By – Prof. V.P. Gupta,Director, Rau’s IAS Study Circle, New Delhi – Jaipur – Bengaluru

Recently, NITI Aayog released a discussion paper titled ‘National Strategy on Artificial Intelligence’. NITI Aayog report is premised on the proposition that India, given its strengths and characteristics, has the potential to position itself among leaders on the global AI map wherein India can leverage the transformative technologies to ensure social and inclusive growth. In addition, India can also strive to replicate these solutions in other similarly placed developing countries.

Let us understand the aspects of Artificial Intelligence from the perspective of UPSC Main Examination within the section of GS-III, in particular:

•   Technology and its applications

•   Economic development

•   e-technology in the aid of farmers

•   Security

Understanding AI Technology

AI refers to the ability of machines to perform cognitive tasks like thinking, perceiving, learning, problem solving and decision making. Initially conceived as a technology that could mimic human intelligence, AI is a constellation of technologies that enable machines to act with higher levels of intelligence and emulate the human capabilities of sense, comprehend and act.

Machine Learning means the ability to learn without being explicitly programmed. Machine Learning involves the use of algorithms to parse data and learn from it, and making a determination or prediction as a result. Instead of hand coding software libraries with well-defined specific instructions for a particular task, the machine gets “trained” using large amounts of data and algorithms, and in turn gains the capability to perform specific tasks.

Deep Learning is a technique for implementing Machine Learning. Deep Learning was inspired by the structure and function of the brain, specifically the interconnecting of many neurons. Artificial Neural Networks (ANNs) are algorithms that are based on the biological structure of the brain. In ANNs, there are ‘neurons’ which have discrete layers and connections to other “neurons”. Each layer picks out a specific feature to learn. It’s this layering that gives deep learning its name, depth is created by using multiple layers as opposed to a single layer.

NITI Aayog has further characterised AI as:

a) Weak AI vs. Strong AI: Weak AI describes “simulated” thinking. That is, a system which appears to behave intelligently, but doesn’t have any kind of consciousness about what it’s doing. For example, a chatbot might appear to hold a natural conversation, but it has no sense of who it is or why it’s talking to you. Strong AI describes “actual” thinking. That is, behaving intelligently, thinking as human does, with a conscious, subjective mind. For example, when two humans converse, they most likely know exactly who they are, what they’re doing, and why.

b) Narrow AI vs. General AI : Narrow AI describes an AI that is limited to a single task or a set number of tasks. For example, the capabilities of IBM’s Deep Blue, the chess playing computer that beat world champion Gary Kasparov in 1997, were limited to playing chess. It wouldn’t have been able to win a game of tic-tac-toe—or even know how to play. General AI describes an AI which can be used to complete a wide range of tasks in a wide range of environments. As such, it’s much closer to human intelligence.

c) Superintelligence : The term “superintelligence” is often used to refer to general and strong AI at the point at which it surpasses human intelligence, if it ever does.

AI for Economic Development

AI research in India is still in its infancy and requires large scale concerted and collaborative interventions. From an economic perspective, AI has the potential to drive growth through enabling:

a.   Intelligent automation through ability to automate complex physical world tasks that require adaptability & agility across industries

b.  Labour and capital augmentation: enabling humans to focus on parts of their role that add the most value, complementing human capabilities and improving capital efficiency

c.   Innovation diffusion through propelling innovations as it diffuses through the economy. NITI Aayog has decided to focus on five sectors that are envisioned to benefit the most from AI in solving societal needs:

      a.      Healthcare: increased access and affordability of quality healthcare

      b.     Agriculture: enhanced farmers’ income, increased farm productivity & reduction of wastage

      c.      Education: improved access & quality of education

d.  Smart Cities and Infrastructure: efficient & connectivity for the burgeoning urban population,

e.   Smart Mobility & Transportation: smarter & safer modes of transportation and better traffic & congestion problems

In addition to this, NITI Aayog has asserted that AI can have the potential to provide large incremental value to:

Healthcare : 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.

Smart Mobility, including Transports and Logistics : 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.

Retail : The retail sector has been one of the early adopters of AI solutions, with applications such as improving user experience by providing personalised suggestions, preference-based browsing and image-based product search. Other use cases include customer demand anticipation, improved inventory management, and efficient delivery management.

Manufacturing : Manufacturing industry is expected to be one of the biggest beneficiaries of AI-based solutions, thus enabling ‘Factory of the Future’ through flexible and adaptable technical systems to automate processes and machinery to respond to unfamiliar or unexpected situations by making smart decisions. Impact areas include engineering (AI for R&D efforts), supply chain management (demand forecasting), production (AI can achieve cost reduction and increase efficiency), maintenance (predictive maintenance and increased asset utilisation), quality assurance (e.g. vision systems with machine learning algorithms to identify defects and deviations in product features), and in-plant logistics and warehousing.

