Introduction

The information age that began in the middle of the 20th century led to rapid advances in computing capabilities, capacity, and communications infrastructure, including innovations in hardware, algorithms, systems, and tools, and to the development of new technologies and products including instruments, communication devices, and sensors. Access to computing power and these new technologies combined with the decreasing costs of data acquisition and storage have resulted in the generation and collection of vast amounts of data from different sources across science, industry, and government. The need to leverage and draw insights from these massive data have led to fundamental breakthroughs in deep learning and natural language processing that are driving the AI revolution in the 21st century.

The availability of big data, including public health, demographic, and economic data from governments and agencies, geospatial data from satellites, financial and transactional data from companies, clinical and biomedical data from hospitals, social media data, and streaming data from the Internet-of-Things, is driving AI innovation, adoption, and implementation in many sectors of the economy. From leveraging deep learning to facilitate the development of new vaccines and repurposing of drugs, automated diagnoses of diseases using images, discovery of new materials through machine learning, real-time weather forecasting and prediction of extreme events, precision agriculture and personalized learning, and autonomous vehicles, Data Science and AI tools and technologies are enabling breakthroughs in many fields of science and engineering, leveraging these scientific breakthroughs to accelerate progress on pressing societal challenges such as public health and climate change, and fundamentally transforming the global economy.

The 2022 AI Index Report published by the Stanford Institute for Human-Centered Artificial Intelligence shows that global corporate investment in AI increased from USD 119.5 billion in 2020 to USD 176.5 billion in 2021 [1]. Private investment in 2021 was USD 93.5 billion, more than double the total in the previous year, with the top five sectors being (i) data management, processing, and cloud, (ii) medical and healthcare, (iii) fintech, (iv) AV, and (v) semiconductors.

As AI tools and technologies become ubiquitous across different sectors, the demand for individuals with a wide range of technical and data skills will continue to increase. The AI workforce will include careers across different application domains requiring a range of skills from coding, visualization, modelling and data analysis, software engineering, to cutting-edge research and development of the next generation of tools and technologies. Gelhaus et al. define the AI Workforce as “the set of occupations that include people who are qualified to work in AI or on an AI development team, or have the requisite knowledge, skills, and abilities (KSAs) such that they could work on an AI product or application with minor training” [2].

While AI is often viewed as a field within Computer Science, there is an advantage to viewing AI through an interdisciplinary lens, recognizing the role that linguistics, biology, cognitive sciences, psychology, mathematics, and statistics play in the development of new tools and technologies [3]. This holistic view can spur the development of innovative, interdisciplinary programs that train the next generation of researchers enabling breakthroughs in many fields of science and engineering.

Developing a diverse, globally-engaged AI workforce will require significant investments in infrastructure through public-private partnerships. Academic institutions, industry, government agencies, and private foundations must come together to support the development of innovative education pathways for students, new interdisciplinary programs that emphasize applications, and training initiatives to upskill the existing workforce.

1.1 The AI Workforce in India

India is young and ambitious. With approximately 63 % of the population in the 15-59 age group, India has one of the youngest populations in the world. This demographic dividend will require major investments in skill development and the creation of an enabling ecosystem to ensure India’s young citizens are ready to enter the highly-skilled, technology driven workforce.

According to NASSCOM, the Indian IT industry including e-commerce has a relative share of about 9% of India’s GDP, and was estimated at USD 306 billion in FY 2022 [4]. Out of this, exports contributed to about USD 178 billion. Indian IT, which is probably India’s largest employer of undergraduates and graduates, provides employment to about 5 million professionals. The Indian IT sector added about 2,25,000 employees during FY 2022.

In 2020, NASSCOM forecasted that the demand for digital skills that includes AI/DS is likely to grow 20X times by 2024 [5]. Global spending on AI is expected to cross USD 500 billion in 2023 [6]. With AI and DS among the largest drivers of IT spending globally, this translates into a huge opportunity for the IT industry in India. The Indian IT industry is already on a war footing to skill its workforce. It is estimated that about 200,000 IT professionals were skilled on digital technologies in 2019-20, and more than 4 million professionals are expected to be trained in digital skills between 2020 and 2025. There is also an increasing demand for AI/DS educated workforce in startups and all other domains of Indian industry.

