COVID outbreak has affected many industries with some sectors being affected badly. The infection is hitting associations hard from everywhere throughout the world. Businesses across the globe are adapting to the ‘new normal’ of working remotely.

In this time of crisis, Artificial Intelligence can be a great aid for businesses to boost their operations and increase productivity. This article will discuss how AI can help revive industrial operations from COVID impact.

1. Demand & supply: Organizations always tend to maintain the balance between demand and supply to avoid excess inventory. In this time of crisis, maintaining this balance is critical to achieving as, even if the demand data is available in the open sources, production is either at a halt or is operating with minimum human support. For industries like manufacturing, steel, factory, etc production, their basic activity, operates with human resources. But organizations who have upgraded themselves to industry 4.0 have AI in place to help and enhance human taskforce. AI can perform the same tasks as that of a man but at a faster pace. AI needs data to learn and during this COVID time availability of current data might be a challenge for the organizations. Organizations are using data to train AI to be representative for it to learn the patterns and intents, for demand forecasting and optimal supply chain distribution.

2. Administrative jobs: Artificial intelligence can perform admin tasks like scheduling meetings, tech support, issuing refunds, order tracking, etc with accuracy. This way AI supports human resources by enabling them to focus on value-added activities. AI automates the tasks using machine learning. Advanced AI can perform structured and unstructured tasks from its learning algorithm. This capability will allow organizations with a fair amount of work in a short span of time.

3. Revenue forecasting: Generating revenue in the next few months will be a big challenge for organizations especially for small and medium businesses whose operations are at halt currently because of lockdowns in several parts of the world. Cash flow becomes critical during times like these pushing accounts to maintain data on the path of cash-flows. This data when feeding to AI, will analyze the purpose of cash-flow and forecast the paths where the cash should flow to gain revenue. The data fed or provided must be accurate for AI to predict actionable insights that would benefit the decision-makers. Many organizations are already moving towards revenue loss but with AI they can streamline the cash-flow with profits.

4. Staffing and infrastructure planning: Because of this pandemic and the worldwide lockdown to maintain social distancing employees are staying indoors. Work from home is followed by several organizations but this method is not applicable to all industries. Industries like manufacturing, automobiles require their staff to be physically present mostly in the production and technical field. AI can benefit these industries to identify the number of staff required to be present in the field so that the operations can continue with the minimum workforce. Following revenue losses, many organizations are laying off staff to balance their revenue profits. AI can be utilized to decide if the number of layoffs is correct, more-skilled employees should retain, and many other decisions.

The artificial intelligence network is working profoundly to deliver its applications in every sector to fight the COVID pandemic. But the AI framework is still at the nascent stage and will take time and data to train themselves to fight the next pandemic with substantially more effectiveness and efficiency.

Amidst these trying times, we got a chance to sit down for a chat with our Co-founder & Chief Analytics Officer Ms. Parvathy. She has taken the time to answer questions asked by Mr. Sridharan, our Talent management head and reflect on how her whole journey began, the inspiration behind creating FLASH, steps she and her team have taken to combat the current situation, the vision of geniSIGHTS, major challenges she faced while building FLASH among other things.

Sridharan –  We would like to hear about you from yourself.

Parvathy – I hail from Thrissur, Kerala, and moved to Chennai to pursue my masters in Econometrics. Madras University was one of the few universities in India that was offering a course in Econometrics during that time. I was intrigued by the subject and this served as my entry point into the field of analytics. I joined Aaum Research and Analytics, the parent company of geniSIGHTS, as a Data Analyst and was among the company’s early employees. It’s been an overwhelming journey of 10 years now. With the support of our Founder Rajesh Kumar and a wonderful team, we have delivered some very challenging analytics assignments, built interesting AI products, spun off the platform initiative into a separate product company, and also broadened our horizons to the North American and Canadian markets in these past years. 

Sridharan – So, let’s talk about the COVID situation, how have you and your team handled the situation? What are some of the major changes you made? Also, what are the major challenges you face every day?

Parvathy – We went into a remote working policy a week before the lockdown was announced in India. We have always believed in extending remote working and flexible working hours to our employees in the past and hence it was not a major transition for us. Having our servers running on the cloud and encouraging remote access for all our projects with customers from the beginning made the transition go smooth for us. Apart from those quick huddles to co-ordinate across teams or brainstorm over ideas and those interesting pieces of chai conversations that we miss terribly, work has taken its normal course otherwise. 

