Artificial Intelligence

IndiaAI Mission gets Rs 2,000 Crore: Budget 2025-26

Context: The Union Budget 2025-26 has sanctioned Rs 2,000 crore for the IndiaAI Mission for 2025-26, which is nearly a fifth of the scheme’s total outlay of Rs 10,370 crore.

Relevance of the topic:

Prelims: IndiaAI Mission

Mains: Status of AI in India, challenges and suggestive measures

Major Highlights:

The government has shortlisted 10 companies that will provide nearly 19,000 graphics processing units (GPUs)- high end chips needed to develop machine learning tools - for setting up artificial intelligence (AI) data centres.
- The initial aim of the IndiaAI Mission was to procure 10,000 GPUs.
The government also aims to build a domestic large language model (LLM) of its own, as part of the IndiaAI Mission.
The government will set up a new centre of excellence for AI for education with an outlay of Rs 500 crore.

About IndiaAI Mission

IndiaAI Mission is an initiative of the Ministry of Electronics and Information Technology (MeitY). Total outlay: Rs 10,370 crores.
It aims to build a comprehensive AI ecosystem that fosters innovation by democratising computing access, enhancing data quality and developing indigenous AI capabilities.
The mission aims to develop:
- IndiaAI Compute Capacity: establish a computing capacity of more than 10,000 GPUs, via public-private partnerships, offering AI services and resources.
- IndiaAI Innovation Centre: develop and deploy indigenous Large Multimodal Models and domain-specific foundational models, with a capacity of >100 billion parameters, for priority sectors like healthcare, agriculture, and governance.
- IndiaAI Datasets Platform: streamline the access to high-quality non-personal datasets for AI innovation.
- Responsible AI development.
A major portion of the total scheme outlay has been earmarked for building computing infrastructure.
The idea is that if such an infrastructure exists in the country, start-ups could plug into it for developing AI systems.

Challenges in AI development in India

Talent shortage: Indian professionals lack skills requisite for the AI development in India. As 20% of companies reported that 50 to 100 AI projects are stalled at the planning stage due to shortage of skilled talent pool.
Data privacy and security concerns: AI development in India poses challenges to privacy, as India lacks the stringent and comprehensive implementation of data privacy rules.
Intellectual property violation: AI models threaten copyrighted work and sanctity of intellectual property rights.
Infrastructure deficit: Despite initiatives like AIRAWAT, India's AI-first compute infrastructure, the country still faces challenges in providing adequate computational resources necessary for advanced AI research and applications
Data deficit: There is a deficit of digitised data in India leading to limited creating a barrier in the development of AI based decision making. Eg; 26% of AI decision makers cited insufficient access to trusted data.

Suggestive Measures for AI Development:

Government initiatives:
- National AI Mission: The Indian government has launched the IndiaAI Mission with a budget of Rs. 10,307 Cr. to foster AI development across various sectors, including healthcare, agriculture, and education
- National Strategy for Artificial Intelligence: NITI aayog has released this strategy to focus on leveraging AI for inclusive growth and positions India as a global leader in AI.
Digital Public Infrastructure (DPI): Collaborations between the government and private sector have led to the development of DPI, facilitating scalable AI solutions and fostering innovation
Enhancing computing capacity: Government should provide subsidised GPU to AI based startups in challenging circumstances when the US has changed India’s position to ‘watchful’ in US AI rules.
Strengthening data governance: Implementing comprehensive data protection rules and digitisation of data can balance privacy and efficacy of data required for AI development.
Fostering collaboration: Efforts to encourage partnership between academia, industry and government. Eg; Apprenticeship of AI students with Industry and government.

About Large Language Models (LLM)

LLMs are a subset of AI models designed to understand and generate human-like text by learning patterns from vast datasets. Examples: Open AI, chatGPT, Gemini.
Other Notable AI Models:
- Convolutional Neural Networks (CNNs): Primarily used in image recognition tasks, such as facial recognition and medical image analysis. Eg; DeepMInd’s AlphaFold
- Recurrent Neural Networks (RNNs): Suited for sequential data processing, like time-series analysis and language modeling. Eg; Google Translate
- Generative Adversarial Networks (GANs): Generate new data samples similar to the training data, used in image and video generation. Eg; DALL-E

IndiaAI Mission seeks to position India at the forefront of the global AI landscape, leveraging technology to address societal challenges and enhance the nation's economic development.