As the demand for technical and data skills increase, the Data Science Education market in India is estimated to grow from USD 103 million in 2020 to USD 626 million by 2025 [7]. Indian policy makers and industry groups are certainly cognizant of these trends, with AI and Data Science representing critical focus domains for India’s workforce development. India’s New Education Policy (2020) acknowledges the importance of these emerging fields right at the beginning [8],

“The world is undergoing rapid changes in the knowledge landscape. With various dramatic scientific and technological advances, such as the rise of big data, machine learning, and artificial intelligence, many unskilled jobs worldwide may be taken over by machines, while the need for a skilled workforce, particularly involving mathematics, computer science, and data science, in conjunction with multidisciplinary abilities across the sciences, social sciences, and humanities, will be increasingly in greater demand.”

NITI Aayog in its National Strategy for Artificial Intelligence discussion paper estimates that AI has the potential to add about USD 1 trillion to the Indian economy by 2035 [9]. Prominent among this paper’s recommendations is a focus on workforce education and training. Indian higher education institutions are responding to the call to action, launching new programs at all levels. Some examples include:

»  IIT Madras launched the world’s first online B.Sc. in programming and data science in 2020, and announced a 4-year BS in 2022.

»  IISc announced a foundational B.Tech. program in Mathematics and Computing in 2022. The interdisciplinary program leverages expertise across the institute to encourage cross-domain research and offers different specializations.

»  IIM Bangalore has started an MBA programme in business analytics that is based on a “DS + X” paradigm [10]. The objective is the innovative application of analytics to solve management problems from different industries such as aerospace, banking and finance, healthcare, insurance, manufacturing, pharmaceutical, retail, services, software, sports, etc.

Private philanthropy is also focusing on AI and data science education. One example is the Bhupat & Jyoti Mehta Family Foundation investments in the Mehta Family School of Data Science and Artificial Intelligence at the Indian Institute of Technology Guwahati and the Indian Institute of Technology Roorkee. In addition to the institutions listed above, a number of public and private institutions have recently launched, or are planning to launch programmes in AI, Data Science, or related areas.

itihaasa’s study on the landscape of AI and Machine learning (ML) research in India highlights the need to ramp up graduate and undergraduate level education and provides some recommendations on potential pathways to train students [11]. These include:

1.  Summer schools on AI/DS for research-oriented students.

2.  Real world driven competitions / hackathons on AI/DS. Provide India-specific problems that include need for data acquisition as well.

3.  Undergraduate final year project - students should be encouraged to participate in a national level AI/DS solution development challenge. India should create a platform that offers problems and data and the winners should be promoted on social media so that there is a pull created to participate in such projects.

Government Agencies have also launched new initiatives to support R&D and training. The Indian Government launched the National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS) to “create a strong foundation and a seamless ecosystem for CPS technologies by coordinating and integrating nationwide efforts encompassing knowledge generation, human resource development, research, technology and product development, innovation and commercialization.” Under the NM-ICPS, the Department of Science and Technology has funded over 25 Technology Innovation Hubs (TIH) with a total five-year investment of INR 3200 crore (USD 430 million). The National Skill Development Mission is collaborating with NASSCOM on AI skilling and with IBM on a Train-the-Trainer program in Artificial Intelligence.

1.2 The AI Workforce in the United States

The United States is recognized as the global leader in AI with a long history of public and private investments in academic and industrial scientific research. The innovation ecosystem includes technology hubs in Silicon Valley, Research Triangle, Boston, and New York, top public and private research institutions including Stanford, Massachusetts Institute of Technology, Carnegie Mellon, and the University of California Berkeley just to name a few. According to the 2022 AI Index Report, overall U.S. AI private investment totalled approximately USD 52.9 billion, over three times that of second ranked China [1]. The United States also led in several key metrics including the number of newly funded AI companies. The Center for Security and Emerging Technology report published in October 2021 estimates the AI workforce in the U.S. to be 14 million, approximately 9% of total U.S. employed. The report also states that the U.S. AI workforce grew at a rate of 21% over the period 2015-2019 compared to an overall 6% rate and predicts demand for AI-related professions “will likely be strong over the next decade, projected to grow twice as fast as for all U.S. occupations [2].”

