In 2023, the Ministry of Electronics and Information Technology published the report “India AI, 2023.”The document outlines the comprehensive strategy for enhancing India’s Artificial Intelligence (AI) ecosystem under the initiative “IndiaAI,” which aims to position the country as a global leader in AI by leveraging its unique strengths and addressing critical societal challenges. Below is a detailed summary of the major points covered in the document.
Working Group 1 –
The primary objective of Working Group 1 is to detail the operational aspects of establishing three Centres of Excellence (CoEs) that aim to leverage India’s unique strengths in Artificial Intelligence (AI) to address critical societal challenges. The CoEs will not only focus on foundational and multidisciplinary research in AI but also on the development and adoption of indigenous AI technologies both nationally and internationally.
Key Features of the CoEs
Research and Innovation:
The CoEs will conduct foundational research in diverse AI areas to generate new knowledge relevant to Indian society’s unique advantages and challenges. They will focus on the application of AI in critical sectors, leveraging the vast data availability and scale within India.
Collaboration:
The CoEs will bring together experts from academia, industry, and research entities to work on cutting-edge research and create high-quality AI solutions. They will foster a culture of creativity, experimentation, and entrepreneurship to maximize the potential of AI.
Governance and Sustainability:
Each CoE will be established as a Section 8 company, ensuring an independent and autonomous governing structure.
The government funding for the CoEs is initially set for five years, with expectations for the industry and other partners to start contributing from the third year.
The CoEs will aim for long-term self-sustainability, targeting 50% self-supporting within three years and 100% within five years.
Operational Framework
Hub and Spoke Model:
Each CoE will have a Hub institution responsible for the overall leadership and coordination, supported by a network of Spoke institutions that provide complementary strengths.
The Spokes will engage in technology development, user studies, field trials, and training, helping to implement solutions developed by the CoE.
Selection Process:
The selection of CoE institutions involves a two-step process:
Step 1: Submission of a short concept proposal by qualifying academic institutions.
Step 2: Shortlisted institutions will submit a detailed proposal outlining their vision and plans for the CoE.
Evaluation and Monitoring
The performance of the CoEs will be regularly evaluated against deliverables and timelines using identified Key Performance Indicators (KPIs) to ensure that objectives are achieved.
A Governance Council and Project Review & Steering Group will be constituted to oversee operations and ensure adherence to the goals set forth.
Working Group 2: India Dataset Platform (IDP)
Objective
The primary goal of the India Dataset Platform (IDP) is to create a robust ecosystem for data-driven governance and innovation by providing a framework for identifying and accessing potential datasets. This platform aims to enable better decision-making strategies and foster an AI-based startup and research innovation ecosystem.
Key Features of IDP
- Federal Structure: The IDP is designed as a federal structure to accommodate data providers from various ministries and departments, allowing them to maintain autonomy while enabling collaborative analysis across datasets.
- Data Discovery: A single platform for data access and linking, promoting efficient data utilization and discovery.
- Value-Added Services: A central agency will provide services to assist government ministries in managing data, ensuring quality and consistency.
- Data Security and Privacy: Development of robust security measures and compliance with data protection regulations.
- Technical Infrastructure: Creation of a scalable and secure platform with APIs for data access and availability.
- Governance and Access Control: Implementation of clear data stewardship, access controls, and usage agreements.
- Collaboration and User Adoption: Engaging with data consumers and promoting the platform’s benefits through user-friendly interfaces.
- Continuous Improvement: Regular evaluation and adaptation of the platform based on feedback and performance metrics.
Working Group 3: Institutional Capacity and Design of NDMO
Objectives
The National Data Management Office (NDMO) aims to streamline data governance and enhance data quality for catalysing data-based innovation and research in alignment with the Draft National Data Governance Policy (NDGP).
Key Functions of NDMO
- Data Regulation: Establishing standards, guidelines, and processes for data management in government ministries and departments.
- Data Quality Assurance: Conducting audits and assessments to ensure compliance with data governance standards.
- Capacity Building: Providing training and resources for government officials to improve their data management capabilities.
- Collaboration: Coordinating with stakeholders to facilitate effective data sharing and utilization.
Structure of NDMO
- Chief Data Officer (CDO): Responsible for overseeing data governance and management practices.
- Functional Divisions:
- Data Integrity & Audit: Ensures compliance with standards and conducts periodic audits.
- Data Management: Develops frameworks for non-personal data collection and processing.
- Data Regulation: Oversees metadata standards and guidelines for data security and privacy.
- HR & Finance: Manages personnel and budgetary compliance.
- Legal: Addresses legal matters related to data governance.
Data Management Units (DMUs)
The NDMO will establish Data Management Units within each ministry to operationalize data governance efforts, ensuring consistent data quality and accessibility. Each DMU will include:
- A Chief Data Officer
- Data Analysts
- Data Scientists
- IT support personnel
Tangible Outputs and Targets
The NDMO is tasked with achieving specific outputs and timelines, including the establishment of DMUs, publication of data standards, and the launch of the India Dataset Platform. Key milestones are set for short-term, medium-term, and long-term objectives to ensure effective implementation.
Working Group 4 – IndiaAI Future Design
Objectives:
The primary objective of Working Group 4 (WG4) is to assess and design funding mechanisms for AI startups to enhance AI innovation in India. The vision is to develop the next 100 AI unicorns through the India AI program.