What is DeepSeek AI?

Context: The Chinese start-up DeepSeek, has created a buzz with the launch of its cutting-edge AI models ‘DeepSeek-R1’ & ‘DeepSeek-V3’, claiming they nearly match the capabilities of top AI models in the U.S., while being far more affordable.

Relevance of the Topic: Prelims: Basic understanding of terms like Large Language Models, DeepSeek AI.

What is DeepSeek?

DeepSeek is a Hangzhou-based Chinese startup that has recently launched artificial intelligence (AI) chatbot built on a low-cost Large Language Model (LLM) infrastructure.
The AI is optimised for tasks like maths and coding, making it a strong competitor in the AI space.

DeepSeek vs global LLMs:

Low training cost: DeepSeek reportedly trained its model for just $6 million, significantly lower than the estimated $100 million expenditure behind OpenAI's GPT-4.
- DeepSeek was able to dramatically reduce the cost of building its AI models by using NVIDIA’s H800 chips, an older generation of GPUs in the US.
High efficiency & Low cost: The model is being praised for its efficiency, as it uses advanced and lower-grade chips to deliver high performance at a lower cost.
- E.g., Reportedly, DeepSeek-R1 is 20 to 50 times cheaper to use than OpenAI o1 model (depending on the task).
- DeepSeek’s R1 may not be quite as advanced as OpenAI’s o3, it is almost on par with OpenAI o1 on several metrics.
Innovative & Adaptable:
- DeepSeek-R1 uses reinforcement learning to naturally (autonomously) evolve its reasoning capabilities. The model self-improves through feedback loops during training, without needing massive labeled datasets.
- DeepSeek-R1 can transfer (distill) reasoning capabilities into smaller models (SLMs), which are faster and more resource-efficient. Thus, DeepSeek-R1’s reasoning capabilities are scalable across different model sizes, making it highly adaptable.
Affordable for users: DeepSeek's paid subscription comes at $0.50 a month, while ChatGPT costs $20.

Concerns:

Censorship on digital content & bias:
- Unlike many Western models, DeepSeek follows China's strict censorship rules. When asked about sensitive topics, it avoids direct answers, reflecting government control over digital content.
- Furthermore, the chatbot is expected to have a pro-China bias.
Potential security risks: Experts have warned about potential security risks associated with the DeepSeek AI app, pointing out the need for scrutiny in data privacy and AI ethics.

Global Impact:

Sputnik moment: Much like the Sputnik in the 1950s, DeepSeek brings a new technological frontier into the great power competition.
Market disruption: The launch of the DeepSeek AI resulted in a historic $600 billion market value drop for Nvidia, a key player in AI chip production.
Policy implications: DeepSeek heralds an escalation of the geopolitical rivalry between the US and China. It risks escalation of the US government's restrictions on advanced chip exports to China.

Read more: What are Small Language Models?

What are Small Language Models?

Context: Small Language Models (SLMs) are a perfect artificial intelligence system for a country like India, where the scope of Artificial Intelligence (AI) adoption is immense but resources are constrained.

Relevance of the Topic:Prelims: Basic understanding of terms like Large Language Models, Small Language Models.

What is a Language Model?

A language model is the core component of modern Natural Language Processing (NLP). It is a statistical model that is designed to analyse the pattern of human language and predict the likelihood of a sequence of words or tokens.
Large language models (LLMs) are AI systems capable of understanding and generating human language by processing vast amounts of text data (has at least one billion or more parameters). E.g., ChatGPT (by Open AI), Gemini (Google), Llama (Meta).

What is a Small Language Model (SLM)?

Small Language Models (SLMs) are compact AI systems designed for natural language processing tasks.
SLMs typically have fewer than 1 billion parameters (ranges from millions to a few billion parameters), making them more efficient in terms of computational resources and energy consumption.
SLMs are capable of performing various NLP tasks such as text generation, translation, and sentiment analysis, with potentially reduced capabilities compared to larger models.

