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Context:

• The global AI market is valued at 200 billion $. And it is estimated that it could add 15.7 trillion $ to the global economy by 2030. In India, Reliance Industries is building the world's largest data center in partnership with Nvidia.
• However, along with its growth, AI also brings environmental challenges.

Background

• Artificial intelligence (AI) has become an important part of our daily lives. It is shaping the way we work, live, & do business.
• AI refers to technologies that can think like human and take decision.
• While AI existed since the 1950s. But recent advances in computing power & data availability have led to faster growth.

Environmental Impact Across the AI Value Chain

Environmental impact of AI can be seen at different stages of its development:

1. Data centres of AI systems consume a lot of energy.
2. According to the International Energy Agency, data centres are producing 1% of global greenhouse gas emissions.
3. And it is expected to increase as data centre electricity demand is expected to double by 2026.
4. Advanced AI models like Generative AI (ChatGPT) require more computing power than earlier models.
5. It is also leading to high r demand for graphic processing units (GPUs), which in turn raises energy use & environmental impact.
6. The growing number of data centres also making problem of e-waste. Because older hardware is discarded & replaced with newer powerful systems.
7. AI software also causes emissions during processes like data collection, model development, training, validation, & retirement.
8. For example, training a large AI model like GPT-3 can produce 500 - 600 tonnes of CO2 emissions. And this is equivalent to the annual emissions of dozens of cars.

Global Awareness and Regulatory Efforts

• Governments & organisations around the world are beginning to acknowledge the environmental costs of AI.
• At COP29, the International Telecommunication Union (ITU) highlighted the need for green AI practices.
• Countries such as the EU & US proposed or implemented laws to reduce the environmental impact of AI.
• However, many countries are still slow to address the issue, & the environmental risks of AI are often ignored in national AI strategies.

The Way Forward: Balancing Innovation with Environmental Responsibility

To reduce the environmental impact of AI while promoting innovation, several actions are necessary:

1. Companies should focus on using renewable energy to power data centers or purchasing carbon credits to offset emissions.
2. Data centers should be located in areas where abundant renewable resources are present . This can reduce the carbon footprint.
3. AI can help to manage energy grids more efficiently, such as Google's DeepMind uses AI to improve wind power forecasting. This allows for better integration of wind power.
4. Transitioning to more energy efficient hardware & regularly maintaining equipment can reduce emissions.
5. Building smaller, domain specific AI models that perform only a few tasks can use less energy.
6. Research shows that the carbon footprint of large language models can be reduced by up to 1,000 times with better algorithms & energy efficient hardware.
7. Rather than building new models or collecting new data each time, businesses can adapt pre trained models for different tasks.
8. This reduces the need for extensive new training & reduces energy consumption.
9. Companies should measure & disclose the environmental impact of their AI systems.
10. Having clear standards for tracking & comparing emissions will help ensure accountability and uniformity across the industry.

Conclusion

AI has great potential to drive economic growth. But it also brings environmental challenges. It is important for governments, companies and researchers to make sustainability a key part of AI development. By balancing innovation with responsibility, we can ensure the benefits of AI without harming the environment.

Source: TH