{"id":530,"date":"2020-09-17T10:19:50","date_gmt":"2020-09-17T10:19:50","guid":{"rendered":"https:\/\/www.competitionreview.in\/blogs\/?p=530"},"modified":"2020-09-17T10:19:50","modified_gmt":"2020-09-17T10:19:50","slug":"national-strategy-for-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/www.competitionreview.in\/blogs\/2020\/09\/17\/national-strategy-for-artificial-intelligence\/","title":{"rendered":"NATIONAL STRATEGY FOR ARTIFICIAL INTELLIGENCE"},"content":{"rendered":"\n

Prof. V.P. Gupta<\/strong>,Director, Rau\u2019s IAS Study Circle, New Delhi \u2013 Jaipur \u2013 Bengaluru<\/strong><\/em><\/h4>\n\n\n\n

Recently, NITI Aayog released a discussion paper titled \u2018National\nStrategy on Artificial Intelligence\u2019. NITI Aayog report is premised on the\nproposition that India, given its strengths and characteristics, has the\npotential to position itself among leaders on the global AI map wherein India\ncan leverage the transformative technologies to ensure social and inclusive\ngrowth. In addition, India can also strive to replicate these solutions in\nother similarly placed developing countries. <\/p>\n\n\n\n

Let us understand the aspects of Artificial Intelligence from the\nperspective of UPSC Main Examination within the section of GS-III, in\nparticular:<\/p>\n\n\n\n

\u2022   Technology and its\napplications<\/p>\n\n\n\n

\u2022   Economic development<\/p>\n\n\n\n

\u2022   e-technology in the aid of\nfarmers<\/p>\n\n\n\n

\u2022   Security<\/p>\n\n\n\n

Understanding AI Technology <\/strong><\/p>\n\n\n\n

AI refers to the ability of machines to perform cognitive tasks like\nthinking, perceiving, learning, problem solving and decision making. Initially\nconceived as a technology that could mimic human intelligence, AI is a\nconstellation of technologies that enable machines to act with higher levels of\nintelligence and emulate the human capabilities of sense, comprehend and act.<\/p>\n\n\n\n

Machine Learning<\/strong> means the ability\nto learn without being explicitly programmed. Machine Learning involves the use\nof algorithms to parse data and learn from it, and making a determination or\nprediction as a result. Instead of hand coding software libraries with well-defined\nspecific instructions for a particular task, the machine gets \u201ctrained\u201d using\nlarge amounts of data and algorithms, and in turn gains the capability to\nperform specific tasks.<\/p>\n\n\n\n

Deep Learning<\/strong> is a technique for\nimplementing Machine Learning. Deep Learning was inspired by the structure and\nfunction of the brain, specifically the interconnecting of many neurons.\nArtificial Neural Networks (ANNs) are algorithms that are based on the\nbiological structure of the brain. In ANNs, there are \u2018neurons\u2019 which have discrete\nlayers and connections to other \u201cneurons\u201d. Each layer picks out a specific\nfeature to learn. It\u2019s this layering that gives deep learning its name, depth\nis created by using multiple layers as opposed to a single layer.<\/p>\n\n\n\n

NITI Aayog has further characterised AI as: <\/strong><\/p>\n\n\n\n

a) Weak AI vs. Strong AI:<\/strong>\nWeak AI describes \u201csimulated\u201d thinking. That is, a system which appears to\nbehave intelligently, but doesn\u2019t have any kind of consciousness about what\nit\u2019s doing. For example, a chatbot might appear to hold a natural conversation,\nbut it has no sense of who it is or why it\u2019s talking to you. Strong AI\ndescribes \u201cactual\u201d thinking. That is, behaving intelligently, thinking as human\ndoes, with a conscious, subjective mind. For example, when two humans converse,\nthey most likely know exactly who they are, what they\u2019re doing, and why.<\/p>\n\n\n\n

b) Narrow AI vs. General AI :<\/strong>\nNarrow AI describes an AI that is limited to a single task or a set number of\ntasks. For example, the capabilities of IBM\u2019s Deep Blue, the chess playing\ncomputer that beat world champion Gary Kasparov in 1997, were limited to\nplaying chess. It wouldn\u2019t have been able to win a game of tic-tac-toe\u2014or even\nknow how to play. General AI describes an AI which can be used to complete a\nwide range of tasks in a wide range of environments. As such, it\u2019s much closer\nto human intelligence.<\/p>\n\n\n\n

c) Superintelligence :<\/strong> The term\n\u201csuperintelligence\u201d is often used to refer to general and strong AI at the\npoint at which it surpasses human intelligence, if it ever does.<\/p>\n\n\n\n