Benefits of Small Language Model:

Ideal for specialised tasks: SLMs are cheaper to run and maintain and ideal for specific use cases. For a company that needs AI for a set of specialised tasks, a large AI model is not required.
Lesser training time: Training small models requires less time, less computation and smaller training data.
High inference speeds: SLMs have faster inference speeds (reduced latency due to fewer parameters) because of their smaller size. This is beneficial for real-time applications where quick responses are crucial. E.g., chatbots or voice assistants.
Use fewer resources: Their smaller size allows for deployment on edge devices, can run offline on smaller devices like mobile phones or embedded systems, making them valuable for applications where resources are limited or privacy is a concern.
- In India, where the scope of AI adoption is immense but resources are constrained, SLMs are perfect.

Examples of Small Language Model

Microsoft Phi (the latest Phi-3-mini has 3.8 billion parameters).
LLaMA 3 (by Meta)
Gemma (by Google)

Limitations of Small Language Model

Less capable of handling complex tasks: Smaller size of SLMs limits their ability to capture and process large amounts of contextual and nuanced information, hence, making them unsuitable for highly intricate tasks, like detailed data analysis or advanced creative writing.
Less accuracy and creativity: Their reduced scale (limited data training) restricts the richness of their outputs, leading to less imaginative or less varied responses, compared to LLMs.
Bias and reduced Performance: Since SLMs operate on fewer parameters and smaller datasets, they are more prone to bias.

Use of Artificial Intelligence in Defence

Relevance of the Topic: Mains: Detailed question on scope, and challenges regarding AI use in the Defence sector.

AI and Defence Integration in India

India is at the nascent stage of integration of AI with military technology. One such example is the Indrajal drone defence system.
Institutional framework: India has launched an institutional framework for inducting AI with the military in 2022.
- Defence Artificial Intelligence council chaired by the Defence Minister of India to to provide necessary guidance and structural support.
- Defence AI Project Agency (DAIPA) has been created under the Chairmanship of Secretary Department of Defence Production (DDP) for enabling AI based processes in defence Organisations.
Listing priorities: In 2022, the government published a list of 75 priority projects related to using AI for defence; these focused on data processing and analysis, cyber security, simulation and autonomous systems, particularly drones.
AI embedded centers in armed forces: AI-application centres embedded in each of the three armed-service branches – at the Military College of Telecommunication Engineering, Mhow (Army), the INS Valsura (Navy) and Air Force Station Rajokri (Air Force).

Scope of AI in Defence sector

Autonomous systems:
- Defence: AI driven autonomous systems can aid armed forces in difficult operations and prevent casualties. E.g., Indrajaal system on drone defence is based on an autonomous AI system; AI-enabled Robot Sentries.
- Offence: Can be used to conduct precision strikes and support armed forces in offence. E.g., AI-powered killer robots and Armed UAVs; AI-embedded guided missiles (determines target’s range and adjust flight patterns without human intervention).
- Surveillance: AI-embedded radars, satellites, software-identification systems can aid in geospatial analysis, detection of illegal or suspicious activities and alerting authorities. E.g., Indian Army uses facial recognition system ‘Project Seeker’ for monitoring, surveillance, and garrison security.
Cyber-security: AI can be utilised to timely detect and launch a counter attack on cyber attack.
Data Analysis: Defence data is complex like intelligence data, enemy movement, previous trends and strategy analysis can be done effectively with use of AI.
Predictive maintenance: AI can be utilized for predicting the maintenance needs to prevent failure during crucial operations.
Simulations and training: AI can generate multiple and complex hypothetical situations to train soldiers and operatives for unpredictable threats.

Risks and Challenges Associated:

Security risks: AI based defence technologies are susceptible to hacking and cyber attacks leading to catastrophic results.
Ethical questions: AI based decision making in defence can cause collateral damage creating questions of accountability in case of unintended harm.
Interoperability issue: Integrating AI across diverse military platforms (aircraft, naval ships, ground vehicles) is complex. Existing systems might not be compatible, limiting its effectiveness.
Bias and poor data quality: AI systems rely on data for training, and if the data is biased or incomplete, AI models can produce flawed decisions. E.g., biased training data could lead AI inappropriately identifying enemy targets based on incomplete intelligence.
Legal issue: AI based operations lack the global consensus, as AI is not subjected to the Geneva convention, unlike traditional wars and conflicts.

