
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- 2. ENVIRONMENTAL SUSTAINABILITY SLIDESHOWS FROMs2adesign.com S2ADESIGN.COM
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- 4. A Branch ofComputer Science that Focuses on Developing Intelligent Machines Capable of Performing Tasks Requiring Human Intelligence.
- 5. Strategies to Reducethe Carbon Footprint & Energy Consumption Associated with AI Operations.
- 6. Improve energy efficiency Monitorcompliance reducewater consumption Benchmark efforts Improve social impact Improve DATA governance risk MANAGEMENT Reduce emissions MCKINSEY.COM SUSTAINABLE ai goals
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- 8. EFFICIENCY Driving More EfficientBusiness Processes and Daily Operations. transparency Increasing Supply Chain Transparency Towards More Responsible Sourcing and Labor Practices. AUTOMATION Automating the Collection and Reporting of Data Across Complex Operations and Endpoints. support Supporting Corporate Diversity Equity, and Inclusion (DEI) Goals such as AI-Enabled Recruiting Practices. IBM.COM SUSTAINABLE ai STRATEGIES
- 9. SUSTAINABLE ai ECONOMY Saves onEnergy and Resources. Less Waste Generated Means Less Waste Management Costs. SOCIAL Enterprise Social Image Improvement: Many Customers Prefer to Buy from Businesses with Sustainable Practices. ENVIRONMENT Minimizes Negative Environmental Impacts, Carbon Footprint, and GHG Emissions. benefits THEDIGITALSPEAKER.COM
- 10. Over the PastDecade, the Amount of Data Used to Train AI Systems has Significantly Increased, Consuming a Large Amount of Energy. Energy use The Growth of Data has Resulted in a Rapid Increase in the Size of Computational Models, Consuming a Large Amount of Energy. Model size The Growing Use of AI is Leading to an Increase in the Carbon Footprint such as Emissions & Energy Consumption. Ai expansion Lack of Policies & Regulations Supporting Sustainable AI Strategies. policies Lack of Awareness & Education Promoting Sustainable AI Practices. awareness SUSTAINABLE ai CHALLENGES TECHOPEDIA.COM
- 11. Prioritizing Data Qualityover Quantity can Significantly Reduce Energy Use: Using High-Quality Data & Avoiding Data Duplication. Energy use Using Smaller Models Eliminates the Need for Powerful Computers. Techniques Include Model Compression & Knowledge Distillation. Model size Developing Energy-Efficient Hardware Specifically for AI Tasks. Using Renewable Energy Sources Like Solar Power for Data Centers. Ai expansion Implementing Policies to Support Sustainable AI Operations as well as Research & Development. Rewarding Energy-Efficient AI Systems. policies Launching Programs to Teach AI Professionals about Energy-Saving Practices. Raising Awareness of Individuals and Organizations. awareness SUSTAINABLE ai solutions TECHOPEDIA.COM
- 13. In 2021, UnderUNESCO's Auspices the International Research Center on Artificial Intelligence (IRCAI) was Established to Advance Research on the Use of Artificial Intelligence to Achieve the SDGs by Showcasing Relevant Projects Worldwide. IRCAI.ORG UNESCO Center in AI AI for Sustainability
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- 16. tons – CO2Equivalent AI CAR HUMAN LIFE AIINDEX.STANFORD.EDU ANNUAL AI EMISSIONScomparison IN 2023
- 17. 502 352 76 25 GPT-3 GROPHER OPT BLOOM EMISSIONS FROM AIbased LLMs IN 2023 tons – CO2 Equivalent ARXIV.ORG
- 18. 1,287 1,066 433 324 GPT-3 GROPHER BLOOM OPT ENERGY CONSUMPTION FROMAI based LLMS IN 2023 megawatt hours ARXIV.ORG
- 19. Ai environmentalimpact worldwidein 2023 502Tons Global Emissions from AI Based GPT-3. The Largest by LLM. 1.3GWH Energy Consumption of AI Based GPT-3. The Largest by LLM. 955Tons Global Emissions from AI Based Large Language Models (LLMs). Key highlights ARXIV.ORG
- 21. 53 47 46 36 31 Developing Markets Asia-Pacific Greater China Europe North America companiestakingsteps to reduce AI emissions percent (2022) MCKINSEY.COM
- 22. Share initiative 62% Improving the environmentalimpact (e.g. energy efficiency, emissions) 51% Evaluating sustainabilityefforts(e.g. benchmarking) 45% Improving governance(e.g. regulatory compliance,risk management) 34% Improving the socialimpact (e.g. sourcingethical products) Sustainability efforts using ai worldwide Share of companies(2022) MCKINSEY.COM
- 23. 61 54 44 39 30 Greater China Asia-Pacific Developing Markets Europe North America MCKINSEY.COM Sustainabilityefforts using ai worldwide Share of companies(2022)
- 24. 62% Share of Companies UsingAI to Reduce their Environmental Impact Worldwide. 61% Share of Companies in the Greater China Region Using AI in their Sustainability Efforts. 53% Share of Companies in the Developing Market Region Taking Steps to Reduce AI Emissions. Key highlights MCKINSEY.COM Sustainability efforts using ai worldwide in 2022
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