India & Artificial Intelligence

• Enhancing and empowering human capabilities to address the challenges of access, affordability, shortage and inconsistency of skilled expertise;
• Effective implementation of AI initiatives to evolve scalable solutions for emerging economies; Endeavors to tackle some of the global challenges from AI’s perspective related to research, development, technology and responsible AI.
• Harnessing collaborations and partnerships, and aspires to ensure prosperity for all. Thus, #AIforAll means technology leadership in AI for achieving the greater good.

National AI strategy is based on a framework which is adapted to India’s unique needs and aspirations, and aims to achieve India’s full potential of leveraging AI developments. It’s a framework which is an aggregation of the following three distinct, yet inter-related components:

1.Opportunity: The Economic Impact of Artificial Intelligence for India

• AI has the potential to overcome the physical limitations of capital and labour, and open up new sources of value and growth. From an economic impact perspective, AI has the potential to drive growth through enabling:
  • Intelligent Automation i.e. ability to automate complex physical world tasks that require adaptability and agility across industries,
  • Labor and Capital Augmentation enabling humans to focus on parts of their role that add the most value, complementing human capabilities and improving capital efficiency, and
  • Innovation diffusion i.e. launching innovations as it percolates through the economy.
• AI innovations in one sector will have positive consequences in another, as industry sectors are interdependent based on value chain. Economic value is expected to be created from the new goods, services and innovations that AI will enable.
• According to a report by Accenture, it is estimated that AI can boost India’s annual growth rate by 1.3 percentage points by 2035.

2. AI for Greater Good: Social Development and Inclusive Growth

• AI is expected to have the transformative impact on the greater good – like improving the quality of life and access of choice to a large section of the country.
• AI technology can be used in solving the pressing issues like:
  • Increased access to quality health facilities, inclusive financial growth for large sections of population which has hitherto been excluded from formal financial products.
  • Providing real-time advisory related to price, weather etc. to farmers and help address unforeseen factors towards increasing productivity.
• Building smart and efficient cities and infrastructure to meet the demands of rapidly urbanising population.

3. AI Garage for 40% of the World

• India provides a perfect “playground” for enterprises and institutions globally to develop scalable solutions which can be easily implemented in the rest of the developing and emerging economies.
• In simple terms, Solve for India means solve for 40% or more of the world. For instance, an advanced AI based solution for early diagnosis of tuberculosis (one of the top-10 causes of deaths worldwide), once developed and refined in India, could easily be extended to countries in South East Asia or Africa.
• The commonality of issues with regard to the sectors like agriculture, health, education etc across developing countries provides the ideal use case of developing AI solutions that could be adapted for multiple markets.
• Hence, AI technologies suited for the Indian agricultural sector could easily be customised for other developing nations based on their local climatic conditions. Similarly, AI technologies that are capable of imparting quality education to India’s linguistically diverse population could prove very useful in other developing nations.
• Another aspect of India’s potential as a leader in AI is its proven track record in technology solution provider of choice. Solved in India (or solved by Indian IT companies) could be the model going forward for Artificial Intelligence as a Service (AIaaS). Indian IT companies have been pioneers in bringing technology products and developments as solutions across the globe.
• Furthermore, India’s competence in IT combined with opportunities, such as interoperability between multiple languages, provides the much needed impetus for finding scalable solutions for problems that have global implications.

NITI Aayog has decided to focus on five sectors that are envisioned to benefit the most from AI in solving societal needs:

1.Health Sector: Application of AI in healthcare can help address issues of high barriers to access to healthcare facilities, particularly in rural areas that suffer from poor connectivity and limited supply of healthcare professionals. This can be achieved through implementation of use cases such as AI driven diagnostics, personalised treatment, early identification of potential pandemics, and imaging diagnostics, among others. Major initiatives in this area are following:

• 3Nethra: NITI Aayog is working with Microsoft and Forus Health to roll out a technology for early detection of diabetic retinopathy as a pilot project. 3Nethra, developed by Forus Health, is a portable device that can screen for common eye problems. Integrating AI capabilities to this device using Microsoft’s retinal imaging APIs (Application Programming Interfaces) enables operators of 3Nethra device to get AI-powered insights even when they are working at eye checkup camps in remote areas with nil or intermittent connectivity to the cloud. The resultant technology solution also solves for quality issues with image capture and systems checks in place to evaluate the usability of the image captured.
• AI-based Solution to Diagnose Emphysema: Japanese technology firm NTT DATA Services tied up with Pune’s Deenanath Mangeshkar Hospital last year to use an AI-based solution to diagnose emphysema, a chronic condition of the lungs. Over a six month period, the detection rate of its proof-of-concept solution turned out to be 170% higher than traditional systems.
• Predictive Diagnostics: Home-grown technology services provider Persistent Systems has been using AI and Machine Learning (ML) in healthcare systems for predictive diagnostics. The company has partnered with Prashanti Cancer Care Mission to develop a platform that will identify new markers in patients with triple negative breast cancer, in order to detect the disease early.

