*The retail industry has been one of worst-hit industries by the global pandemic.

*India is going to take a leadership role in AI for the world.

*There has never been a better time for AI ecosystem players and especially startups offering AI solutions.

The McKinsey Global Institute has recently studied economic statistics from the United Nations, the World Bank, and the World Economic Forum and estimated that by 2030, AI can add 16 percent—or around $13 trillion—to the global economy and AI will boost global GDP by $15.7 billion. Since 2000, AI venture investments have risen six times and McKinsey also estimates that by 2030, at least 70 percent of businesses are likely to have implemented at least one type of AI technology.

In India comparatively, Artificial Intelligence is a growing industry sector, as a report by Computer Vision Market reveals and stands at $6.4 billion as of 2030.  36.2 percent of this demand is coming from MNCs, GICs, and Captive firms with a market valuation of $2.3 billion. The future will see a vast number of other industries such as healthcare, high-tech manufacturing, and semiconductor companies adopting AI making it one of the most attractive and fastest-growing technology sectors over the next decade.

The retail industry has been one of worst-hit industries by the global pandemic and after an initial knee-jerk pause during the lockdowns, we see a sudden spike in retail brands (across categories of products) globally wanting to adopt and embrace digital transformation initiatives. The age-old model of customer service via call-centre’s has been disrupted permanently by Covid-19 and moving to AI solutions that can help retail businesses manage both exponential rise in call volumes while maintaining very high service level quality is the new emerging opportunity. It is, therefore, not a surprise that the conversational AI market alone is expected to grow at 32 percent CAGR to $9.4 billion by 2024 according to market research company markets & markets.

The Indian Government is playing a significant role as well and recently joined a Multinational Alliance on Artificial Intelligence that includes big nations such as the United States, the UK and Australia. Also, India has launched a Nationwide Artificial Intelligence Plan to bring the best of ideas and expertise from all fields and industries together making it one of the most happening and promising technology ecosystems to watch out for.

The National policymakers substantially expanded public AI funding through promises, such as increased R&D spending, the creation of industrial and construction funds by startups, network and technology investments, and public procurement relevant to AI. The government is also creating and fostering AI through numerous variations of public-private academies; the creation of technology parks and the connection between big companies and startups.

NASSCOM estimates that by 2022, 46% of the Indian workforce will be employed in completely new industries, which do not exist, or jobs that have drastically changed skill sets. NITI Ayog reports estimate that in India demand for AI and computer learners increased 60 percent in 2018. An Independent study has also stated that in 2020 India faced a demand-supply gap of 2,00,000d data analytics experts.

While global businesses have been at the forefront of adopting emerging technologies such as AI, blockchain, and IoT, Indian businesses have been traditionally late technology adopters. Owing to an in general risk-averse mindset culturally, only once a technology has been proven and tested at scale, Indian businesses are willing to adopt it and we see a similar trend even in AI and AI-based solutions.

However, the COVID-19 pandemic and subsequent lockdowns have reversed this trend completely and we see Indian businesses realize that adopting emerging technologies in today’s world is no longer an option that can be delayed any further. In many cases it can be the deciding factor between thriving, surviving, or perishing for the business. For example, at AskSid, where we offer a digital shopping assistant that help retail brands sell more, we have seen a drastic increase in demand in the last 6 months leading to a 3X-5X jump in our opportunity pipeline.

This increased adoption within businesses is being driven by the rapid usage and adoption of AI by consumers in their daily life. Alexa, Siri, Google as voice assistants are house-hold names today both in India and outside and the expectations to get served instantly in a personalized manner is considered totally normal by today’s consumers. Any business which fails to deliver on this high standard of customer experience which consumers demand carries the serious threat of becoming redundant very fast and therefore embracing digital transformation using emerging technologies such as AI is no longer an option for Indian businesses.To conclude the overall scenario, one thing that is evident in the global vs. Indian AI debate is that India is now no longer a laggard,

• India is going to take a leadership role in AI for the world. Indeed, through zealous technological advances and consistent R&D India can be the next artificial intelligence superpower. Indian firms and AI entrepreneurs must begin to invest more in research and to grow AI products and solutions from India.
• There has never been a better time for AI ecosystem players and especially startups offering AI solutions. There lie a huge market and humongous opportunity for budding entrepreneurs to tap into.

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Twitter and Facebook use algorithms to ease the burden of human moderators. But, how good are these algorithms?