Conclusion: AI in the military offers significant potential for enhancing capabilities, including autonomous systems and cybersecurity. However, it also faces challenges such as security risks, ethical concerns, and technical limitations. A balanced approach involving robust research, international cooperation, and ethical considerations will ensure that AI serves as a strategic asset for defense.

Microsoft to invest $3 billion on AI, cloud infrastructure in India

Context: Microsoft CEO has announced plans to invest $3 billion in India in AI and cloud infrastructure, including setting up new data centres over the next two years. The company is also aiming to train 10 million Indian people with AI skills by 2030.

Relevance of the Topic: Mains: Key facts about AI, Cloud infrastructure in India, Government Initiatives.

Major Highlights:

Efficiency metrics for AI applications: The formula to measure the efficiency of AI applications is stipulated to be: Tokens per dollar per watt.
- Tokens per dollar per watt signify how many tokens (units of information) can be generated per dollar spent on computing power, per unit (watt) consumption of energy, required to produce those tokens.

Key Terms:

What is Artificial Intelligence?

Artificial intelligence (AI) is broadly defined as the capability of a machine (computer systems) to imitate intelligent human behaviour. E.g., Machines can perform cognitive tasks like thinking, perceiving, learning, problem-solving and decision-making.

What is Cloud Computing?

Cloud computing involves the rental of computing resources—such as servers, storage, applications, and databases— over the internet, as opposed to owning physical infrastructure. At its core, cloud computing relies on the infrastructure provided by data centers.
E.g., Software as a Service (SaaS): SaaS grants users access to software applications hosted by the cloud service provider.

What are Data Centres?

Data centers are highly specialised facilities designed to house computing systems and their related components, such as, physical hardware, servers, networking equipment and storage systems.
The primary purpose of data centers is to process, store, and distribute data for various applications and services, such as websites, cloud computing, and enterprise operations.
Data centers empower organisations to handle large volumes of data securely and efficiently and enable cloud computing to function seamlessly.

Potential of Data Centres in India

India aims to become a global hub for AI innovation and data centre development.
Current capacity:
- As of 2024, India's data centre capacity is at approximately 1,255 MW, which is expected to surge to 17 GW by 2030.
- India holds 20% of global data but only 3% of data centre capacity.
Expansion potential of data centres in future, due to:
- Increasing digitalisation & data consumption
- Rise in demand for AI and generative AI projects
- Nationwide roll-out of 5G
- Need for edge computing to allow data processing on devices
- Need to store data locally (within National borders).
Concentration of data centres in India: About 95% of the existing data centre capacity is in metros and big cities. Mumbai has >50% of current capacity due to its central location, reliable power and cable landing stations.

Government Initiatives in this Regard

1. Data Localisation Rules: India’s laws mandate that certain data be stored locally, such as:

Reserve Bank of India's Directive (2018) mandates payment system providers to store entire payment data (transaction details, customer information and related data) within India.
IRDAI (Maintenance of Insurance Records) Regulation, 2015 requires covered organisations to store insurance data within India.
The draft Digital Personal Data Protection Rules focus on targeted data localisation, addressing children's online age verification challenges, and data protection.
- Digital Personal Data Protection Act permits cross-border data transfers to all countries, unless restricted by the Central Government by notification.

2. Digital India Mission:

Digital India campaign launched in 2015, aims at the development of secure and stable digital infrastructure (including data centres), delivering government services digitally, and universal digital literacy.

3. IndiaAI Mission:

The Rs 10,370 croreIndiaAI Mission aims to:
- establish a computing capacity of more than 10,000 GPUs.
- help develop foundational models with a capacity of more than 100 billion parameters trained on datasets covering major Indian languages for priority sectors like healthcare, agriculture, and governance.
The idea is that if such an infrastructure exists in the country, start-ups could plug into it for developing AI systems.
Of the total outlay, Rs 4,564 crore has been earmarked for building computing infrastructure.

Read More: IndiaAI Mission

AI-Powered Surveillance in India

Context: India is at the forefront of adopting AI in surveillance. Technological integration is a welcome move to modernise law enforcement, however, in the absence of suitable legal frameworks it might intersect with constitutional rights of citizens, particularly the right to privacy.