2.Agriculture Sector: Artificial intelligence is silently but increasingly entering Indian agriculture and hence affecting our society at large. It holds the promise of driving a food revolution and meeting the increased demand for food (global need to produce 50% more food and cater to an additional 2 billion people by 2050 as compared to today). It also has the potential to address challenges such as inadequate demand prediction of crops, lack of assured irrigation, and overuse / misuse of pesticides and fertilisers. Some use cases include improvement in crop yield through real time advisory, better comparison of the desired outcomes through data analysis of historic values, personalized and context based advisories to individual farmers, advanced detection of pest attacks, and prediction of crop prices to inform sowing practices thereby improving the farming processes. Notable steps taken in this field are following:

• AI Sowing App: Microsoft in collaboration with ICRISAT developed an AI Sowing App powered by Microsoft Cortana Intelligence Suite including Machine Learning. The app sends sowing advisories to participating farmers on the optimal date to sow. The farmers don’t need to install any sensors in their fields or incur any capital expenditure. All they needed was a feature phone capable of receiving text messages. The advisories contained essential information including the optimal sowing date, soil test based fertilizer application, farm yard manure application, seed treatment, optimum sowing depth, and more. In 2017, the program was expanded to touch more than 3,000 farmers across the states of Andhra Pradesh and Karnataka during the Kharif crop cycle (rainy season) for a host of crops including groundnut, ragi, maize, rice and cotton, among others. The increase in yield ranged from 10% to 30% across crops.
• AI for Precision Farming: NITI Aayog and IBM have partnered to develop a crop yield prediction model using AI to provide real time advisory to farmers. IBM’s AI model for predictive insights to improve crop productivity, soil yield, control agricultural inputs and early warning on pest/disease outbreak will use data from remote sensing (ISRO), soil health cards, IMD’s weather prediction, crop phenology, etc. to give accurate prescriptions to farmers. The project is being implemented in 10 Aspirational Districts across the States of Assam, Bihar, Jharkhand, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh.
• AI for Herbicide Optimisation: Blue River Technology has designed and integrated computer vision and machine learning technology that enables farmers to reduce the use of herbicides by spraying only where weeds are present, optimising the use of inputs in farming – a key objective of precision agriculture.
• AI can be used to create intelligent systems which are embedded in machines that can work with higher accuracy and speed than humans and at the same time be responsive like humans. AI together with Internet of Things (IoT) and Sensor Technology can be the great enabler of precision agriculture. It can also play a critical role along with remote sensing technology in wide scale implementation of Climate Smart Agriculture. Thus, agriculture sector which employs almost 50% of the Indian population can benefit immensely with AI revolution across the spectrum from farm to fork.

3.Education and Skilling Sector: AI can potentially solve for quality and access issues observed in the Indian education sector. Potential use cases include augmenting and enhancing the learning experience through personalised learning, automating and expediting administrative tasks, and predicting the need for student intervention to reduce dropouts or recommend vocational training.

• Smart Content for Improved Interactivity: Content Technologies Inc. (CTI), an AI research and development company, develops AI that creates customised educational content. Using deep learning to absorb and analyse existing course materials, textbooks, and course curriculum, the technology creates custom learning materials, including textbooks, chapter summaries, and multiple-choice tests.
• ReadEx: A recent hackathon conducted by NITI Aayog also featured ‘ReadEx’, an android application that does real-time question generation using NLP (Natural Language Processing), content recommendations, and flashcard creation.
• Prediction of Dropout Rate:Microsoft is helping in predicting dropouts in Andhra Pradesh. Based on specific parameters, such as gender, socio-economic demographics, academic performance, school infrastructure and teacher skills, an application powered by Azure Machine Learning processes the data pertaining to all students to find predictive patterns. With these data insights, the district education officials can intervene and help students who are most likely to dropout. A variety of programs and counselling sessions could be conducted for these students and their parents. The Andhra Pradesh government, based on machine learning and analytics, had identified about 19,500 probable dropouts from government schools in Visakhapatnam district for the academic year (2018- 19).

4.Smart Mobility and Transportation: Potential use cases in this domain include autonomous fleets for ride sharing, semi-autonomous features such as driver assist, and predictive engine monitoring and maintenance. Other areas that AI can impact include autonomous trucking and delivery, and improved traffic management.

• AI for Railways: According to official figures, more than 500 train accidents occurred between 2012- 2017, 53% of them due to derailment. Train operators can obtain situational intelligence through real-time operational data and analyse them in three different dimensions: spatial, temporal and nodal. Recently, the Ministry of Railways, Government of India has decided to use AI to undertake remote condition monitoring using non-intrusive sensors for monitoring signals, track circuits, axle counters and their sub-systems of interlocking, power supply systems including the voltage and current levels, relays and timers.
• LogiNext: It is an AI powered start up that helps in managing field services. It tracks and optimizes field agent movements in real time on a single map interface. It empowers its users by providing them the power to track (real-time) the shipment every single minute. It provides insights and visualizations based on predictive and big data analytics. Logistics analytics helps the user to accurately predict the future with algorithm-enabled location intelligence and optimize the logistics and field service management operations. It also provides every single detail right from pickup to delivery. It also provides complete field service management analytics.