“Freedom of expression is not absolute and it is subject to reasonable restrictions.”

MeitY, citing Article 19 (2)

Earlier this month, the Government of India reprimanded Twitter for allowing fake, unverified, anonymous and automated bot accounts to be operated on its platform. The Secretary of MeitY raised doubts about the platform’s commitment to transparency and healthy conversation on this platform. The way Twitter and Facebook handled the events leading upto the elections in the US and the aftermath, has served as a wake up call to the governments around the world. Heads of states like France and Germany openly opposed the “problematic” nature of these platforms. As the debate around privacy and freedom of speech grows stronger, individuals and nations are looking out for better, more transparent alternatives.

The differential treatment in practising censorship on these platforms has been exposed quite often. Recently, Twitter and the Indian Government had a brief stand-off during the farmers protests. In a strong worded statement, the government rebuked Twitter for showing differential treatment to what happened at the Red Fort in comparison to its action during the recent Capitol Hill protests. Twitter caved in. The platform banned few accounts under the guidance of MeitY. 

MeitY in a recent meeting with Twitter executives said that the social media company is free to formulate its own rules and guidelines like any other business entity does, but the  laws which are enacted by the Parliament of India “must be followed” irrespective of Twitter’s own rules and guidelines.

A private entity with power to silence the local elements in a foreign land is problematic in my ways. This is the reason why nations across the world root for their own spin-offs of these popular platforms. Indian app makers have responded whenever there is a radical push for digitisation: The demonetisation propelled Paytm, and the ban of Chinese apps birthed half a dozen startups. Koo is the latest entrant to the club: The Twitter alternative was launched 10 months ago and is the winner of the Aatmanirbhar App Challenge organised by the Indian government. The app raked in nearly a million users in the second week of this month. Parler is a good study in this regard. Launched in 2018, Parler was supposed to be the free speech alternative to Twitter. But the company’s lacklustre content moderation allowed radicals from all walks of life to disrupt conversations on the platform. Google and Apple disowned the app. AWS kicked them out of their servers and the app disappeared overnight.

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Researchers in Japan and The Netherlands have, for the first time, used machine learning techniques, in particular artificial neural networks (ANNs), to predict the Indian Ocean Dipole (IOD), a positive phase of which has affected weather and climate in India and Australia in a spectacular fashion so far in 2019-20.

Positive, negative phases

The IOD has both positive and negative phases, and signals large socio-economic impacts on many countries and hence predicting the IOD well in advance will benefit the affected societies, note authors JV Ratnam and Swadhin K Behera (Application Laboratory, Japan Agency for Marine-Earth Science and Technology, Yokohama) and HA Dijkstra (Institute for Marine and Atmospheric Research Utrecht, Utrecht University in The Netherlands) in a paper published by Nature.

Ocean temperatures

The IOD is a mode of climate variability observed in the Indian Ocean sea surface temperature anomalies with one pole in Sumatra (Indonesia) and the other near East Africa. Therefore, the IOD is represented by an index derived from the gradient between the western equatorial Indian Ocean and the south-eastern equatorial Indian Ocean. It starts sometime in May-June, peaks in September-October and ends in November (2019’s rather strong positive phase of the IOD lasted into early January of 2020).

In a positive IOD phase, the western part of the Indian Ocean (closer to East Africa where the monsoon winds turn as south-westerly winds towards India) warms up relative to the eastern basin, beefing up the incoming monsoon flows. These conditions are more or less reversed during a negative IOD phase.

Atmospheric teleconnections

The IOD is also known to affect the climates of other parts of the world, including Sri Lanka, the Maritime Continent (Indonesia, et al), Japan, East Africa and Europe through atmospheric teleconnections. The climate of Australia and the Maritime Continent also are affected by the cool (warm) SST anomalies over the South-East Indian Ocean region during the positive (negative) phase of the IOD.

The anomalously cool (warm) waters around Australia and the Maritime Continent during the positive (negative) phase of IOD reduce (enhance) rainfall over those countries. The IOD also has a remote effect on the climate of Japan through modification of the Pacific-Japan teleconnection and it is also known to affect the summers of Europe due to the atmospheric teleconnections as a response to the IOD.

Wetter India, dry and hot Australia

In recent years, it has been found that the spatial distribution of the summer (monsoon) rainfall over India is affected by IOD during its various phases. During the positive IOD phase, India experiences anomalously high rainfall along the latitude belts covering Central India and during the negative phase of the IOD, the rainfall is anomalously high along the longitudinal belt with the western part of the country receiving high rainfall.

