The government of India has allocated $480 million for developing fifth generation technologies like artificial intelligence (AI), machine learning (ML) and Internet of things (IoT). Accenture claims that continued investments in AI could add $957 billion to the Indian economy by 2035. But AI just for enhancement, rather than enablement, limits its potential. Though a late entrant to the global AI market, India can advance in the sector considerably if it enables various aspects of a citizen’s life with the technology. They are:
The Ministry of Defence’s Centre for Artificial Intelligence and Robotics has been developing systems based on AI, robotics, networking, communication intelligence and secrecy. Today, intelligence is the most sought-after commodity for our security agencies (responsible for internal and external security) to be agile and be effective on-ground.
A greater push can give birth to: autonomous surveillance, complete AI-based view of persons-of-interest and their connection networks, leading edge combat systems, cyber-attack mitigation and counterattack systems, and multi-sensor data-fusion based decision-making systems. AI can also enhance protective measures for airports, power plants and infrastructure vulnerable to man-made disruptions, by detecting behavioural anomalies with distributed sensors and pattern recognition.
With limited preventive and protective measures for natural and man-made disasters, AI has the potential to aid disaster management in India. Using data from drone imaging and satellite feeds, AI image processors and recognition systems can assess damage, predict dangers, and give topographical insights to speed-up rescue processes.
Integrated with communication channels, AI can optimise mobile networks, allocate bandwidth, and process calls for emergency response post filtering redundant calls. It can interact with callers, instantly translating languages and analyse urgency, and can also analyse unstructured pictures and videos posted to find missing people on social media.
Around 30% of India’s groundwater assets have reached critical levels due to overexploitation. Confronting the crisis, Kerala water authorities are making use of data analytics for better water management and distribution; they are able to keep a close eye on the information received from the sensors and meters installed across the state. With real-time information tracking the usage and quality of water, the utility has not only improved the state of the water supply in the region but has also brought efficiency leading to lesser wastage and higher revenue collection. AI and ML could help such systems evolve from just repairs to a predictive maintenance model, that cuts waste, improves treatment systems, and keeps infrastructure healthy. It can also map data from flow and pressure sensors to determine optimal flow, leak patters and connection failures.
India spends about $330 million on energy imports daily and will need three times its current supply for 8% economic growth by 2031-32. Renewable energy is the only way out, but intermittent supply is a roadblock. Systems are needed that can balance demand and supply swings to meet targets.
Using AI, grids can develop predictive technology algorithms and computation models to reduce dependency, availability, costs and consumption. It can help decide energy circulation for balancing power grids and efficiently handle the rapid increase of grid control points. It can also optimise the resource allocation by predicting usage and suggest conservation techniques to consumers for better living conditions for next generations. AI can also be used for improving efficiency and maintaining assets and also detecting failure before it actually takes place.
Large audit firms are using AI tools to perform baseline tasks such as data review, freeing professionals for applying their judgement on the extracted insights. Deloitte UK uses an AI tool that analyses complete sets of data, eliminating risks of sampling and human error, and learns from users’ tax decisions.
In India, AI-based digital tax assistants could help authorities improve personalised services and provide users with tailored advice. Detecting frauds across tax payment, tax refund can very well be automated with the help of AI. Fraud evolves rapidly with ever increasing sophistication in taxation; manually curated rules, along with human inspection of data and decision-making, can delay the response to fraud. Also, the negative impact of false positives is also very high.
AI can come to rescue of this and help tax officials detect frauds, with very low false positives. The advances in AI natural language processing can support voice conversations, enabling automated bots to understand and pay customers’ taxes, and it can also help automatic case preparation for tax officials before they head for any tax appeal case hearing.
India is the fourth worst country in curbing pollution. Optimising solutions to identify and combat pollutants is the need of the hour. Civic agencies need to put out a widespread and integrated network of air quality monitoring sensors across the cities to begin real-time monitoring of air quality. This should be accompanied by a policy change to ensure that civic agencies install pollution meters across factories, commercial establishments (such as malls and office complexes), dump-yards and even locations such as schools and hospitals, to begin monitoring of emissions and pollutant levels.
Air-quality levels should be monitored across a wide spectrum of emissions so that civic agencies and citizens get information on spikes and dips in specific pollutants, allowing them to pin-point the cause to a specific source. Pollution control certificates, whether for automobiles or factories and commercial establishments, should be made real-time instead of being periodic (as it is now). The data should also include other sources such as weather monitoring stations and satellites, traffic systems, industrial data, farm data, and even social media.
By combining all this disparate data, highly accurate models can be created to predict pollution trends in advance, allowing civic agencies to make relevant predictions and changes to prevent spikes and keep pollution levels in check. It can help estimate and control smoke, effluent and solid waste being released into air, soil and water.
ML today can read and memorise all the available legal literature. It’ll soon be able to relate and recall relevant prosecutions and give an objective analysis in seconds. Cyril Amarchand Mangaldas, has become one of the first law firms in India to use ML for automating due diligence and transactional practises.
AI systems are being used in complex legal matters of US Federal Patent Law, judgement prediction and risk assessment. With natural language processing (NLP), ML can understand unstructured legal data and help settle cases objectively in India. AI can fast-track court administration and provide low-cost, quick access to justice benefiting millions of citizens.
AI can impact and simultaneously enhance almost every sector in India. However, it is important for these sectors to not only adopt AI technologies for its current state of operations, but to change the way it does business.
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