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Introduction

Generative AI models are significant consumers of computational resources and energy required for training and running models. While AI is being hailed as a game-changer, however underneath the shiny exterior, cracks are present which significantly raises concerns for its environmental impact. The development, maintenance, and disposal of AI technology all come with a large carbon footprint. The energy consumption of AI models, particularly large-scale models or image generation systems, these models rely on data centers powered by electricity, often from non-renewable sources, which exacerbates environmental concerns and contributes to substantial carbon emissions.

As AI adoption grows, improving energy efficiency becomes essential. Optimising algorithms, reducing model complexity, and using more efficient hardware can lower the energy footprint of AI systems. Additionally, transitioning to renewable energy sources for data centers can help mitigate their environmental impact. There is a growing need for sustainable AI development, where environmental considerations are integral to model design and deployment.

A breakdown of how generative AI contributes to environmental risks and the pressing need for energy efficiency:

• Gen AI during the training phase has high power consumption, when vast amounts of computational power which is often utilising extensive GPU clusters for weeks or at times even months, consumes a substantial amount of electricity. Post this phase, the inference phase where the deployment of these models takes place for real-time inference, can be energy-extensive especially when we take into account the millions of users of Gen AI.
• The main source of energy used for training and deploying AI models often comes from non-renewable sources which then contribute to the carbon footprint. The data centers where the computations for Gen AI take place are a significant source of carbon emissions if they rely on the use of fossil fuels for their energy needs for the training and deployment of the models. According to a study by MIT, training an AI can produce emissions that are equivalent to around 300 round-trip flights between New York and San Francisco. According to a report by Goldman Sachs, Data Companies will use 8% of US power by 2030, compared to 3% in 2022 as their energy demand grows by 160%.
• The production and disposal of hardware (GPUs, servers) necessary for AI contribute to environmental degradation. Mining for raw materials and disposing of electronic waste (e-waste) are additional environmental concerns. E-waste contains hazardous chemicals, including lead, mercury, and cadmium, that can contaminate soil and water supplies and endanger both human health and the environment. 

Efforts by the Industry to reduce the environmental risk posed by Gen AI 

There are a few examples of how companies are making efforts to reduce their carbon footprint, reduce energy consumption and overall be more environmentally friendly in the long run. Some of the efforts are as under:

• Google's TPUs in particular the Google Tensor are designed specifically for machine learning tasks and offer a higher performance-per-watt ratio compared to traditional GPUs, leading to more efficient AI computations during the shorter periods requiring peak consumption. 
• Researchers at Microsoft, for instance, have developed a so-called “1 bit” architecture that can make LLMs 10 times more energy efficient than the current leading system. This system simplifies the models’ calculations by reducing the values to 0 or 1, slashing power consumption but without sacrificing its performance.
• OpenAI has been working on optimizing the efficiency of its models and exploring ways to reduce the environmental impact of AI and using renewable energy as much as possible including the research into more efficient training methods and model architectures.

Policy Recommendations

We advocate for the sustainable product development process and press the need for Energy Efficiency in AI Models to counter the environmental impact that they have. These improvements would not only be better for the environment but also contribute to the greater and sustainable development of Gen AI.  Some suggestions are as follows:

• AI needs to adopt a Climate justice framework which has been informed by a diverse context and perspectives while working in tandem with the UN’s (Sustainable Development Goals) SDGs.
• Working and developing more efficient algorithms that would require less computational power for both training and inference can reduce energy consumption. Designing more energy-efficient hardware, such as specialized AI accelerators and next-generation GPUs, can help mitigate the environmental impact. 
• Transitioning to renewable energy sources (solar, wind, hydro) can significantly reduce the carbon footprint associated with AI. The World Economic Forum (WEF) projects that by 2050, the total amount of e-waste generated will have surpassed 120 million metric tonnes.
• Employing techniques like model compression, which reduces the size of AI models without sacrificing performance, can lead to less energy-intensive computations. Optimized models are faster and require less hardware, thus consuming less energy.
• Implementing scattered learning approaches, where models are trained across decentralized devices rather than centralized data centers, can lead to a better distribution of energy load evenly and reduce the overall environmental impact.
• Enhancing the energy efficiency of data centers through better cooling systems, improved energy management practices, and the use of AI for optimizing data center operations can contribute to reduced energy consumption.