The U.S. Congress established the National Artificial Intelligence Initiative (NAII) “to ensure continued U.S. leadership in AI R&D; lead the world in the development and use of trustworthy AI systems in public and private sectors; prepare the present and future U.S. workforce for the integration of artificial intelligence systems across all sectors of the economy and society; and coordinate ongoing AI activities across all Federal agencies, to ensure that each informs the work of others.” The National Artificial Intelligence Research And Development Strategic Plan released by the White House Office of Science and Technology Policy (OSTP) notes that “It is critical to maintain a robust academic research ecosystem in AI that, in collaboration with industry R&D, can continue to deliver tremendous dividends by advancing national health, prosperity, and welfare, and securing the national defense.” The document further adds that “Federal agencies must therefore continue to strategically foster expertise in the AI R&D workforce that spans multiple disciplines and skill categories to ensure sustained national leadership [12].”

U.S. Academic Institutions have been leading the way in the development of new programs in AI and Data Science. Some examples are listed below:

»  Carnegie Mellon University offers a number of different degree programs to prepare students to “Create the AI of the Future”. Some of the unique offerings include graduate degrees in Human-Computer Interaction, Robotics, Language Technologies, Artificial Intelligence and Innovation, and an undergraduate degree in Computational Biology.

»  The University of California, Berkeley offers an undergraduate degree in Data Science with curricula that cuts across Computer Science, Statistics, Humanities, and the Social Sciences. Data 8: The Foundations of Data Science course was one of the early attempts to integrate computational and inferential thinking. The course has been replicated in other institutions.

»  The Institute for Advanced Analytics at North Carolina State University was the first institution in the U.S. to offer a Master of Science in Analytics. The program offers a unique, innovative curriculum that requires students to work on problems provided by industry and government agencies.

U.S. Institutions are launching programs at all levels, including AI associate degree programs at community colleges.

The report “A 20-Year Community Roadmap for Artificial Intelligence Research in the U.S.” notes that “Comprehensive changes need to be undertaken in order to restructure and train a diverse AI workforce to prepare highly skilled researchers and innovators [3].” Some of the recommendations for training a diverse workforce include

»  Development of guidelines for professional programs/ certifications

»  Interdisciplinary AI Training: programs to train students for careers at the interface of AI and other disciplines.

»  The need to incorporate ethics training

The National Science Foundation launched the National AI Research Institutes program in 2019, supporting the establishment of “national nexus points for collaborative efforts spanning institutions of higher education, federal agencies, industry, and nonprofits/foundations” that would “accelerate the transition of AI innovations into many economic sectors, and nurture and grow the next generation of talent.” The program is a partnership with several Federal Agencies and private companies including Google, Amazon, Intel, and Accenture.

1.3 The India-United States Partnership

Across the world, countries are developing frameworks and policies to leverage the power and potential of AI to serve the needs of their citizens while at the same time grappling with issues of ethics, privacy, access, and the impact of new technologies and automation on the workforce.

The Indian and U.S. Governments have developed comprehensive strategic plans on AI that (a) identify priority areas for investment in research and infrastructure, (b) highlight the need for workforce development, (c) address the role of partnerships to accelerate progress, and (d) highlight the need for policies and guidelines that address data sharing, benchmarking, ethics and values, and privacy.

India’s “National Strategy for Artificial Intelligence” prepared by Niti Aayog identified key sectors where AI tools and technologies could yield significant societal impact and developed a series of recommendations to address critical R&D and workforce issues [9]. The National Artificial Intelligence Research And Development Strategic Plan: 2019 Update released by the White House Office of Science and Technology Policy (OSTP) identified strategic priorities and challenges [12].

