#FactCheck -Viral Claim of Alcohol Ban in West Bengal by Amit Shah Is Fake, No Such Announcement Made

Introduction

Words come easily, but not necessarily the consequences that follow. Imagine a 15-year-old child on the internet hoping that the world will be nice to him and help him gain confidence, but instead, someone chooses to be mean on the internet, or the child becomes the victim of a new kind of cyberbullying, i.e., online trolling. The consequences of trolling can have serious repercussions, including eating disorders, substance abuse, conduct issues, body dysmorphia, negative self-esteem, and, in tragic cases, self-harm and suicide attempts in vulnerable individuals. The effects of online trolling can include anxiety, depression, and social isolation. This is one example, and hate speech and online abuse can touch anyone, regardless of age, background, or status. The damage may take different forms, but its impact is far-reaching. In today’s digital age, hate speech spreads rapidly through online platforms, often amplified by AI algorithms. 

As we celebrate today, i.e., 18th June, the International Day for Countering Hate Speech, if we have ever been mean to someone on the internet, we pledge never to repeat that kind of behaviour, and if we have been the victim, we will stand against the perpetrator and report it. 

This year, the theme for the International Day for Countering Hate Speech is “Hate Speech and Artificial Intelligence Nexus: Building coalitions to reclaim inclusive and secure environments free of hatred. UN Secretary-General Antonio Guterres, in his statement, said, “Today, as this year’s theme reminds us, hate speech travels faster and farther than ever, amplified by Artificial Intelligence. Biased algorithms and digital platforms are spreading toxic content and creating new spaces for harassment and abuse."

Coded Convictions: How AI Reflects and Reinforces Ideologies

Algorithms have swiftly taken the place of feelings; they tamper with your taste, and they do so with a lighter foot, invisibly. They are becoming an important component of social media user interaction and content distribution. While these tools are designed to improve user experience, they frequently inadvertently spread divisive ideologies and push extremist propaganda. This amplification can strengthen the power of extremist organisations, spread misinformation, and deepen societal tensions. This phenomenon, known as “algorithmic radicalisation,” demonstrates how social media companies may utilise a discriminating content selection approach to entice people down ideological rabbit holes and shape their ideas. AI-driven algorithms often prioritise engagement over ethics, enabling divisive and toxic content to trend and placing vulnerable groups, especially youth and minorities, at risk. The UN’s Strategy and Plan of Action on Hate Speech, launched on June 18, 2019, recognises that while AI holds promise for early detection and prevention of harmful speech, it also demands stringent human rights safeguards. Without regulation, these tools can themselves become purveyors of bias and exclusion. 

India’s Constitutional Resolve and Civilizational Ethos against Hate

India has always taken pride in being inclusive and united rather than divided. As far as hate speech is concerned, India's stand is no different. The United Nations, India believes in the same values as its international counterpart. Although India has won many battles against hate speech, the war is not over and is now more prominent than ever due to the advancement in communication technologies. In India, while the right to freedom of speech and expression is protected under Article 19(1)(a), its exercise is limited subject to reasonable restrictions under Article 19(2). Landmark rulings such as Ramji Lal Modi v. State of U.P. and Amish Devgan v. UOI have clarified that speech can be curbed if it incites violence or undermines public order. Section 69A of the IT Act, 2000, empowers the government to block content, and these principles are also reflected in Section 196 of the BNS, 2023 (153A IPC) and Section 299 of the BNS, 2023 (295A IPC). Platforms are also required to track down the creators of harmful content and remove it within a reasonable hour and fulfil their due diligence requirements under IT rules.

While there is no denying that India needs to be well-equipped and prepared normatively to tackle hate propaganda and divisive forces. India’s rich culture and history, rooted in philosophies of Vasudhaiva Kutumbakam (the world is one family) and pluralistic traditions, have long stood as a beacon of tolerance and coexistence. By revisiting these civilizational values, we can resist divisive forces and renew our collective journey toward harmony and peaceful living.

CyberPeace Message

The ultimate goal is to create internet and social media platforms that are better, safer and more harmonious for each individual, irrespective of his/her/their social and cultural background. CyberPeace stands resolute on promoting digital media literacy, cyber resilience, and consistently pushing for greater accountability for social media platforms. 

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References

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• https://www.un.org/en/observances/countering-hate-speech 
• https://www.artemishospitals.com/blog/the-impact-of-trolling-on-teen-mental-health 
• https://www.orfonline.org/expert-speak/from-clicks-to-chaos-how-social-media-algorithms-amplify-extremism ‍
• https://www.techpolicy.press/indias-courts-must-hold-social-media-platforms-accountable-for-hate-speech/

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/

The World Wide Web was created as a portal for communication, to connect people from far away, and while it started with electronic mail, mail moved to instant messaging, which let people have conversations and interact with each other from afar in real-time. But now, the new paradigm is the Internet of Things and how machines can communicate with one another. Now one can use a wearable gadget that can unlock the front door upon arrival at home and can message the air conditioner so that it switches on. This is IoT.