AI for Economic Development <\/strong><\/p>\n\n\n\n

AI research in India is still in its infancy and requires large scale\nconcerted and collaborative interventions. From an economic perspective, AI has\nthe potential to drive growth through enabling: <\/p>\n\n\n\n

a.<\/strong>   Intelligent automation through ability to automate complex\nphysical world tasks that require adaptability & agility across industries<\/p>\n\n\n\n

b.<\/strong>   Labour and capital augmentation: enabling humans to focus on parts\nof their role that add the most value, complementing human capabilities and improving\ncapital efficiency<\/p>\n\n\n\n

c.<\/strong>   Innovation diffusion through propelling innovations as it diffuses\nthrough the economy. NITI Aayog has decided to focus on five sectors that are\nenvisioned to benefit the most from AI in solving societal needs:<\/p>\n\n\n\n

      a.      <\/strong>Healthcare: increased access\nand affordability of quality healthcare<\/p>\n\n\n\n

      b.      <\/strong>Agriculture: enhanced farmers\u2019\nincome, increased farm productivity & reduction of wastage<\/p>\n\n\n\n

      c.      <\/strong>Education: improved access\n& quality of education<\/p>\n\n\n\n

d.   <\/strong>Smart Cities and Infrastructure: efficient & connectivity for the\nburgeoning urban population,<\/p>\n\n\n\n

e.   <\/strong>Smart Mobility & Transportation: smarter & safer modes of\ntransportation and better traffic & congestion problems<\/p>\n\n\n\n

In addition to this, NITI Aayog has asserted\nthat AI can have the potential to provide large incremental value to:<\/p>\n\n\n\n

Healthcare :<\/strong> Application of AI\nin healthcare can help address issues of high barriers to access to healthcare\nfacilities, particularly in rural areas that suffer from poor connectivity and\nlimited supply of healthcare professionals. This can be achieved through\nimplementation of use cases such as AI-driven diagnostics, personalised\ntreatment, early identification of potential pandemics, and imaging\ndiagnostics, among others.<\/p>\n\n\n\n

Smart Mobility, including Transports and Logistics :<\/strong> Potential use cases in this domain include autonomous fleets for\nride sharing, semi-autonomous features such as driver assist, and predictive\nengine monitoring and maintenance. Other areas that AI can impact include\nautonomous trucking and delivery, and improved traffic management.<\/p>\n\n\n\n

Retail :<\/strong> The retail sector\nhas been one of the early adopters of AI solutions, with applications such as\nimproving user experience by providing personalised suggestions,\npreference-based browsing and image-based product search. Other use cases\ninclude customer demand anticipation, improved inventory management, and\nefficient delivery management.<\/p>\n\n\n\n

Manufacturing <\/strong>: Manufacturing\nindustry is expected to be one of the biggest beneficiaries of AI-based\nsolutions, thus enabling \u2018Factory of the Future\u2019 through flexible and adaptable\ntechnical systems to automate processes and machinery to respond to unfamiliar\nor unexpected situations by making smart decisions. Impact areas include\nengineering (AI for R&D efforts), supply chain management (demand\nforecasting), production (AI can achieve cost reduction and increase\nefficiency), maintenance (predictive maintenance and increased asset\nutilisation), quality assurance (e.g. vision systems with machine learning\nalgorithms to identify defects and deviations in product features), and\nin-plant logistics and warehousing.<\/p>\n\n\n\n

Energy : <\/strong>Potential\nuse cases in the energy sector include energy system modelling and forecasting\nto decrease unpredictability and increase efficiency in power balancing and\nusage. In renewable energy systems, AI can enable storage of energy through\nintelligent grids enabled by smart meters, and also improve the reliability and\naffordability of photovoltaic energy. Similar to the manufacturing sector, AI\nmay also be deployed for predictive maintenance of grid infrastructure.<\/p>\n\n\n\n

Smart Cities : <\/strong>Integration\nof AI in newly developed smart cities and infrastructure could also help meet\nthe demands of a rapidly urbanising population and providing them with enhanced\nquality of life. Potential use cases include traffic control to reduce\ncongestion and enhanced security through improved crowd management.<\/p>\n\n\n\n