Relevance of the Topic: Mains: AI in surveillance- Challenges, Opportunities, Way Forward

Use of AI in Governance

In 2019, India announced its ambition to build the world’s largest facial recognition system for policing.
- Artificial Intelligence (AI)-powered surveillance systems have been deployed across railway stations and the Delhi Police is preparing to use AI for crime patrols.
India plans to launch 50 AI-powered satellites to further intensify India’s surveillance infrastructure.

Challenges:

While technological advancements in law enforcement offer potential, they raise significant legal, constitutional, and ethical concerns.

Privacy Concerns:
- Right to Privacy: Recognised as a fundamental right under Article 21 in K.S. Puttaswamy vs Union of India (2017).
- AI-enabled “dragnet surveillance” often involves indiscriminate data collection, infringing on citizens’ informational privacy.
- Lack of proportional safeguards against AI misuse raises risks of mass surveillance and data breaches.
Gaps in Legal Frameworks:
- Digital Personal Data Protection Act (DPDPA), 2023:
  - Grants the government unchecked power to process personal data without the need for consent, when processing data for medical treatment during an epidemic, data related to employment etc.
  - Mandates citizens not to suppress any material information when submitting personal data. This provision (while intended to ensure data accuracy) could lead to punitive measures for something as simple as an outdated address or technical error in data collection systems.
  - Criticised for skewing power towards state surveillance over individual rights.
- Absence of specific legislation for AI regulation, despite growing deployment of AI-powered systems.
Unregulated AI Usage:
- India’s AI surveillance lacks clear guidelines on data collection, processing, storage, and usage and mechanisms to prevent abuse or discrimination.
- Example: Deployment of facial recognition technologies in Delhi and Hyderabad without public risk assessments or legislative debate.
Risk of Overreach:
- International experiences, such as the U.S. Foreign Intelligence Surveillance Act (FISA), shows that surveillance laws can lead to overreach.
- Expanding India’s AI surveillance infrastructure without sufficient safeguards risks violating constitutional principles of proportionality and legality.

Indian Context:

India’s surveillance capabilities are growing rapidly with plans for 50 AI-powered satellites and integration of AI in public systems.
Cases like Telangana Police data breach highlight the misuse of personal data collected through welfare schemes (E.g., “Samagra Vedika”).
Current frameworks fail to ensure transparency, judicial oversight, or accountability in data collection and AI deployment.

International Context:

European Union: EU’s Artificial Intelligence Act follows a risk-based approach:
- Categorises AI applications as unacceptable, high, transparency, or minimal risk.
- Prohibits real-time biometric identification for law enforcement, except under strict conditions.
United States: Surveillance laws like FISA offer lessons on the potential for overreach and the need for stringent safeguards.

Impact on Civil Liberties:

AI surveillance, without sufficient safeguards, might risk:

Privacy violations: Indiscriminate data collection threatens informational privacy.
Discrimination: Biased AI systems can exacerbate social inequalities.
Data breaches: Weak safeguards increase vulnerability to cyberattacks.
Loss of trust: Citizens may lose confidence in public institutions.

Way Forward:

Regulatory Frameworks:
- Enact comprehensive legislation for AI governance.
- Categorize AI applications based on risk levels, with specific restrictions on high-risk activities.
Transparent Data Practices:
- Mandate public disclosure of: What data is collected, for what purpose, how it is stored, timelines for data retention and deletion.
- Narrow exemptions for consent-based data collection, with judicial oversight.
Independent Oversight:
- Create independent bodies to oversee AI deployment in public systems.
- Establish mechanisms for judicial review of surveillance activities.
International Best Practices:
- Adopt risk-based regulatory approaches like that of the EU.
Citizen Awareness:
- Educate citizens on their privacy rights under the Constitution and relevant laws.
- Build grievance redressal mechanisms for AI-related violations.
Strengthen the DPDPA:
- Narrow government exceptions.
- Ensure accountability in state surveillance.

Conclusion: India stands at a critical juncture in deploying AI-powered surveillance. While technological advancements promise enhanced governance and law enforcement, they must be balanced against constitutional rights. A proactive regulatory approach that is aligned with international best practices can ensure that AI serves the public interest without compromising civil liberties.

What is AI Safety Institute?