A summary of the key takeaways from the two reports is provided below:

Table 1: NITI Aayog Report – Focus Areas and Recommendations

Focus Areas :
1.	Healthcare: increased access and affordability of quality healthcare;
2.	Agriculture: enhanced farmers’ income, increased farm productivity and reduction of wastage;
3.	Education: improved access and quality of education;
4.	Smart Cities and Infrastructure: efficient and connectivity for the burgeoning urban population;
5.	Smart Mobility and Transportation: smarter and safer modes of transportation and better traffic and congestion problems
Recommendations :
»	Research: Incentivising Core and Applied research in AI
»	Skilling for the AI age: Getting India ready for the AI wave
»	Accelerating Adoption: AI across the value chain
»	Ethics, Privacy, Security and Artificial Intelligence: Towards a “Responsible AI”

Table 2: National AI R&D Strategic Plan: 2019 Update - Priorities

1.	Make long-term investments in AI research.
2.	Develop effective methods for human-AI collaboration.
3.	Understand and address the ethical, legal, and societal implications of AI.
4.	Ensure the safety and security of AI systems.
5.	Develop shared public datasets and environments for AI training and testing.
6.	Measure and evaluate AI technologies through standards and benchmarks.
7.	Better understand the national AI R&D workforce needs.
8.	Expand public-private partnerships to accelerate advances in AI.

The need for investments in AI R&D, workforce development, research data infrastructure, and public-private and international collaborations are priorities for both governments.

Science and Technology form the cornerstone of the strategic partnership between India and the United States. From the creation of the Indian Institute of Technology, Kanpur with the assistance of a consortium of nine U.S. research universities in 1960, to joint collaborations in areas such as Space, Energy, Agriculture, and Healthcare, to the large numbers of Indian students who pursue advanced degrees in Science and Engineering at U.S. Institutions, these partnerships have strengthened over the years. The Indian diaspora in the U.S. includes leaders within the tech industry, faculty at the top U.S. academic institutions, and leading researchers in the health and biomedical fields. Top U.S. companies have large operations in India, including R&D facilities, leveraging the incredibly talented pool of engineers and quantitatively trained graduates from Indian universities.

The Indo-U.S. Science and Technology Forum (IUSSTF), a binational foundation, was established by the two Governments in 2000 to promote, catalyze and seed S&T cooperation between India and the United States. IUSSTF is uniquely positioned to proactively engage the S&T communities by identifying “leading edge areas” that are high-priority for both nations, bringing together key stakeholders to help create synergies, and supporting workshops/ networking opportunities to initiate new collaborations.

Recognizing the promise and potential of AI, IUSSTF launched the U.S. - India Artificial Intelligence (USIAI) Initiative, a unique opportunity for the world’s two largest democracies to strengthen their strategic partnership by focusing on AI cooperation in critical areas that are priorities for both countries. USIAI will serve as a platform to discuss opportunities for bilateral AI R&D collaboration, identify key challenges and barriers to adoption of AI, share ideas for developing an AI workforce, and recommend modes and mechanisms for catalyzing partnerships.

IUSSTF is partnering with itihaasa Research and Digital (https://itihaasa.com/), a not-for-profit, organization that studies the evolution of technology and business domains in India, on a series of activities in the area of Education, Training, and Workforce Development under the USIAI umbrella. With AI workforce development a priority for both countries, the USIAI platform will allow Indian and U.S. academic institutions to come together to share best practices for education and program development, identify synergies for collaborative research and training including faculty and student exchange, and address the challenges associated with developing a diverse, robust AI workforce.

In May 2022, Prime Minister Modi and President Biden welcomed the launch of the United States–India Initiative on Critical and Emerging Technologies (iCET) to expand partnership in these strategic areas. With AI being a priority for both the United States and India, a partnership between our two democracies can lead to a strategic framework for AI that conforms to the shared values of openness, transparency, and reciprocity and encourages responsible innovation that will benefit both countries.

This report focuses on AI and Data Science higher education initiatives. The second chapter provides an overview of the AI/DS initiatives and their outcomes in the U.S. and Europe. The third chapter presents the key results of a first of a kind survey on the landscape of AI/DS higher education in India. The final chapter of the report presents key action plans in the form of key next steps to be undertaken.