Sridharan – We see technology like AI playing a major role directly or indirectly in the fight against COVID, have you come up or do you plan on building anything that’s relevant to these times?

Parvathy – Over the past few weeks, we have seen innumerable instances of AI playing a major role in areas like virus diagnosis, patient care, drug discovery, etc. When the outbreak happened, we saw an influx of dashboards reporting numbers on COVID statuses and hotspots across the country/state/district which was very useful. We wanted to however focus our efforts on understanding how the public was reacting to the pandemic. With the implementation of lockdown and most of the companies going for a temporary shutdown or remote working, mobile screen time of users has increased considerably. Most of them have also become more vocal about their thoughts and activities on social media. We wanted to make sense of this disruption and thus launched ‘Ordo Ab Chao’, an intuitive dashboard providing insights on how the nation is reacting to this pandemic. It is powered through our flagship product FLASH making it interactive over voice. This novel dashboard gives users data-driven insights on the pulse of the nation during these upheaval times as vocalized through social media. 

Sridharan- When and how did the concept of FLASH come about? What was the inspiration? What’s the vision?

Parvathy – We have seen technology adapt over the three stages of click, touch, and voice over the past decade. With the integration of voice-based products and AI assistants like Google Assistant, Alexa, etc. into everyday technology, there has been a paradigm shift for individuals to control a large part of their day to day technology needs over voice. This was where FLASH drew its inspiration from. FLASH is your data assistant and marks your essential BI transition from touch to voice. 

Sridharan – What do you think makes FLASH unique? What’s different about it when compared to the other players in the market?

Parvathy – FLASH is a first of its kind AI-driven dashboard with voice support for decision-makers. It is meant for users who are hard-pressed for time and would want to get near-time updates on their business through a conversational tool. The fact that it marries AI and voice support to provide relevant data-driven insights through conversations driven by the speed of thought is what makes it unique over other BI tools available in the market. While there have been tools on Search and AI-driven technology, we have not come across anything like a true data assistant yet. This is FLASH’s USP and the product is also on a patent-pending state in this regard. 

Sridharan – Could FLASH help businesses especially in situations like these? How could it add more value?

Parvathy – FLASH comes with an admin and business user suite that makes configuration and engagement with users easy. The admin suite is powered through features that enable users to add data elements to dashboards dynamically and self-build their own dashboards through voice/text. The business user suite is constantly listening and learning from the user’s interests, past queries, past behavior patterns to provide users with personalized recommendations, and relevant insights. Such features help businesses to scale their deployments quickly and support users to make proactive decisions based on data-driven insights. 

Sridharan – What do you think the roadmap would be for AI-driven products like FLASH?

Parvathy – There is no doubt that AI is soon going to become the de facto standard for all organizations to thrive irrespective of the nature of their businesses. Amidst the ongoing crisis, organizations will need to adopt innovative means to keep their operations running. We, at geniSIGHTS, is committed to make AI an affordable platform for all businesses. While the first set of FLASH deployments was on-prem based and for large corporates, we are soon rolling out a cloud and subscription-based offering of FLASH targeting the small and mid-sized companies. With the technology available at a near and easy reach, the AI adoption drive amongst businesses will scale up and help them in their daily challenges. 

Sridharan – What are some of the other initiatives your team is currently engaged in? 

Parvathy – The team has also been focusing on Conversational AI oriented deployments outside the scope of FLASH otherwise. This is another emerging area of technology that will see widespread adoption. We have already seen a lot of traction for this in the North American markets. Big Data and IoT related offerings have always been a part of our kitty otherwise. The best thing about our standalone AI products is that they are exposed as Restful services making it easier for companies to integrate with their own platforms or products.

Pandemic like the ongoing coronavirus outbreak which has no previous recorded information about how it’s being created has ended up being a great challenge for the government and healthcare officials to collect information and find a way to eradicate it permanently. This hinders the efforts to prevent and rapidly respond to the virus attack and increases the term of the spread. This is where artificial intelligence (AI) plays a crucial role in foreseeing an outbreak, limiting it’s spread and aid researchers fight such pandemics in the future.

Data is pivotal for AI to develop potential medicines for diseases. Currently, the biggest challenge is the availability of past COVID data or any data that is similar. AI identifies possible drug compounds by using generative design algorithms which produce a wide range of prospective results and further culminate the ones worth looking for attentively. There are various AI projects where scientists are closely studying AI as a diagnostic tool. Not only the drug data but other data like news reports, internet, and social media contents are also important to forecast a virus outbreak. Researchers are being able to detect the initial COVID outbreak stages with the content available on the internet and social media channels.

