#FactCheck-RBI's Alleged Guidelines on Ink Colour for Cheque Writing

Executive Summary:

A viral video has circulated on social media, wrongly showing lawbreakers surrendering to the Indian Army. However, the verification performed shows that the video is of a group surrendering to the Bangladesh Army and is not related to India. The claim that it is related to the Indian Army is false and misleading. 

Claims:

A viral video falsely claims that a group of lawbreakers is surrendering to the Indian Army, linking the footage to recent events in India.

Fact Check:

Upon receiving the viral posts, we analysed the keyframes of the video through Google Lens search. The search directed us to credible news sources in Bangladesh, which confirmed that the video was filmed during a surrender event involving criminals in Bangladesh, not India.

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We further verified the video by cross-referencing it with official military and news reports from India. None of the sources supported the claim that the video involved the Indian Army.  Instead, the video was linked to another similar Bangladesh Media covering the news.

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No evidence was found in any credible Indian news media outlets that covered the video. The viral video was clearly taken out of context and misrepresented to mislead viewers.

Conclusion:

The viral video claiming to show lawbreakers surrendering to the Indian Army is footage from Bangladesh. The CyberPeace Research Team confirms that the video is falsely attributed to India, misleading the claim.

• Claim: The video shows miscreants surrendering to the Indian Army.  
• Claimed on: Facebook, X, YouTube
• Fact Check: False & Misleading

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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 

In the digital era, where technology is growing rapidly,  the role of Artificial Intelligence (AI) has been making its way to different corners of the world. Where nothing seems to be impossible, technology and innovation have been moving conjointly and once again, and such innovation is in the limelight with its groundbreaking initiative known as “Project Groot”, which has been announced by the AI chip leader “Nvidia”. The core of this project is the fusion of technology with AI and robotics, where a humanoid can be produced with the capability to understand the natural language and interact with it to further learn from the physical environment by observing human actions and skills. Project Groot aims to assist humans in diverse sectors such as Healthcare and so on. 

Humanoid robots are based on NVIDIA’s thor system-on-chip (SoC). The thor powers the intelligence of these robots, and the chip has been designed to handle complex tasks and ensure a safe and natural interaction between humans and robots. However, a big question arises about the ethical considerations of privacy, autonomy and the possible replacement of human workers. 

Brief Analysis 

Nvidia has announced Project GR00T, or Generalist Robot 00 Technology, which aims to create AI-powered humanoid robots with human-like understanding and movement. The project is part of Nvidia's efforts to drive breakthroughs in robotics and embodied AI, which can interact with and learn from a physical environment. The robots built on this platform are designed to understand natural language and emulate movements by observing human actions, such as coordination, dexterity, and other skills.

The model has been trained on NVIDIA GPU-accelerated simulation, enabling the robots to learn from human demonstrations with imitation learning and from the robotics platform NVIDIA Isaac Lab for reinforcement learning. This multimodal AI system acts as the mind for humanoid robots, allowing them to learn new skills and interact with the real world. Leading names in robotics, such as Figure, Boston Dynamics, Apptronik, Agility Robotics, Sanctuary AI, and Unitree, are reported to have collaborated with Nvidia to leverage GR00T.

Nvidia has also updated Isaac with Isaac Manipulator and Isaac Perceptor, which add multi-camera 3D vision. The company also unveiled a new computer, Jetson Thor, to aid humanoid robots based on NVIDIA's SoC, which is designed to handle complex tasks and ensure a safe and natural interaction between humans and robots.

Despite the potential job loss associated with humanoid robots potentially handling hazardous and repetitive tasks, many argue that they can aid humans and make their lives more comfortable rather than replacing them.

Policy Recommendations 

The Nvidia project highlights a significant development in AI Robotics, presenting a brimming potential and ethical challenges critical for the overall development and smooth assimilation of AI-driven tech in society. To ensure its smooth assimilation, a comprehensive policy framework must be put in place. This includes:

• Human First Policy - Emphasis should be on better augmentation rather than replacement. The authorities must focus on better research and development (R&D) of applications that aid in modifying human capabilities, enhancing working conditions, and playing a role in societal growth. 
• Proper Ethical Guidelines - Guidelines stressing human safety, autonomy and privacy should be established. These norms must include consent for data collection, fair use of AI in decision making and proper protocols for data security. 
• Deployment of Inclusive Technology - Access to AI Driven Robotics tech should be made available to diverse sectors of society. It is imperative to address potential algorithm bias and design flaws to avoid discrimination and promote inclusivity.
• Proper Regulatory Frameworks - It is crucial to establish regulatory frameworks to govern the smooth deployment and operation of AI-driven tech. The framework must include certification for safety and standards, frequent audits and liability protocols to address accidents. 
• Training Initiatives - Educational programs should be introduced to train the workforce for integrating AI driven robotics and their proper handling. Upskilling of the workforce should be the top priority of corporations to ensure effective integration of AI Robotics. 
• Collaborative Research Initiatives - AI and emerging technologies have a profound impact on the trajectory of human development. It is imperative to foster collaboration among governments, industry and academia to drive innovation in AI robotics responsibly and undertake collaborative initiatives to mitigate and address technical, societal, legal and ethical issues posed by AI Robots. 

Conclusion 

On the whole, Project GROOT is a significant quantum leap in the advancement of robotic technology and indeed paves the way for a future where robots can integrate seamlessly into various aspects of human lives. 

References

• https://indianexpress.com/article/explained/explained-sci-tech/what-is-nvidias-project-gr00t-impact-robotics-9225089/
• https://medium.com/paper-explanation/understanding-nvidias-project-groot-762d4246b76d
• https://www.techradar.com/pro/nvidias-project-groot-brings-the-human-robot-future-a-significant-step-closer
• https://www.barrons.com/livecoverage/nvidia-gtc-ai-conference/card/nvidia-announces-ai-model-for-humanoid-robot-development-BwT9fewMyD6XbuBrEDSp

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