Context: The Ministry of Electronics and Information Technology (MeitY) is exploring the idea to establish an AI Safety Institute (AISI) in India under the IndiaAI Mission.

Relevance of the Topic: Prelims: Key facts about AI Safety Institute (once it is established); Global Partnership on AI (GPAI) initiative; Bletchley Declaration; IndiaAI Mission.

Major Highlights:

In recent years, India has displayed leadership in developing a robust Artificial Intelligence ecosystem and AI governance at the G20 and the Global Partnership on AI (GPAI) initiative.
India needs to consider setting up its own globally assimilated, but locally driven, AI institute to create an ecosystem for cutting-edge AI innovation, access, and safety, as outlined in the Bletchley Declaration.

Bletchley Declaration:

Bletchley Declaration is a global agreement on the responsible development of AI.
Aim: Enhance global cooperation on artificial intelligence (AI) safety by identifying AI-related risks and developing collaborative policies for mitigation of these risks.
It was signed by the 28 countries & EU at the AI Safety Summit, UK, in November 2023.
Important signatories: India, China, the US, the UK, European Union etc.

What is AI Safety?

AI safety refers to practices and principles that help ensure AI technologies are designed as responsibly as possible to benefit humanity and minimize any potential harm or negative outcomes. It includes:
- Ethical design of algorithms
- Ensuring data privacy and security of individuals and organisations
- Identifying potential AI risks (such as bias, data security, vulnerability to external threats) and developing AI safety measures for risk mitigation.

Scope of AI Safety Institute:

Advocate responsible AI deployment adapting to the unique needs of industries such as healthcare, finance, and logistics etc.
Facilitate proactive information sharing without being a regulator.
Assess the risk to public safety from frontier AI models by leveraging multi-stakeholder consortiums and partnerships.
Improve government capacity and mainstream the idea of external third-party testing and risk mitigation and assessment.
Deliver insights which can transform AI governance into an evidence-based discipline.

Structure of AI Safety Institute:

Standardisation Agency: AISI should be a technical institution that operates exclusively as a technical research, testing, and standardisation agency which sets standards for AI safety. It should be independent from rulemaking and enforcement authorities.
Advisory role of AISI: AISI’s role should not be limited to safety testing and standard-setting but should also be advisory in nature, helping policymakers and the private sector understand and mitigate the socio-technical risks AI poses. The institute could champion perspectives on risks relating to bias, discrimination, social exclusion, gendered risks, labour markets, data collection and individual privacy.
Multi-stakeholder collaboration: In collaboration with a broad range of stakeholders (including startups, large enterprises, academic institutions, civil society organizations, and government bodies), AISI should work closely to develop and disseminate industry best practices, responsible AI use guidelines, and advocate the importance of responsible AI practices across sectors.
Scalability and Global Engagement: AISI should collaborate with governments and stakeholders from across the world. Shared expertise will be essential to keep up with AI’s rapid innovation trajectories and help in scaling the capabilities of AI.

The AI Safety Institute would help India become a global steward for forward-thinking AI governance which embraces many stakeholders and government collaboration. AISI can demonstrate India’s scientific temper and willingness to implement globally compatible, evidence-based and proportionate policy solutions.

Bengaluru to switch to AI-powered traffic signals

Context: In order to deal with the increasing traffic congestion the Bengaluru Traffic Police has planned to go for artificial intelligence enabled real-time adaptive traffic signals for major cross roads. The move is part of the Bengaluru Adaptive Traffic Control System (BATCS), a technology initiative designed to streamline traffic flow and reduce manual intervention at traffic signals.

Bengaluru Adaptive Traffic Control System (BATCS)

The Bengaluru Traffic Police has rolled out Bengaluru Adaptive Traffic Control System (BATCS) technology for traffic management since May 2024.
BATCS uses a real-time Artificial Intelligence (AI)-powered traffic signal control system.
- The system dynamically adjusts signal timings based on real-time traffic densities/conditions, using inputs from camera sensors placed near junctions.
- This ensures optimal traffic flow, minimising delays and providing smoother travel experiences for commuters.
BATCS enables centralised monitoring and control of traffic signals from a central command centre.
- Signals along major traffic corridors are synchronised to create ‘green waves’, allowing vehicles to pass through multiple junctions without stopping, thereby reducing travel time and improving fuel efficiency.
- The system also prioritises emergency vehicles and has the potential for future integration with pedestrian and public transport needs.