Education and Skilling :<\/strong> AI can potentially solve quality and access issues observed in the\nIndian education sector. Potential use cases include augmenting and enhancing\nthe learning experience through personalised learning, automating and\nexpediting administrative tasks, and predicting the need for student\nintervention to reduce dropouts or recommend vocational training.<\/p>\n\n\n\n

AI Implementation in India<\/strong><\/p>\n\n\n\n

NITI Aayog has listed several barriers to AI\nAdoption in India, which are:<\/p>\n\n\n\n

a.   <\/strong>Lack of broad-based expertise in research and application of AI<\/p>\n\n\n\n

b.   <\/strong>Absence of enabling data ecosystems\u2014access to intelligent data<\/p>\n\n\n\n

c.   <\/strong>High resource cost and low awareness for adoption of AI<\/p>\n\n\n\n

d.   <\/strong>Privacy and security, including a lack of formal regulations around\nanonymisation of data<\/p>\n\n\n\n

e.   <\/strong>Absence of collaborative approach to adoption and application of AI <\/p>\n\n\n\n

f.    <\/strong>Unattractive Intellectual Property regime to incentivise research and\nadoption of AI<\/p>\n\n\n\n

To overcome these barriers, NITI Aayog has\nintended for an umbrella organisation responsible for providing direction to\nresearch efforts through analysis of socio-economic indicators, studying global\nadvancements, and encouraging international collaboration. NITI Aayog has\nproposed a two-tiered structure to address India\u2019s AI research aspirations\nwhich would complement the intended umbrella organisation, which are:<\/p>\n\n\n\n

1.  Centre of Research Excellence (CORE)<\/strong>\nfocused on developing better understanding of existing core research and\npushing technology frontiers through creation of new knowledge.<\/p>\n\n\n\n

2.  International Centres of Transformational AI (ICTAI)<\/strong> with a mandate of developing and deploying application-based\nresearch. Private sector collaboration is envisioned to be a key aspect of\nICTAI.<\/p>\n\n\n\n

There are barriers to AI development and\ndeployment, which the NITI Aayog has envisioned can effectively be addressed by\nadopting the marketplace model. A formal marketplace could be created focusing\non data collection and aggregation, data annotation and deployable models, for\nwhich there could be a common platform called the National AI Marketplace\n(NAIM).<\/p>\n\n\n\n

AI in the Aid of Agriculture & Farmers<\/strong><\/p>\n\n\n\n

AI has the potential to address the various challenges in Indian\nagriculture through prospective initiatives such as: <\/p>\n\n\n\n

Soil health monitoring and restoration : <\/strong>Image\nrecognition and deep learning models have enabled distributed soil health\nmonitoring without the need of laboratory testing infrastructure. AI solutions\nintegrated with data signals from remote satellites, as well as local image\ncapture in the farm, have made it possible for farmers to take immediate\nactions to restore soil health.<\/p>\n\n\n\n

Crop health monitoring and providing real time action advisories to\nfarmers:<\/strong> The Indian agriculture sector is vulnerable to\nclimate change due to being rain dependent. Varying weather patterns such as\nincrease in temperature, changes in precipitation levels, and ground water\ndensity, can affect farmers especially in the rainfed areas of the country. AI\ncan be used to predict advisories for sowing, pest control, input control can\nhelp in ensuring increased income and providing stability for the agricultural\ncommunity. <\/p>\n\n\n\n

Increasing efficiency of farm mechanisation : <\/strong>Image classification tools combined with remote and local sensed data\ncan bring a revolutionary change in utilisation and efficiency of farm\nmachinery, in areas of weed removal, early disease identification, produce\nharvesting and grading. Horticultural practices require a lot of monitoring at\nall levels of plant growth and AI tools provide round the clock monitoring of\nthese high value products.<\/p>\n\n\n\n

Increasing the share of price realisation to producers : <\/strong>Current low levels of price realisation to farmers are primarily due\nto ineffective price discovery and dissemination mechanisms, supply chain\nintermediary inefficiency and local regulations. Predictive analytics using AI\ntools can bring more accurate supply and demand information to farmers, thus\nreducing information asymmetry between farmers and intermediaries. As commodity\nprices are interlinked globally, big data analysis becomes imperative. Data\nfrom e-NAM, Agricultural Census (with data on over 138 million operational\nholdings), AGMARKET and over 110 million Soil Health Samples provide the\nvolumes required for any predictive modelling.<\/p>\n\n\n\n