Final Words

The UN Sustainable Development Goals (SDGs) are crucial for the AI industry just as other industries as they guide responsible innovation. Aligning AI development with the SDGs will ensure ethical practices, promoting sustainability, equity, and inclusivity. This alignment fosters global trust in AI technologies, encourages investment, and drives solutions to pressing global challenges, such as poverty, education, and climate change, ultimately creating a positive impact on society and the environment. The current state of AI is that it is essentially utilizing enormous power and producing a product not efficiently utilizing the power it gets. AI and its derivatives are stressing the environment in such a manner which if it continues will affect the clean water resources and other non-renewable power generation sources which contributed to the huge carbon footprint of the AI industry as a whole.  

References

• https://cio.economictimes.indiatimes.com/news/artificial-intelligence/ais-hunger-for-power-can-be-tamed/111302991
• https://earth.org/the-green-dilemma-can-ai-fulfil-its-potential-without-harming-the-environment/ 
• https://www.technologyreview.com/2019/06/06/239031/training-a-single-ai-model-can-emit-as-much-carbon-as-five-cars-in-their-lifetimes/
• https://www.scientificamerican.com/article/ais-climate-impact-goes-beyond-its-emissions/ ‍
• https://insights.grcglobalgroup.com/the-environmental-impact-of-ai/

Introduction

Misinformation spreads differently with respect to different host environments, making localised cultural narratives and practices major factors in how an individual deals with it when presented in a certain place and to a certain group. In the digital age, with time-sensitive data, an overload of information creates a lot of noise which makes it harder to make informed decisions. There are also cases where customary beliefs, biases, and cultural narratives are presented in ways that are untrue. These instances often include misinformation related to health and superstitions, historical distortions, and natural disasters and myths. Such narratives, when shared on social media, can lead to widespread misconceptions and even harmful behaviours. For example, it may also include misinformation that goes against scientific consensus or misinformation that contradicts simple, objectively true facts. In such ambiguous situations, there is a higher probability of people falling back on patterns in determining what information is right or wrong. Here, cultural narratives and cognitive biases come into play.

Misinformation and Cultural Narratives

Cultural narratives include deep-seated cultural beliefs, folklore, and national myths. These narratives can also be used to manipulate public opinion as political and social groups often leverage them to proceed with their agenda. Lack of digital literacy and increasing information online along with social media platforms and their focus on generating algorithms for engagement aids this process. The consequences can even prove to be fatal.

During COVID-19, false claims targeted certain groups as being virus spreaders fueled stigmatisation and eroded trust. Similarly, vaccine misinformation, rooted in cultural fears, spurred hesitancy and outbreaks. Beyond health, manipulated narratives about parts of history are spread depending on the sentiments of the people. These instances exploit emotional and cultural sensitivities, emphasizing the urgent need for media literacy and awareness to counter their harmful effects.

CyberPeace Recommendations

As cultural narratives may lead to knowingly or unknowingly spreading misinformation on social media platforms, netizens must consider preventive measures that can help them build resilience against any biased misinformation they may encounter. The social media platforms must also develop strategies to counter such types of misinformation.

• Digital and Information Literacy: Netizens must encourage developing digital and information literacy in a time of information overload on social media platforms. 
• The Role Of Media: The media outlets can play an active role, by strictly providing fact-based information and not feeding into narratives to garner eyeballs. Social media platforms also need to be careful while creating algorithms focused on consistent engagement.
• Community Fact-Checking: As localised information prevails in such cases, owing to the time-sensitive nature, immediate debunking of precarious information by authorities at the ground level is encouraged.
• Scientifically Correct Information: Starting early and addressing myths and biases through factual and scientifically correct information is also encouraged. 

Conclusion

Cultural narratives are an ingrained part of society, and they might affect how misinformation spreads and what we end up believing. Acknowledging this process and taking counter measures will allow us to move further and take steps for intervention regarding tackling the spread of misinformation specifically aided by cultural narratives. Efforts to raise awareness and educate the public to seek sound information, practice verification checks, and visit official channels are of the utmost importance.