COVID outbreak has accelerated various AI applications to search for a cure. It might be a bit late now for the current AI technology to completely eradicate the current COVID outbreak but with the huge advancements in AI, there’s hope any outbreaks in the future will be better handled. Tech companies have already started researching other potential AI applications to mine all the outbreak information and train their AI with accurate information in order to unfold any future pandemic beforehand. As artificial intelligence can find connections and patterns through big data, it makes it easier to determine what kind of treatments or experiments could yield results. When compared to the initial outbreak, data now are immediately available which is helping AI in a greater way as some researchers are able to produce the genomic sequence of the new virus. This will help future scientists to do such studies and trials with increasingly precise and fast outcomes. The role of artificial intelligence in treating pandemics and other healthcare challenges is just set to develop.

Significantly, the key AI elements that will majorly help in epidemics prevention is its speed and range. AI can instantly detect any anomalies which are incredible when attempting to prevent epidemics. Humans cannot efficiently detect conditions from this volume of data with much speed. AI can be trained with a wide range of available data as it can extract information from them simultaneously. Artificial intelligence is all set to turn into a firewall against deadly diseases and epidemics. This is possible because AI can not only screen and detect the conditions but also identify potential vaccines and treatments for next COVID or any similar diseases.

This treatment revelation will prove essentially significant later on. What’s more, in terms of screening, AI will get one of the essential ingredients in guaranteeing that another COVID wouldn’t affect the worldwide economy. COVID-19 coronavirus is likely to cut global GDP growth by $1.1 trillion this year, in addition to having already wiped around $5 trillion off the value of global stock markets (Source: Forbes).

Artificial intelligence algorithms are as of now developing drugs for the known diseases and this technique can be utilized to identify new anti-infection compounds that could execute any similar viruses or bacteria in the future. It will lessen the time taken by scientists to do research on new, unknown infections and their possible medications from scratch during the future outbreaks. To be successful, AI-based medication engineers would need to prepare for the time, choosing an infection genome prone to cause problems in the future and focusing on it when there are hardly any motivators to do as such.

It has been proven time and again that during any situation of crisis, technologies have been immensely supportive. Governments, academic institutions, and start-ups are all doing their part to deploy innovative solutions as quickly as possible when situations warrant.

Apps are being used as a contact tracing solution to help in breaking the chain of infection and connect the patient with the healthcare system and the government directly. Such an app also keeps the user alert about infection in their surroundings. Telemedicine/teleconsultation is a field that is seeing a wide acceptance where one can avail doctor consultations through an electronic portal.

Banking technology which is not so new is becoming of great use these days. People prefer going to shops that are UPI enabled instead of crowding at ATMs. The government is also able to transfer relief amounts and other benefits directly to the people. The constant live updates through the government dashboard itself have demonstrated the application of technology. We are able to receive live updates and all sorts of news at our fingertips.

Even the future of virtual education has become a matter of concern with the recent trend of online classes and webinars. Virtual communication has come such a long way that we are able to communicate with foreign investors and facilitate FDI & FII inflow for the benefit of our economy.

Most of the employees of the service sector are able to work and organize meetings from the comfort of their homes.

The coronavirus was first detected in Wuhan as unusual pneumonia by a Canada based AI firm. The predictive analysis could have been used globally to take further precautions. Similarly, rapid testing kits also could have been developed earlier with the help of AI.

There are pros and cons to the use of technology. However, in a time like this, the uses have clearly outdone the drawbacks.

AI applications in the automotive industry are huge, from self- driving cars to voice commands systems to build a car with more convenience and safety, Automated Guided Vehicles (AVGs) are being used to move materials in the plant units without human intervention.  AI has a huge potential to innovate the automotive industry to gain a strong foothold. Conversational assistants integrated into cars have NLP and ML algorithms enabled to provide the driver with ease of delivering commands to the system. AI also has other advantages like to imitate, develop, and assist human actions while leveraging its algorithms in precision with machine-based frameworks.