5.Smart Cities and Infrastructure: Integration of AI in newly developed smart cities and infrastructure could also help meet the demands of a rapidly urbanising population and providing them with enhanced quality of life. Potential use cases include traffic control to reduce congestion and enhanced security through improved crowd management.

• Pune- a True Smart City: AI plays a key role in securing smart cities. For this, the University of Toronto has partnered with IIT-Bombay to deploy AI to make Pune a truly Smart City in India. The long-term partnership focuses on tackling the large number of rural immigrants arriving in Pune, cyber security, digital systems interoperability and affordable housing. Pune will reportedly serve as a test case and also as a template for AI to be leveraged for finding solutions in a rapidly urbanizing country like India.
• Smart city and its AI-powered-IoT use cases: A smart city has lots of use cases for AI-powered IoT-enabled technology, from maintaining a healthier environment to enhancing public transport and safety.
• Intelligent Traffic Management System (ITMS), Mumbai: It is aimed at improving traffic flow and cut travel time, the ITMS will include installation of 4,705 smart traffic signals; 300 red light violation detection cameras; 925 automatic number plate recognition cameras and 300 specialised cameras to identify vehicles moving the wrong way and 300 other cameras to detect illegal parking. On the basis of dynamic traffic data captured by the system’s software, the city traffic police will also be able to modify signal timings and regulate flow.

The analysis of focus sectors also details a multitude of challenges that India needs to overcome to realise the full potential of a disruptive technology like AI.

• Technical Challenges: Absence of enabling data ecosystems – access to intelligent data, high resource cost and low awareness for adoption of AI, absence of collaborative approach to adoption and application of AI, inadequate availability of AI expertise, manpower and skilling opportunities and low intensity of AI research both in: Core research in fundamental technologies and Transforming core research into market applications.
• Regulatory Challenges: Data is one of the primary drivers of AI solutions, and thus appropriate handling of data, ensuring privacy and security is of prime importance. Challenges include data usage without consent, risk of identification of individuals through data, data selection bias and the resulting discrimination of AI models, and asymmetry in data aggregation. Further challenges remain, especially in respect of applying stringent and narrowly focused patent laws to AI applications – given the unique nature of AI solution development.
• Socio-Economic Challenges: There is a challenge of finding a way to bridge the digital divide in the society between haves and have-nots and to hand-pick such innovations which are worthy of public funds and partnerships.
• Ethical Challenges: As AI-based solutions spread across the entire spectrum of our lives, questions on ethics, privacy and security will also emerge. Some of the ethical considerations of AI are related to Fairness, Accountability and Transparency.

Incentivise core and applied research in AI through following two-tiered structure:

(a) Centre of Research Excellence (CORE) focused on developing better understanding of existing core research and pushing technology frontiers through creation of new knowledge;

(b) International Centers of Transformational AI (ICTAI) with a mandate of developing and deploying application-based research. Private sector collaboration is envisioned to be a key aspect of ICTAIs.

• Use Common Cloud computing platform AIRAWAT (AI Research, Analytics and knowledge Assimilation platform) for Big Data Analytics and Assimilation, with a large, power optimised AI Computing infrastructure.
• To achieve technology leadership in AI, pursue “moonshot” projects – ambitious explorations that aim to push the technology frontier to solve some of the biggest challenges.
• Develop a dedicated supranational agency “CERN for AI” to channel research in solving big, audacious problems of AI.
• Accelerate Skilling for AI across institutions and industries as AI will disrupt the nature of jobs and shift the benchmarks of technological aptitude. This could be done via the adoption of decentralised teaching mechanisms working in collaboration with the private sector and educational institutions to prescribe certification with value.
• Accelerate the adoption of AI across the value chain through creating a multi-stakeholder National AI Marketplace (NAIM), partnerships and collaborations, spreading awareness on the advantages AI offers and supporting startups.
• Furthermore, for accelerated adoption of AI, the government should play the critical role of a catalyst in supporting partnerships, providing access to infrastructure, fostering innovation through research and creating the demand by seeking solutions for addressing various governmental needs.
• Establish a data protection framework with legal backing and sectoral regulatory frameworks for additional protection to user privacy and security.
• Benchmark national data protection and privacy laws with international standards and encourage self-regulation
• A consortium of Ethics Councils at each Centre of Research Excellence can be set up and it would be expected that all COREs adhere to standard practices while developing AI technology and products.
• To tackle the issues of Intellectual Property, establishment of IP facilitation centers to help bridge the gap between practitioners and AI developers, and adequate training of IP granting authorities, judiciary and tribunals is suggested.

AI is the game changer of 21^st century and India should gear up in time to exploit its full potential for its socio-economic development in an inclusive and sustainable way. Moreover, as we enter the age of AI, no industry will be exempt from its impact. “Disrupt before you are disrupted” is the mantra for the future and when it comes to AI disruption, fortune favours the prepared!