Authored by Sameer Dhanrajani, Chief Strategy Officer, Fractal Analytics
In this blog, we deeply examine dynamics in Indian artificial intelligence (AI) ecosystems and its aim to potentially become a major technology disrupter by looking at some of the critical large, complex and unsolved problems. AI potential is looked through multiple lenses across sectors, particularly its implications and application in agricultural landscape followed by smart city developments. In this second machine age, economies will find themselves competing with advanced technologies across nations that augment or even replace human workers with sophisticated algorithms.
India must stay laser focused to aggressively build the AI block at a rapid pace and address its huge challenges in areas like health care, banking, sanitation, agriculture, education and governance. AI outcomes realized in the above-mentioned segments gives India the opportunity to leapfrog some of these issues via cheap diagnostic methods, automatic processing of applications, or learning and teaching aids.
India and Artificial intelligence are poised at a vantage position to unleash the next wave of digital disruption. Our focus is on some of the key AI technology systems: robotics, computer vision, language, virtual agents, and machine learning (including deep learning) that underpins many recent advances made in the agricultural sector, smart cities and overall governance.
Agriculture leveraged by AI
“AI-Enabled Farms” are at the crux of the changing paradigm of Indian agriculture. Productivity and Efficiency gains through technology are giving agriculture a makeover. The key solution driver lies in making use of cognitive technologies that help understand, learn, interact, reason and increase efficiency. Some examples of the game changers are analyzed below:
AI-driven farming: Analyze and correlate information about, weather, types of seeds, soil quality. Based on historical data farmers will make more informed decisions. Image classification tools combined with data capture through remote sensors can bring a revolutionary change in predictive maintenance space of farm machinery, in areas of weed removal, disease identification, and produce realization (harvesting and grading). A lot of monitoring across all levels is needed for plant growth in horticultural practices. AI tools provide effective round-the-clock monitoring for such practices.
Chatbots: In the current scenario, AI driven chatbots are used across high-tech, media, retail sectors, etc. Test run is being done in agriculture to leverage this technology by assisting farmers on answers and recommendations on specific problems.
AI Enabled soil health card and restoration: Image recognition and deep learning models can play a pivotal role to enable distributed soil health monitoring for active decision making. AI solutions integrated with data signals through satellites and image captures in the farm, have gained mileage for farmers to initiate immediate actions to restore soil health.
Pattern based advisory and guidance: Agricultural landscape in India is vulnerable to different factors such as rain, seasons, changes in precipitation levels, ground water density etc. These factors have a direct effect on farmers. AI can be used as a disruptive force to predict such advisories for sowing, pest control, through remote sensing at a farm level. Using remote sensed data, high resolution weather data coupled with AI platforms/technologies will enable to monitor crops holistically for effective insights and active decision making.
AI enabled agricultural economics: Dissemination mechanisms and disparate prices have a direct impact in agricultural produce due to inefficient supply chain. Application of predictive analytics using AI tools will increase the accuracy rate and most importantly enable effective economics (balance the supply and demand information). This reduces information asymmetry between farmers and intermediaries. As commodity prices are interlinked globally, big data analysis becomes imperative.
In summary, digital and AI wave is helping solve pressing issues across the agriculture value chain. Today’s vast digital ecosystem coupled with partnership among players such as Original Equipment Manufacturers (OEM), AI enablers, cloud providers, open source platforms, startups, etc. have the potential to impact productivity and efficiency across the agricultural value chain.
AI-Enabled Smart cities
India has seen a steady surge in urbanisation during the last two decades. However, the key challenges exist around congestion, pollution, crime rates, poor living standard that put intense pressure on administrative needs in the existing cities. To effectively overcome these challenges, the Government of India has embarked on an ambitious initiative to establish Smart Cities. This initiative is particularly aimed at driving economic growth and improve the quality of life, by harnessing IT solutions.
As part of the Smart Cities Mission, 99 cities have been selected with expected investment of INR 2.04 lakh crores1. The strategic components of these Smart Cities include
- City improvement (retrofitting),
- City renewal (redevelopment) and
- City extension (greenfield development)
Smart cities are acquiescent to application of AI through the large amount of data they can create. This in-turn can be transformed into predictive intelligence making the city an ‘intelligent entity’.
Some of the prominent use cases of AI that can augment features of a smart city include:
Water and Power
A cutting-edge advantage in deploying AI will be to streamline the usage of power and water at homes. Smart cities will be enabling smart grids to better manage power use. AI will also be applied to water metering both at a macro and micro levels to curb excess water and find leaks.
Public Safety:
Use of AI to monitor public facilities and control associated systems such as lighting, park maintenance and operational conditions would optimize cost and most importantly improving safety.
Crowd management in recent times have proven very effective through the application of AI. “Kumbh Mela Experiment” in recent times was aimed at predicting crowd behavior and possibility of a stampede. In summary, Big Data and AI solutions would enable an effective advance prediction and response management.
AI enabled Command Centers:
AI technology could reshape safety through smart command centres with sophisticated surveillance systems. Such deployments would enable to track people’s movement, potential crime incidents, social media and general security of the residents. In the city of Surat, the crime rate has declined by 27%2 after the implementation of AI powered safety systems.
Governance through AI
Technology has been a major lever in driving various initiatives in Tax and public finance space. This segment has seen the rollout of various e-governance initiatives, such as National e-Governance Plan (NEGP), Commercial Taxes program, Treasury Computerization Project to the recent rollout of the ambitious Goods and Services Tax (GST) platform.
The GST platform rolled out by the Goods & Service Tax Network (GSTN) is undoubtedly the largest current initiative in the sector in India. Analytics adoption is spanning across tax administrations in India by the Income Tax Department.
Creation of Electronic Intelligence Units (EIU) with analytics and cyber-forensic capabilities is seen as a major boost. These technologies are shaping the reality to influence a whole range of taxes, including VAT/GST, customs duties, environmental levies and excise taxes that are closely connected with business transactions.
A user-friendly AI ecosystem has the potential for creating value, but there are times when the desire to adopt such solutions across segments become road blocks by long implementation time lines, limitations in the budgeting process, reliance on legacy platforms, and the overall complexity of the government's technology environment.
To overcome the above challenges of introducing and building an AI enabled environment, government need to enable incremental adoption methods and technologies. The critical part is ensuring that the transition allows them to overcome the change management/ behavioural issues. The secret sauce of successful deployment is to ensure a low barrier to entry that are easy to use and integrate, can parse both structured and unstructured data, and seamlessly fit into the existing technology architecture landscape, making an effective and efficient government enabled AI enterprise environment.
1. Based on news coverage titled ‘Rs 2.04 lakh cr projects sanctioned for smart cities: Centre’ in Business Standard, 24 May 2018
2. Based on the discussion paper titles ‘National Strategy from Artificial Intelligence’ by NITI Aayog, June 2018
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