Energy : Potential use cases in the energy sector include energy system modelling and forecasting to decrease unpredictability and increase efficiency in power balancing and usage. In renewable energy systems, AI can enable storage of energy through intelligent grids enabled by smart meters, and also improve the reliability and affordability of photovoltaic energy. Similar to the manufacturing sector, AI may also be deployed for predictive maintenance of grid infrastructure.

Smart Cities : 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.

Education and Skilling : AI can potentially solve 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.

AI Implementation in India

NITI Aayog has listed several barriers to AI Adoption in India, which are:

a.   Lack of broad-based expertise in research and application of AI

b.  Absence of enabling data ecosystems—access to intelligent data

c.   High resource cost and low awareness for adoption of AI

d.  Privacy and security, including a lack of formal regulations around anonymisation of data

e.   Absence of collaborative approach to adoption and application of AI

f.   Unattractive Intellectual Property regime to incentivise research and adoption of AI

To overcome these barriers, NITI Aayog has intended for an umbrella organisation responsible for providing direction to research efforts through analysis of socio-economic indicators, studying global advancements, and encouraging international collaboration. NITI Aayog has proposed a two-tiered structure to address India’s AI research aspirations which would complement the intended umbrella organisation, which are:

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

2.  International Centres 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 ICTAI.

There are barriers to AI development and deployment, which the NITI Aayog has envisioned can effectively be addressed by adopting the marketplace model. A formal marketplace could be created focusing on data collection and aggregation, data annotation and deployable models, for which there could be a common platform called the National AI Marketplace (NAIM).

AI in the Aid of Agriculture & Farmers

AI has the potential to address the various challenges in Indian agriculture through prospective initiatives such as:

Soil health monitoring and restoration : Image recognition and deep learning models have enabled distributed soil health monitoring without the need of laboratory testing infrastructure. AI solutions integrated with data signals from remote satellites, as well as local image capture in the farm, have made it possible for farmers to take immediate actions to restore soil health.

Crop health monitoring and providing real time action advisories to farmers: The Indian agriculture sector is vulnerable to climate change due to being rain dependent. Varying weather patterns such as increase in temperature, changes in precipitation levels, and ground water density, can affect farmers especially in the rainfed areas of the country. AI can be used to predict advisories for sowing, pest control, input control can help in ensuring increased income and providing stability for the agricultural community.

Increasing efficiency of farm mechanisation : Image classification tools combined with remote and local sensed data can bring a revolutionary change in utilisation and efficiency of farm machinery, in areas of weed removal, early disease identification, produce harvesting and grading. Horticultural practices require a lot of monitoring at all levels of plant growth and AI tools provide round the clock monitoring of these high value products.

Increasing the share of price realisation to producers : Current low levels of price realisation to farmers are primarily due to ineffective price discovery and dissemination mechanisms, supply chain intermediary inefficiency and local regulations. Predictive analytics using AI tools can bring more accurate supply and demand information to farmers, thus reducing information asymmetry between farmers and intermediaries. As commodity prices are interlinked globally, big data analysis becomes imperative. Data from e-NAM, Agricultural Census (with data on over 138 million operational holdings), AGMARKET and over 110 million Soil Health Samples provide the volumes required for any predictive modelling.

AI can aid in Precision Farming wherein a crop yield prediction model using AI to provide real time advisory to farmers. 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 and soil moisture/temperature, crop phenology etc. to give accurate prescriptions to farmers.  An 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 for National Security

AI is essentially a dual use technology whereby it can provide technology-driven economic growth and also has the potential to provide military superiority, and recently Ministry of Defence formed the AI Task Force under the chairmanship of C. Chandrasekharan. AI has potential in for ensuring Security for India in:

Weapon Systems : Developing lethal autonomous weapon systems for air, ground and underwater defence requirements, autonomous systems for ships, drones, machine guns, etc.

War Games and Training : Simulated War Games and Training-based upon training the forces in a simulated environment, conducting pilots projects for simulated air combat mission, conducting mass personalised training & performance evaluation of personnel and simulated military equipments for practice.

Unmanned Surveillance : AI can be leveraged for unmanned surveillance for collecting video, audio and sensory data in real time, scouting battlefield and conflict zones, strengthening perimeter defence and border and maritime patrol, canvassing harsh terrains and under harsh conditions and key installations & harbour protection.

Cyber Security : It can be used for cyber security to monitor internet traffic in real time, act real-time on cumulative intelligence, detect malware & prevent darknets and automate cyber offence against targets & adversary networks.

Intelligence and Reconnaissance : AI can be crucial for intelligence gathering and reconnaissance initiatives such as gathering satellite imagery, movement tracking, object and pattern recognition and analysing unstructured data from sensor data, radar data, video, audio & satellite imagery.

In addition, AI can be used to establish Indian tactical deterrent in the South Asian region, to mitigate catastrophic risk, to visualise potential transformative weaponry of future, to facilitate in keeping a check on non-state actors and to bolster cyber defence.

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