#FactCheck: Debunking the Edited Image Claim of PM Modi with Hafiz Saeed

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

The Australian Parliament has passed the world’s first legislation regarding a ban on social media for children under 16. This was done citing risks to the mental and physical well-being of children and the need to contain misogynistic influence on them. The debate surrounding the legislation is raging strong, as it is the first proposal of its kind and would set precedence for how other countries can assess their laws regarding children and social media platforms and their priorities. 

The Legislation

Currently trailing an age-verification system (such as biometrics or government identification), the legislation mandates a complete ban on underage children using social media, setting the age limit to 16 or above. Further, the law does not provide exemptions of any kind, be it for pre-existing accounts or parental consent. With federal elections approaching, the law seeks to address parental concerns regarding measures to protect their children from threats lurking on social media platforms. Every step in this regard is being observed with keen interest. 

The Australian Prime Minister, Anthony Albanese, emphasised that the onus of taking responsible steps toward preventing access falls on the social media platforms, absolving parents and their children of the same. Social media platforms like TikTok, X, and Meta Platforms’ Facebook and Instagram all come under the purview of this legislation.

CyberPeace Overview

The issue of a complete age-based ban raises a few concerns:

1. It is challenging to enforce digitally as children might find a way to circumnavigate such restrictions. An example would be the Cinderella Law, formally known as the Shutdown Law, which the Government of South Korea had implemented back in 2011 to reduce online gaming and promote healthy sleeping habits among children. The law mandated the prohibition of access to online gaming for children under the age of 16 between 12 A.M. to 6 A.M. However, a few drawbacks rendered it less effective over time. Children were able to use the login IDs of adults, switch to VPN, and even switch to offline gaming. In addition, parents also felt the government was infringing on the right to privacy and the restrictions were only for online PC games and did not extend to mobile phones. Consequently, the law lost relevance and was repealed in 2021.  
2. The concept of age verification inherently requires collecting more personal data and inadvertently opens up concerns regarding individual privacy. 
3. A ban is likely to reduce the pressure on tech and social media companies to develop and work on areas that would make their services a safe child-friendly environment. 

Conclusion

Social media platforms can opt for an approach that focuses on how to create a safe environment online for children as they continue to deliberate on restrictions. An example of an impactful-yet-balanced step towards the protection of children on social media while respecting privacy is the U.K.'s Age-Appropriate Design Code (UK AADC). It is the U.K.’s implementation of the European Union’s General Data Protection Regulation (GDPR), prepared by the ICO (Information Commissioner's Office), the U.K. data protection regulator. It follows a safety-by-design approach for children. As we move towards a future that is predominantly online, we must continue to strive and create a safe space for children and address issues in innovative ways.

References

1. https://indianexpress.com/article/technology/social/australia-proposes-ban-on-social-media-for-children-under-16-9657544/
2. https://www.thehindu.com/opinion/op-ed/should-children-be-barred-from-social-media/article68661342.ece
3. https://forumias.com/blog/debates-on-whether-children-should-be-banned-from-social-media/
4. https://timesofindia.indiatimes.com/education/news/why-banning-kids-from-social-media-wont-solve-the-youth-mental-health-crisis/articleshow/113328111.cms
5. https://iapp.org/news/a/childrens-privacy-laws-and-freedom-of-expression-lessons-from-the-uk-age-appropriate-design-code
6. https://www.techinasia.com/s-koreas-cinderella-law-finally-growing-up-teens-may-soon-be-able-to-play-online-after-midnight-again
7. https://wp.towson.edu/iajournal/2021/12/13/video-gaming-addiction-a-case-study-of-china-and-south-korea/
8. https://www.dailysabah.com/world/asia-pacific/australia-passes-worlds-1st-total-social-media-ban-for-children

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Executive Summary:

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A viral video depicting a powerful tsunami wave destroying coastal infrastructure is being falsely associated with the recent tsunami warning in Japan following an earthquake in Russia. Fact-checking through reverse image search reveals that the footage is from a 2017 tsunami in Greenland, triggered by a massive landslide in the Karrat Fjord.

Claim:

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A viral video circulating on social media shows a massive tsunami wave crashing into the coastline, destroying boats and surrounding infrastructure. The footage is being falsely linked to the recent tsunami warning issued in Japan following an earthquake in Russia. However, initial verification suggests that the video is unrelated to the current event and may be from a previous incident.

Fact Check:

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The video, which shows water forcefully inundating a coastal area, is neither recent nor related to the current tsunami event in Japan. A reverse image search conducted using keyframes extracted from the viral footage confirms that it is being misrepresented. The video actually originates from a tsunami that struck Greenland in 2017. The original footage is available on YouTube and has no connection to the recent earthquake-induced tsunami warning in Japan

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The American Geophysical Union (AGU) confirmed in a blog post on June 19, 2017, that the deadly Greenland tsunami on June 17, 2017, was caused by a massive landslide. Millions of cubic meters of rock were dumped into the Karrat Fjord by the landslide, creating a wave that was more than 90 meters high and destroying the village of Nuugaatsiaq. A similar news article from The Guardian can be found. 

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Conclusion:

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Videos purporting to depict the effects of a recent tsunami  in Japan are deceptive and repurposed from unrelated incidents. Users of social media are urged to confirm the legitimacy of such content before sharing it, particularly during natural disasters when false information can exacerbate public anxiety and confusion.

• Claim: Recent natural disasters in Russia are being censored
• Claimed On: Social Media
• Fact Check: False and Misleading

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