The extended South-West monsoon (June-September) of 2019 in India had a lag effect on the Australian monsoon (delayed to this day), which is thought to have aided and abetted the devastating bush/forest fire in the island-continent. Owing to its large impacts, previous studies have addressed the predictability of the IOD using modern coupled climate models. Various forecasting centres try to predict IOD using the coupled climate models at seasonal time scales. Such dynamical models are promising but are dependent on large computational as well as human resources.

Machine learning to the fore

But in the instant case, researchers in Japan and The Netherlands tried to complement those efforts with a simpler model based on the machine learning technique of ANNs. ANNs are tools used in machine learning which mimic the functioning of neurons in the human brain. Similar to the human brain, the ANN also learns from past data and makes decisions for the future.

An ANN consists of input, output and hidden layers. ANNs have been used in many fields for classification and regression studies to model processes. The correlation analysis of the IOD index indicated that a single ANN model is not suitable for forecasting the IOD index for all the months from May to November. So, researchers developed ANN models for forecasting the IOD index for every month from May to November.

The results were compared with persistence forecasts and also IOD index forecasts derived from the ensemble mean sea surface temperature anomalies of seven models within the North American Multi-Model Ensemble (NMME), an experimental multi-model seasonal forecasting system consisting of coupled models from the US and Canada. The ANN and NMME model results were compared with persistence forecasts to check if the models have skill higher than just persistence of the IOD index of February-April to May-November.

Superior forecast skills

The IOD forecasts were generated for May to November from February-April conditions. The attributes for the ANNs were derived from sea surface temperature and conditions in the upper levels of the atmosphere using a correlation analysis for the period 1949–2018.

An ensemble of ANN forecasts indicates the machine learning-based ANN models to be capable of forecasting the IOD index well in advance with excellent skills. The forecast skills are much superior to the skills obtained from the persistence forecasts that one would guess from the observed data. The ANN models also performed far better than the models of the NMME with higher correlation coefficients and lower root mean square errors for all the target months of May-November.

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Realising the importance of data, Indian enterprises are leveraging it to enhance customer experience, employee productivity and business growth. According to Forrester, insights-driven companies will earn $1.8 trillion by 2021.

This journey of maximising data starts with the building of a data lake.

Insights drive productivity, cost efficiency, newer opportunities

“A massive data lake aggregates data from all our systems and third party sources,” says Bharat Krishnamurthy, CTO, Exide Life Insurance.

Krishnamurthy powered data with a machine learning model to predict the documents required from customers to process an insurance.

This seamless experience extended to his field agents who could process the documents without any lag. In Exide Life Insurance’s case, the machine learning model also helped predict the persistency of customers in paying premiums for the next year.

Healthcare has been equally geared up to implement an insight -driven culture to arrive at decision making faster and reducing the cost of patient care.

Santosh Rathi, VP, Columbia Asia saved Rs 7 crore last year with optimization of medical assets based on the data generated by the healthcare chain.

Relying on the dataset — consisting of a particular equipment’s business utilization from the platform — the total cost of ownership and the total cost to maintain the equipment which rigorously monitored month-on-month. This helps Rathi to arrive at a decision whether to keep, shift or discard a particular medical equipment, thereby driving cost optimization for business.

“Now I can see a trend where a particular manufacturer gives me that kind of cost vis-a-vis clinical operation, doctor’s ease of use, and the reliability of the doctor with respect to the equipment,” Rathi says.

Insights derived from structured data also helps the manufacturing sector to come up with newer initiatives, explains Beena Nayar, Head-IT, Forbes Marshall.

“Several years of data has been captured through IoT enabled sensors and different technologies. We are in the process of building a data lake and analyzing it. We have built one level of analytics, now we focus on the next to enhance it,” she says.

Building predictive analytics can help with monitoring the parameters of flagship assets of the company and bring in corrections real time for efficiency.

Data-driven journey is a bumpy ride

Though the benefits to be derived as enormous, Krishnamurthy lists some of the practical challenges that enterprises encounter in their journey to leverage data.

Data consolidation is the first hurdle. “One of the challenges is to have a single view of the data. The challenge is also to ensure that every system across the organization represents data in a uniform way,” he explains.