Key Recommendations:
- Empower AI Startups:
- Facilitate the development of AI-enabled products and solutions tailored for both domestic and global markets under initiatives like “Make AI in India” and “Make AI Work for India.”
- Utilize R&D Ecosystem:
- Leverage existing research and development ecosystems to stimulate innovation in AI and emerging technologies.
- Funding Mechanism:
- Establish a structured funding mechanism to support promising AI startups. This includes:
- Sourcing, executing, and monitoring challenges.
- Providing support tailored to AI startups.
- Establish a structured funding mechanism to support promising AI startups. This includes:
- Corporate and Institutional Partnerships:
- Build links with global AI startup ecosystems and aggregate resources from various stakeholders, including industries and institutions.
- Capacity Building and Performance Optimization:
- Enhance the performance of MeitY-related assets, programs, and schemes through targeted capacity-building initiatives.
- Community Engagement:
- Foster a self-sustaining community of AI startups through awareness programs and effective media marketing strategies.
Scheme Management:
To ensure effective governance, an Empowered Committee (EC) will be constituted under the chairmanship of the Secretary of MeitY. The EC will oversee the implementation of the IndiaAI scheme, reviewing progress every three to six months.
Monitoring:
Mid-term appraisals will be conducted after two years to assess the impact of the scheme against its stated objectives and make necessary adjustments.
Working Group 5 – India AI- Future Skills
Objectives:
Working Group 5 (WG5) aims to address the growing demand for AI-related skills and prepare the Indian workforce for future employment in AI and related areas.
Key Recommendations:
- Model Curriculum & Repository:
- Develop a comprehensive AI curriculum that encompasses fundamentals such as mathematics, statistics, machine learning, deep learning, natural language processing (NLP), computer vision, AI ethics, and practical projects.
- Framework for Skill Development:
- Structure curricula into technology-specific, infrastructure-specific, application-specific, and best-practice categories to ensure relevance and applicability.
- Career Path Mapping:
- Create a mapping of various career paths in AI, including roles like AI researcher, machine learning engineer, data scientist, AI architect, and more.
- Community Building:
- Establish an India-specific AI community to promote national challenges and data sharing, and to support collaborations among researchers and practitioners.
- Continuous Learning:
- Promote ongoing learning and upskilling initiatives through partnerships with educational institutions and industry to keep pace with AI advancements.
- Mentorship and Support:
- Facilitate mentorship opportunities for students and professionals to enhance their skills and employability in the AI sector.
Vision:
The Working Group’s vision is encapsulated in the phrase “A Transformative Approach: From Job Takers to Job Providers,” aiming to shift the perspective of the workforce towards creating jobs rather than just filling them.
Both working groups play a crucial role in shaping India’s AI landscape by fostering innovation, enhancing skills, and establishing a robust ecosystem for AI startups and education.
Working Group 6- IndiaAI future labs compute
Objectives and Recommendations
The main objective of Working Group 6 is to provide a comprehensive overview of the current state of AI computational resources in India and identify the limitations and opportunities for enhancing AI compute capacity. The group emphasizes the importance of establishing a robust AI compute infrastructure to facilitate the development and deployment of AI applications.
Key recommendations include:
- Infrastructure and Compute Capacity:
- Establish a high-end compute center with 10,000 GPUs, achieving 40 Exaflops performance.
- Create four mid-range compute centers, each with 750 GPUs, totalling 3,000 GPUs for 12 Exaflops performance.
- Develop a high-performance storage system with a capacity of 200 PB.
- Monitoring and Maintenance:
- Implement regular monitoring of cloud infrastructure to ensure proper functioning and appropriate data management.
- Conduct regular security checks and updates to protect against cyber threats, ensuring compliance with relevant regulations.
- Capacity Building and Collaboration:
- Promote AI education and training to cultivate an AI-ready talent pool.
- Foster collaborations across different sectors to leverage AI technologies and develop cross-sector solutions.
- Increase public awareness and understanding of AI technologies to foster a more AI-accepting culture.
Overall, the group emphasizes the need for a structured and well-managed AI compute infrastructure to support India’s AI ambitions and ensure that the country remains competitive on a global scale.
Working Group 7: Semicon IndiaAI Chipsets
Objectives and Recommendations
The primary goal of Working Group 7 is to conceptualize the design for AI compute chipsets, assess the requirements for technical capabilities, latency, specifications, and to elaborate on the pricing models for semiconductor design in India.
Key recommendations include:
- Design Approaches for AI Chipsets:
- Focus on developing integrated circuits (ICs) and chipsets tailored for AI applications, ensuring they meet the performance and efficiency needs of various AI workloads.
- Application Areas:
- Identify critical sectors for the deployment of AI chipsets, including healthcare, agriculture, and smart cities, where advanced computational capabilities can drive innovation.
- Criteria for Assessing AI Hardware:
- Establish clear criteria for evaluating the performance, scalability, and flexibility of AI chipsets to ensure they meet the evolving demands of AI applications.
- Financial Incentives:
- Propose a Design Linked Incentive (DLI) Scheme to support domestic companies and startups in various stages of semiconductor design, development, and deployment for AI.
The report underscores the importance of building a strong semiconductor ecosystem in India that can support the nation’s growing AI and digital ambitions, ensuring that India becomes a competitive player in the global semiconductor market.
Together, these working groups highlight the strategic focus on enhancing AI compute capacity and semiconductor design capabilities as integral to India’s AI vision and its implementation