Significance:

The BATCS system represents a significant leap forward from other traffic management systems which lacked real-time adaptability. This initiative will enhance the efficiency of city-wide traffic management and reduce the burden on personnel who manually manage signals.
- Bengaluru, known for heavy congestion and a high number of private vehicles, is home to one of the highest concentrations of vehicles in India.
- With two-wheelers accounting for around 60% of the city’s traffic, managing such a diverse mix of vehicles on the road has become increasingly challenging for the authorities.

Real Time Adaptive Traffic Control System (ATCS):

Intelligent traffic management systems incorporated with advanced technologies like AI.

Working:

Sensors (Infrared or video camera) are installed at intersections. These sensors collect real-time data of traffic volume, traffic speed and position of vehicles (using GPS technology).
Collected data is sent to a central command centre. Then the data is processed using Artificial Intelligence and Machine learning techniques to understand the traffic condition and predict the future traffic pattern.
Traffic Signal is dynamically adjusted based on the data analysis.

IndiaAI Mission

Context: The government has finalized a tender to acquire 1,000 graphics processing units (GPUs) as a key component of its ambitious IndiaAI Mission. These GPUs will provide computing power to Indian start-ups, researchers, public sector agencies, and other government-approved entities.

GPU Procurement

GPUs are indispensable for training and developing large-scale AI models, which are foundational to advanced AI applications.
In data centres, GPUs enable parallel processing, AI tasks, media analytics, and 3D rendering, making them vital for complex operations such as machine learning, simulation, and cloud gaming.
Procuring these GPUs will empower Indian startups with the necessary computing power, filling a critical gap in the current market and enabling them to innovate and compete effectively.

IndiaAI Mission

Initiative of: Ministry of Electronics and Information Technology
Aim: To ensure a structured implementation of the IndiaAI Mission through a public-private partnership model aimed at nurturing India’s AI innovation ecosystem.
The IndiaAI Mission will be implemented by the ‘IndiaAI’ Independent Business Division (IBD) under the Digital India Corporation (DIC).

Important components

IndiaAI Compute capacity:
- This pillar focuses on building a high-end, scalable AI computing ecosystem to meet the growing demands of India’s rapidly expanding AI start-ups and research community.
- The ecosystem will consist of AI compute infrastructure with 10,000 or more Graphics Processing Units (GPUs), developed through public-private partnerships.
- Additionally, an AI marketplace will be established to provide AI as a service and offer pre-trained models to innovators, serving as a one-stop solution for resources essential to AI innovation.
IndiaAI Innovation centre:
- The Innovation Centre will lead the development and deployment of indigenous Large Multimodal Models (LMMs) and domain-specific foundational models tailored for critical sectors.t
IndiaAI Datasets platform:
- This platform will facilitate access to high-quality non-personal datasets for AI innovation. A unified data platform will be created to offer seamless access to these datasets for Indian start-ups and researchers.

IndiaAI Application development initiative

This initiative aims to promote AI applications in critical sectors by addressing problem statements provided by Central Ministries, State Departments, and other institutions.
It will focus on developing, scaling, and promoting the adoption of impactful AI solutions that have the potential to drive large-scale socio-economic transformation.

IndiaAI FutureSkills

The FutureSkills initiative is designed to reduce barriers to entry into AI programs.
It will expand AI courses at the undergraduate, masters, and Ph.D. levels, and establish Data and AI Labs in Tier 2 and Tier 3 cities across India to deliver foundational courses.
IndiaAI Startup financing:
This pillar is aimed at supporting and accelerating deep-tech AI start-ups by providing streamlined access to funding, enabling the development of forward-looking AI projects.
Safe and trusted AI:
Recognizing the importance of responsible AI development, this component will facilitate the implementation of responsible AI projects, including the creation of indigenous tools and frameworks.

The IndiaAI Mission will propel innovation and build domestic capacities to ensure the tech sovereignty of India. It will also harness the demographic dividend of the country. IndiaAI Mission will help India demonstrate to the world how this transformative technology can be used for social good and enhance its global competitiveness.