AI can aid in Precision Farming<\/strong>\nwherein a crop yield prediction model using AI to provide real time advisory to\nfarmers. AI model for predictive insights to improve crop productivity, soil\nyield, control agricultural inputs and early warning on pest\/disease outbreak\nwill use data from remote sensing (ISRO), soil health cards, IMD\u2019s weather\nprediction and soil moisture\/temperature, crop phenology etc. to give accurate\nprescriptions to farmers.  An integrated\ncomputer vision and machine learning technology that enables farmers to reduce\nthe use of herbicides by spraying only where weeds are present, optimising the\nuse of inputs in farming\u2014a key objective of precision agriculture. <\/p>\n\n\n\n

AI for National Security <\/strong><\/p>\n\n\n\n

AI is essentially a dual use technology whereby it can provide\ntechnology-driven economic growth and also has the potential to provide\nmilitary superiority, and recently Ministry of Defence formed the AI Task Force\nunder the chairmanship of C. Chandrasekharan. AI has potential in for ensuring\nSecurity for India in:<\/p>\n\n\n\n

Weapon Systems :<\/strong> Developing lethal\nautonomous weapon systems for air, ground and underwater defence requirements,\nautonomous systems for ships, drones, machine guns, etc.<\/p>\n\n\n\n

War Games and Training <\/strong>: Simulated\nWar Games and Training-based upon training the forces in a simulated\nenvironment, conducting pilots projects for simulated air combat mission,\nconducting mass personalised training & performance evaluation of personnel\nand simulated military equipments for practice.<\/p>\n\n\n\n

Unmanned Surveillance : <\/strong>AI can be\nleveraged for unmanned surveillance for collecting video, audio and sensory\ndata in real time, scouting battlefield and conflict zones, strengthening\nperimeter defence and border and maritime patrol, canvassing harsh terrains and\nunder harsh conditions and key installations & harbour protection.<\/p>\n\n\n\n

Cyber Security : <\/strong>It can be used for\ncyber security to monitor internet traffic in real time, act real-time on\ncumulative intelligence, detect malware & prevent darknets and automate\ncyber offence against targets & adversary networks.<\/p>\n\n\n\n

Intelligence and Reconnaissance : <\/strong>AI\ncan be crucial for intelligence gathering and reconnaissance initiatives such\nas gathering satellite imagery, movement tracking, object and pattern\nrecognition and analysing unstructured data from sensor data, radar data,\nvideo, audio & satellite imagery.<\/p>\n\n\n\n

In addition, AI can be used to establish Indian tactical deterrent in\nthe South Asian region, to mitigate catastrophic risk, to visualise potential\ntransformative weaponry of future, to facilitate in keeping a check on\nnon-state actors and to bolster cyber defence.<\/p>\n","protected":false},"excerpt":{"rendered":"

Prof. V.P. Gupta,Director, Rau\u2019s IAS Study Circle, New Delhi \u2013 Jaipur \u2013 Bengaluru Recently, NITI Aayog released a discussion paper titled \u2018National Strategy on Artificial Intelligence\u2019. 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 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[3],"tags":[],"_links":{"self":[{"href":"https:\/\/www.competitionreview.in\/blogs\/wp-json\/wp\/v2\/posts\/530"}],"collection":[{"href":"https:\/\/www.competitionreview.in\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.competitionreview.in\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.competitionreview.in\/blogs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.competitionreview.in\/blogs\/wp-json\/wp\/v2\/comments?post=530"}],"version-history":[{"count":1,"href":"https:\/\/www.competitionreview.in\/blogs\/wp-json\/wp\/v2\/posts\/530\/revisions"}],"predecessor-version":[{"id":531,"href":"https:\/\/www.competitionreview.in\/blogs\/wp-json\/wp\/v2\/posts\/530\/revisions\/531"}],"wp:attachment":[{"href":"https:\/\/www.competitionreview.in\/blogs\/wp-json\/wp\/v2\/media?parent=530"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.competitionreview.in\/blogs\/wp-json\/wp\/v2\/categories?post=530"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.competitionreview.in\/blogs\/wp-json\/wp\/v2\/tags?post=530"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}