According to Allied Market Research, the global automotive artificial intelligence market is expected to reach $8,887.6 million by 2025, from $445.8 million in 2017, growing at a CAGR of 45.0% from 2018 to 2025. The growth of AI in the automotive market will increase if there’s a demand for autonomous automobiles and customer driving experience enhancements. Customer’s most preferred AI interface is voice control because it allows the drivers with hand-free control which reduces the probability of getting distracted from the road. Today voice assistants are ruling the tech industry with its advanced features like talkback, interactive experience, real-time insights, etc. The concept of self-driving cars has been there in the automotive industry for quite some time and to materialize this concept artificial intelligence plays a very crucial role. AI has its applications in many other industries too but the scope of innovation is more in the automotive industry. This cutting edge technology leads to many automobile manufacturers designing and developing the best AI-enabled automotive technology. Artificial intelligence’s subset ML enhances the AI automotive features by learning the driver’s movements and expressions from the videos/images captured from the cameras integrated into the car that helps the system to learn and perform accordingly. AI in the automotive industry is estimated to exceed $10,73 billion in the next five years (source: medium.com). The future of automotive vehicles relies on AI and its algorithms.

To get a deeper understanding of what AI/ML brings to the automotive industry, in this article we will discuss about its prominent applications in this industry.

1. Cloud computing: AI-powered vehicles require big data to efficiently perform their task and for training purposes. This application makes sure the data is available 24/7 to the system to execute AI technologies like predictive analytics, big data access, and centralized connectivity. The cloud service further helps in predicting the maintenance required by monitoring the sensors for identifying irregularities in operation. It helps the drivers to get their required items easily available by recommending them the nearest buying point and also let them pay for the fuel from inside the car in gas stations. Cloud computing allows automotive marketers to brand their product for a precise target market rather than targeting the audience as a whole.
2. Deep learning: Deep learning enhances AI activities in automobile functioning thereby avoiding the chances of having any problems. It automates the tasks which make the work of the driver easier to manage the vehicle function. It has brought a wave of innovations in the automotive industry by its applications in advanced driving assistance systems and self-driving. Deep learning has lately instigated a huge headway in this sector.
3. Infotainment systems: Features like eye tracking, speech and gesture recognition, monitored driving, etc. comprises the infotainment. These are some of the advanced features drivers’ expect from automotive vehicles. With artificial intelligence developing the infotainment systems have become better and bigger. It receives data from the cloud service neural networks for operational purposes.
4. Cognitive capabilities: Cognitive frameworks are expected to work like a human which would interpret a genuine circumstance, so as to do that, profound knowledge of unstructured data is required. Auto manufacturers have now begun consolidating this into their vehicles. With capabilities like identifying patterns, natural language interpretation, cognitive analytics gives us a path to mine and comprehend this data.

AI innovation is constantly developing and the reception of this utilizing innovation is welcoming; thanks to the engineers and analysts who have been able to structure the application to the optimized computational capacity to attain the degree of flawlessness.

Governance need not always be a time consuming, tedious, and inefficient process, at least not with the use of AI.

However, a government agency’s readiness for AI is not only about buying or and installing new technology. The dynamic nature of AI calls for preparation across multiple critical areas. To capture AI’s potential for improving governance, there has to be a solid plan to re-establish relevant existing processes, upskill, hire key staff, train personnel and refine approaches towards public-private partnerships and develop the necessary data and technical infrastructure to deploy AI.

With a growing population and a rapid economic engine, every government has a lot of data to be processed. For any new scheme to be applicable, a lot of time goes into processing data. AI can help shift through data and identify patterns that could improve decision making which is ultimately done by a person.

And Moreover, it is not just about policymaking but also for improving citizen services. For immigration services and other government portals, conversational AI can be utilized. For instance, when one has to file tax returns, the government website can provide virtual assistance. this way every layman would find it easy.

Security within a country can be enhanced with features such as facial recognition. This proves useful in airport terminals where mobility can be made faster and crowds could be managed better. In vast cities that function 24 hours, it becomes difficult to administer. Real-time data sensors can alert authorities of abnormal activity to aid in keeping a check on crime and any abnormal activity. This has been used and proved beneficial in Las Vegas. Real-time sensors can amplify public transportation convenience and safety with penetrative maintenance and risk assessment. With urban mobility being a crisis in recent times, such technology to ensure safety and traffic management is most needed.

Deloitte has estimated that automation could save US government employees between 96.7 million to 1.2 billion hours a year, resulting in potential savings of between $3.3 billion to $41.1 billion a year. (source: Deloitte insights).

Conversational AI is the quickest way to convert your leads into prospective customers. It has already built an enormous application space in the technology and marketing domains. Businesses are adopting voice-based assistants to enhance their sales efforts and increase the lead generation processes. According to McKinsey, AI has the potential to add between $1.4 to $2.6 trillion in sales for businesses worldwide.