Krishnamurthy points out that it is important to have a common data dictionary across the organization so that every department be it finance, sales, analytics, or marketing refers to a particular terminology with the exact same definition.

Another major challenge in the whole exercise is ensuring security and access control around this data. “It is a continuously changing ecosystem with new sources of data coming in all the time, new partnerships being made, the third party data sources contributing to the database,” he says.

Technology leaders believe that the data consolidation and providing uniform view is a change management exercise in itself. It is therefore essential to build a suitable environment for such experiments to thrive.

“In order for the culture of the larger organization to change, it is imperative that data is democratized and made available to everyone in a form which makes sense to the individual and meets their specific requirements,” iterates Vishal Bhasin, SVP-Technology, Viacom18 Media.

To foster the process further, Bhasin set up detailed workshops with different stakeholders to understand the exact requirements from executives, business owners and analysts, operations team, and data scientists.

“After diligently understanding the ask, we curate the data models and generate customized dashboards for different user groups,” he says.

In addition to descriptive analytics, the data engineering pipeline and unified analytics layer also supports predictive analytics ensuing a data-driven, decision-making culture.

Though the idea of transforming to a data-driven model seems enticing to the enterprises, a lot of IT leaders deal with the availability of relevant skill sets. The requirement can be narrowed to data science, data engineering and a sound understanding of the business, says Krishnamurthy. This sets the base for artificial intelligence and machine learning implementation in the organization.

Data science entails having core ML or AI skills and understanding the models and algorithms. An edge above others would, however, lie in understanding the underlying data.

Data engineering involves filling data from across the organization into a form that can be used by machine learning algorithms. Understand business well enough is critical to guide data scientists into defining the problem, concludes Krishnamurthy.

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India is the largest producer and the second largest exporter of cotton in the world. Yet, for the farmers raising cotton crop the year 2017 was horrendous. Reason? Nearly 50 per cent of their yield was destroyed following an attack by the pink bollworm. This happened despite cotton farmers using nearly 55 per cent pesticides in India for their crop. The problem is not availability of pesticides. It’s the early detection of pests. A large portion of the cotton crop in 2017 could have been saved if farmers were able to identify pests at an early stage of infestation. Wadhwani AI aims to change that.

Wadhwani AI is a non-profit organization based in Mumbai. The organisation says that it is dedicated to using artificial intelligence for social good. The company focuses on using AI in their field of health and agriculture.

Before we understand what how this startup is helping the small cotton farmers in India, it is important to understand what leads to a large infestation, such as the one in 2017, that destroyed the crop. Small farmers primarily depend on field staff or the extension workers appointed by the government for advice. These extension workers visit farming plots in every village where pest traps – that are essentially sticky paper where pests get stuck – are placed. They manually identify the pests and count them for infestation. They enter this data on their smartphones and send them to experts for advice. When the experts analyse this data, they send their advice for the farms with infestation.

This technique is not very efficient as it is not very reliable. It is also time consuming. This is where the artificial intelligence based model developed by the Wadhwani AI, which of late is backed by some of the expertise from Google, comes into picture.

nstead of the extension workers manually identifying and counting the pests, the workers just have to take a picture of the pest trap. The AI model working in the background then recognizes various pests and it gives on-spot advice to the farmers by processing the data collected in the cloud. This data also goes to the experts for further analysis.

While this method is efficient it can be rendered ineffective, especially in the areas with low connectivity. Wadhwani AI has solved that problem by compressing its AI model such that it can work on a basic smartphone. “We are quite excited to announce that we have just achieved model compression that makes this AI model small enough to put on a really basic smartphone and hence this can now actually work offline,” said Raghu Dharmaraju, vice president, products and program, Wadhwani Institute for Artificial Intelligence. He was talking about the details of the programme at Google’s Solve with AI conference in Tokyo last week.

What is interesting about this feature is that it is not available as a standalone, rather it is an open source model that is available to any agricultural program around the world.

“By augmenting human skills, we are able to help millions of farmers right from the beginning. That’s technology at scale,” he added.

That leaves us with just one question: How does Google help here? Wadhwani Institute for Artificial Intelligence is one of the 20 winners of the Google AI Impact Challenge 2018 that provides a funding of $20 million along with mentorship to all the winners so that they can complete the first phase of their operations and scale them further. The institute is also one of the three winners from the Asia Pacific region. Basically, Google is empowering this company to improve its product and scale it up such that it can reach to farmers across the country.

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