WHAT EXACTLY IS IoT?

The term ‘Internet of Things’ was coined in 1999 by Kevin Ashton, a computer scientist who put Radio Frequency Identification (RFID) chips on products in order to track them in the supply chain, while he worked at Proctor & Gamble (P&G). And after the launch of the iPhone in 2007, there were already more connected devices than people on the planet.

Fast forward to today and we live in a more connected world than ever. So much so that even our handheld devices and household appliances can now connect and communicate through a vast network that has been built so that data can be transferred and received between devices. There are currently more IoT devices than users in the world and according to the WEF’s report on State of the Connected World, by 2025 there will be more than 40 billion such devices that will record data so it can be analyzed.

IoT finds use in many parts of our lives. It has helped businesses streamline their operations, reduce costs, and improve productivity. IoT also helped during the Covid-19 pandemic, with devices that could help with contact tracing and wearables that could be used for health monitoring. All of these devices are able to gather, store and share data so that it can be analyzed. The information is gathered according to rules set by the people who build these systems.

APPLICATION OF IoT

IoT is used by both consumers and the industry.

Some of the widely used examples of CIoT (Consumer IoT) are wearables like health and fitness trackers, smart rings with near-field communication (NFC), and smartwatches. Smartwatches gather a lot of personal data. Smart clothing, with sensors on it, can monitor the wearer’s vital signs. There are even smart jewelry, which can monitor sleeping patterns and also stress levels.

With the advent of virtual and augmented reality, the gaming industry can now make the experience even more immersive and engrossing. Smart glasses and headsets are used, along with armbands fitted with sensors that can detect the movement of arms and replicate the movement in the game.

At home, there are smart TVs, security cameras, smart bulbs, home control devices, and other IoT-enabled ‘smart’ appliances like coffee makers, that can be turned on through an app, or at a particular time in the morning so that it acts as an alarm. There are also voice-command assistants like Alexa and Siri, and these work with software written by manufacturers that can understand simple instructions.

Industrial IoT (IIoT) mainly uses connected machines for the purposes of synchronization, efficiency, and cost-cutting. For example, smart factories gather and analyze data as the work is being done. Sensors are also used in agriculture to check soil moisture levels, and these then automatically run the irrigation system without the need for human intervention.

Statistics

• The IoT device market is poised to reach $1.4 trillion by 2027, according to Fortune Business Insight.
• The number of cellular IoT connections is expected to reach 3.5 billion by 2023. (Forbes)
• The amount of data generated by IoT devices is expected to reach 73.1 ZB (zettabytes) by 2025.
• 94% of retailers agree that the benefits of implementing IoT outweigh the risk.
• 55% of companies believe that 3^rd party IoT providers should have to comply with IoT security and privacy regulations.
• 53% of all users acknowledge that wearable devices will be vulnerable to data breaches, viruses,
• Companies could invest up to 15 trillion dollars in IoT by 2025 (Gigabit)

CONCERNS AND SOLUTIONS

• Two of the biggest concerns with IoT devices are the privacy of users and the devices being secure in order to prevent attacks by bad actors. This makes knowledge of how these things work absolutely imperative.
• It is worth noting that these devices all work with a central hub, like a smartphone. This means that it pairs with the smartphone through an app and acts as a gateway, which could compromise the smartphone as well if a hacker were to target that IoT device.
• With technology like smart television sets that have cameras and microphones, the major concern is that hackers could hack and take over the functioning of the television as these are not adequately secured by the manufacturer.
• A hacker could control the camera and cyberstalk the victim, and therefore it is very important to become familiar with the features of a device and ensure that it is well protected from any unauthorized usage. Even simple things, like keeping the camera covered when it is not being used.
• There is also the concern that since IoT devices gather and share data without human intervention, they could be transmitting data that the user does not want to share. This is true of health trackers. Users who wear heart and blood pressure monitors have their data sent to the insurance company, who may then decide to raise the premium on their life insurance based on the data they get.
• IoT devices often keep functioning as normal even if they have been compromised. Most devices do not log an attack or alert the user, and changes like higher power or bandwidth usage go unnoticed after the attack. It is therefore very important to make sure the device is properly protected.
• It is also important to keep the software of the device updated as vulnerabilities are found in the code and fixes are provided by the manufacturer. Some IoT devices, however, lack the capability to be patched and are therefore permanently ‘at risk’.

CONCLUSION

Humanity inhabits this world that is made up of all these nodes that talk to each other and get things done. Users can harmonize their devices so that everything runs like a tandem bike – completely in sync with all other parts. But while we make use of all the benefits, it is also very important that one understands what they are using, how it is functioning, and how one can tackle issues should they come up. This is also important to understand because once people get used to IoT, it will be that much more difficult to give up the comfort and ease that these systems provide, and therefore it would make more sense to be prepared for any eventuality. A lot of times, good and sensible usage alone can keep devices safe and services intact. But users should be aware of any issues because forewarned is forearmed.