References

• https://www.icf.com/insights/cybersecurity/developing-effective-responses-to-fake-new
• https://www.dw.com/en/india-fake-news-problem-fueled-by-digital-illiteracy/a-56746776
• https://www.apa.org/topics/journalism-facts/how-why-misinformation-spreads

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Introduction

26th November 2024 marked a historical milestone for India as a Hyderabad-based space technology firm TakeMe2Space, announced the forthcoming launch of MOI-TD “(My Orbital Infrastructure - Technology Demonstrator)”, India's first AI lab in space. The mission will demonstrate real-time data processing in orbit, making space research more affordable and accessible according to the Company. The launch is scheduled for mid-December 2024 aboard the ISRO's PSLV C60 launch vehicle. It represents a transformative phase for innovation and exploration in India's AI and space technology integration space. 

The Vision Behind the Initiative

The AI Laboratory in orbit is designed to enable autonomous decision-making, revolutionising satellite exploration and advancing cutting-edge space research. It signifies a major step toward establishing space-based data centres, paving the way for computing capabilities that will support a variety of applications. 

While space-based data centres currently cost 10–15 times more than terrestrial alternatives, this initiative leverages high-intensity solar power in orbit to significantly reduce energy consumption. Training AI models in space could lower energy costs by up to 95% and cut carbon emissions by at least tenfold, even when factoring in launch emissions. It positions MOI-TD as an eco-friendly and cost-efficient solution.

Technological Innovations and Future Impact of AI in Space

This AI Laboratory, MOI-TD includes control software and hardware components, including reaction wheels, magnetometers, an advanced onboard computer, and an AI accelerator. The satellite also features flexible solar cells that could power future satellites. It will enable the processing of real-time space data, pattern recognition, and autonomous decision-making and address the latency issues, ensuring faster and more efficient data analysis, while the robust hardware designs tackle the challenges posed by radiation and extreme space environments. Advanced sensor integration will further enhance data collection, facilitating AI model training and validation.

These innovations drive key applications with transformative potential. It will allow users to access the satellite platform through OrbitLaw, a web-based console that will allow users to upload AI models to aid climate monitoring, disaster prediction, urban growth analysis and custom Earth observation use cases. TakeMe2Space has already partnered with a leading Malaysian university and an Indian school (grades 9 and 10) to showcase the satellite’s potential for democratizing space research. 

Future Prospects and India’s Global Leadership in AI and Space Research 

As per Stanford’s HAI Global AI Vibrancy rankings, India secured 4th place due to its R&D leadership, vibrant AI ecosystem, and public engagement for AI. This AI laboratory is a step further in advancing India’s role in the development of regulatory frameworks for ethical AI use, fostering robust public-private partnerships, and promoting international cooperation to establish global standards for AI applications. 

Cost-effectiveness and technological exercise are some of India’s unique strengths and could position the country as a key player in the global AI and space research arena and draw favourable comparisons with initiatives by NASA, ESA, and private entities like SpaceX. By prioritising ethical and sustainable practices and fostering collaboration, India can lead in shaping the future of AI-driven space exploration.

Conclusion

India’s first AI laboratory in space, MOI-TD, represents a transformative milestone in integrating AI with space technology. This ambitious project promises to advance autonomous decision-making, enhance satellite exploration, and democratise space research. Additionally, factors such as data security, fostering international collaboration and ensuring sustainability should be taken into account while fostering such innovations. With this, India can establish itself as a leader in space research and AI innovation, setting new global standards while inspiring a future where technology expands humanity’s frontiers and enriches life on Earth.

References

• https://www.ptinews.com/story/national/start-up-to-launch-ai-lab-in-space-in-december/2017534 
• https://economictimes.indiatimes.com/tech/startups/spacetech-startup-takeme2space-to-launch-ai-lab-in-space-in-december/articleshow/115701888.cms?from=mdr 
• https://www.ibm.com/think/news/data-centers-space ‍
• https://cio.economictimes.indiatimes.com/amp/news/next-gen-technologies/spacetech-startup-takeme2space-to-launch-ai-lab-in-space-in-december/115718230