#FactCheck - False Claim of Italian PM Congratulating on Ram Temple, Reveals Birthday Thanks

Introduction

Digital Public Infrastructure (DPI) serves as the backbone of e-governance, enabling governments to deliver services more efficiently, transparently, and inclusively. By leveraging information and communication technology (ICT), digital governance systems reconfigure traditional administrative processes, making them more accessible and citizen-centric. However, the successful implementation of such systems hinges on overcoming several challenges, from ensuring data security to fostering digital literacy and addressing infrastructural gaps.

This article delves into the key enablers that drive effective DPI and outlines the measures already undertaken by the government to enhance its functionality. Furthermore, it outlines strategies for their enhancement, emphasizing the need for a collaborative, secure, and adaptive approach to building robust e-governance systems.

Key Enablers of DPI

Digital Public Infrastructure (DPI), the foundation for e-governance,  relies on common design, robust governance, and private sector participation for efficiency and inclusivity. This requires common principles, frameworks for collaboration, capacity building, and the development of common standards. Some of the key measures undertaken by the government in this regard include: 

1. Data Protection Framework: The Digital Personal Data Protection (DPDP) Act of 2023 establishes a framework to ensure consent-based data sharing and regulate the processing of digital personal data. It delineates the responsibilities of data fiduciaries in safeguarding users' digital personal data.
2. Increasing Public-Private Partnerships: Refining collaboration between the government and the private sector has accelerated the development, maintenance, expansion, and trust of the infrastructure of DPIs, such as the AADHAR, UPI, and  Data Empowerment and Protection Architecture (DEPA). For example, the Asian Development Bank attributes the success of UPI to its “consortium ownership structure”, which enables the wide participation of major financial stakeholders in the country.
3. Coordinated Planning: The PM-Gati Shakti establishes a clear coordination framework involving various inter-governmental stakeholders at the state and union levels. This aims to significantly reduce project duplications, delays, and cost escalations by streamlining communication, harmonizing project appraisal and approval processes, and providing a comprehensive database of major infrastructure projects in the country. This database called the National Master Plan, is jointly accessible by various government stakeholders through APIs.
4. Capacity Building for Government Employees:  The National e-Governance Division of the Ministry of Electronics and Information Technology routinely rolls out multiple training programs to build the technological and managerial skills required by government employees to manage Digital Public Goods (DPGs). For instance, it recently held a program on “Managing Large Digital Transformative Projects”. Additionally, the Ministry of Personnel, Public Grievances, and Pensions has launched the Integrated Government Online Training platform  (iGOT) Karmayogi for the continuous learning of civil servants across various domains. 

Digital Governance; Way Forward

E-governance utilizes information and communication technology (ICT) such as Wide Area Networks, the Internet, and mobile computing to implement existing government activities, reconfiguring the structures and processes of governance systems. This warrants addressing certain inter-related challenges such as : 

1. Data Security: The dynamic and ever-changing landscape of cyber threats necessitates regular advancements in data and information security technologies, policy frameworks, and legal provisions. Consequently, the digital public ecosystem must incorporate robust data cybersecurity measures, advanced encryption technologies, and stringent privacy compliance standards to safeguard against data breaches.
2. Creating Feedback Loops: Regular feedback surveys will help government agencies improve the quality, efficiency, and accessibility of digital governance services by tailoring them to be more user-friendly and enhancing administrative design. This is necessary to build trust in government services and improve their uptake among beneficiaries. Conducting the decennial census is essential to gather updated data that can serve as a foundation for more informed and effective decision-making.
3. Capacity Building for End-Users: The beneficiaries of key e-governance projects like Aadhar and UPI may have inadequate technological skills, especially in regions with weak internet network infrastructure like hilly or rural areas. This can present challenges in the access to and usage of technological solutions.  Robust capacity-building campaigns for beneficiaries can provide an impetus to the digital inclusion efforts of the government.
4. Increasing the Availability of Real-Time Data: By prioritizing the availability of up-to-date information, governments and third-party enterprises can enable quick and informed decision-making.  They can effectively track service usage, assess quality, and monitor key metrics by leveraging real-time data. This approach is essential for enhancing operational efficiency and delivering improved user experience.
5. Resistance to Change: Any resistance among beneficiaries or government employees to adopt digital governance goods may stem from a limited understanding of digital processes and a lack of experience with transitioning from legacy systems.  Hand-holding employees during the transitionary phase can help create more trust in the process and strengthen the new systems. 

Conclusion

Digital governance is crucial to transforming public services, ensuring transparency, and fostering inclusivity in a rapidly digitizing world. The successful implementation of such projects requires addressing challenges like data security, skill gaps, infrastructural limitations, feedback mechanisms, and resistance to change. Addressing these challenges with a strategic, multi-stakeholder approach can ensure the successful execution and long-term impact of large digital governance projects. By adopting robust cybersecurity frameworks, fostering public-private partnerships, and emphasizing capacity building, governments can create efficient and resilient systems that are user-centric, secure, and accessible to all. 

References

• https://www.adb.org/sites/default/files/publication/865106/adbi-wp1363.pdf 
• https://www.jotform.com/blog/government-digital-transformation-challenges/ 
• https://aapti.in/wp-content/uploads/2024/06/AaptixONI-DPIGovernancePlaybook_compressed.pdf 
• https://community.nasscom.in/sites/default/files/publicreport/Digital%20Public%20Infrastructure%2022-2-2024_compressed.pdf 
• https://proteantech.in/articles/Decoding-Digital-Public-Infrastructure-in-India/ 

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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/

Executive Summary:

A viral image circulating on social media claims it to be a natural optical illusion from Epirus, Greece. However, upon fact-checking, it was found that the image is an AI-generated artwork created by Iranian artist Hamidreza Edalatnia using the Stable Diffusion AI tool. CyberPeace Research Team found it through reverse image search and analysis with an AI content detection tool named HIVE Detection, which indicated a 100% likelihood of AI generation. The claim of the image being a natural phenomenon from Epirus, Greece, is false, as no evidence of such optical illusions in the region was found.

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Claims:

The viral image circulating on social media depicts a natural optical illusion from Epirus, Greece. Users share on X (formerly known as Twitter), YouTube Video, and Facebook. It’s spreading very fast across Social Media.

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Fact Check:

Upon receiving the Posts, the CyberPeace Research Team first checked for any Synthetic Media detection, and the Hive AI Detection tool found it to be 100% AI generated, which is proof that the Image is AI Generated. Then, we checked for the source of the image and did a reverse image search for it. We landed on similar Posts from where an Instagram account is linked, and the account of similar visuals was made by the creator named hamidreza.edalatnia. The account we landed posted a photo of similar types of visuals.

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We searched for the viral image in his account, and it was confirmed that the viral image was created by this person.

The Photo was posted on 10th December, 2023 and he mentioned using AI Stable Diffusion the image was generated . Hence, the Claim made in the Viral image of the optical illusion from Epirus, Greece is Misleading.

Conclusion:

The image claiming to show a natural optical illusion in Epirus, Greece, is not genuine, and it's False. It is an artificial artwork created by Hamidreza Edalatnia, an artist from Iran, using the artificial intelligence tool Stable Diffusion. Hence the claim is false.