Many well-known brands are implementing conversational AI in their business models to offer customers with personalized experiences. AI helps businesses to understand the intent of the customer while engaging in conversations with them. In the present time, customers have a variety of options to make purchase decisions. Businesses can further leverage the NLP algorithm to respond to customer intent in the form of text or voice. Conversational AI uses an ML algorithm to continuously learn customer behavior, their language based on the conversations and help generate recommendations to ease the customer’s product choices. This method can be achieved by using powerful intelligent chat platforms. The conversational AI must be well trained and computed before customer handling.  More customer-centric campaigns with marketing techniques coupled with product recommendations and predictive lead scoring can be used to drive sales and maximize revenue potential.

Customer-centric campaigns – Businesses now are more digitally-driven which is an added advantage in conversational AI implementation. Organizations’ websites have various customer-centric forms such as feedback forms, inquiry forms, subscription forms, etc which helps them to keep a record of all the data collected. And with the help of conversational AI, organizations can do predictive analytics and get insights in real-time to address the customers with more relevant products and services. Organizations can further segment the customers based on their intent by applying funnel analytics to get a more clear understanding of how their products and services are meeting their customer needs, a segment of customers’ desires, new innovations prospects are looking for and much more.

Market research – Many organizations do extensive market research before rolling out products and services in order to segment their target markets. Conversational AI eases their research analytics models by providing them with actionable insights and assistance on how to deal with various market segments. As conversational AI can automate repetitive tasks without human interventions the possibility of any error is minimal.

Sales enablement – The marketing and sales team collects hundreds of leads on a daily basis from several ongoing campaigns and sources. But only a fraction of them is capable of effectively utilizing the leads to final closure. Leads gathered need to be nurtured until the final sign-off. This process is wearisome but significant for the sales team in order to close a deal. It is widely observed that after an initial contact it takes 3-4 follow-ups by the sales team to get a response from the leads. One sales resource can reach out to one lead at a time which will be quite difficult for companies having big data. AI assistants can be integrated into any existing ERP and other databases to contact each leads in a personalized way and do multiple automated follow-ups using deep learning algorithms in order to qualify them as sales prospects within a short time span, unlike human representatives. These tasks can be easily automated using deep learning to close deals rapidly.

Customer relationship management – Sales CRM contains a wide range of leads information and quite often sales representatives are lost in those records while manually searching for lead details that lead to a loss of considerable time.  AI helps the sales team to dig up old leads collected way back and somehow fallen off track for not communicating proactively. Conversational AI when integrated with sales CRM provides deeper insights on leads’ intent, demography, and other information required by the sales rep to engage with them proficiently. One huge advantage voice-based assistants can provide here is that the sales personnel are not required to be wary of technical know-how to understand the insights provided.  They just need to speak in natural language with the tool and the NLP does the rest. The time and efforts saved by the sales representative can be productively utilized in communication with prospective clients to increase sales closures.

Predicting purchases – Predictive analytics and machine learning can predict customer purchases as the tool is consistently learning from human behavior and can alert the logistics departments about the deficiencies of required materials in their inventory well ahead of time in order to avoid any delay in sales. AI can track the movements of millions of customers on the website at once and decide the demand for definite products and services which encourages the sales team to come up with various promotions on those particular products and services.

Content selling – AI can analyze high volumes of data and track content engagement rates. This can help sales and marketing personnel to prioritize their follow-ups to provide personalized content to prospects at every stage of the customer journey and also share relevant content with the social networks thereby progressing towards a meaningful conversation and a fruitful conversion.  AI and ML can be a great support to the sales team in designing sales campaigns and promotions and allows them to offer feasible sales discounts. AI algorithms analyze past price data such as discounted figures, promotional parameters, and sales history and calculate the price elasticity for certain products and services so that the team can set prices optimally personalized to the customers.

Conversational AI not only reduces human efforts in tedious tasks but also helps the sales team to generate more revenue from the market.

The global artificial intelligence (AI) in agriculture market size is expected to be worth USD 2.6 billion by 2025 according to a report by Markets and Markets.  Agriculture is a major industry and plays a pivotal role in the economy of nations.  Population growth and climate changes have impelled the $5 trillion industry to look for sustainable and innovative solutions to improve growth.  AI has emerged as the industry’s technology arm

Agriculture is one of the most important professions for humanity. Irrespective of how the other sectors have grown, the demand for food has only increased. So, why not combine AI technology with agriculture and support both the sectors?  The rapid population growth and high demand for food combined with the scarcity of land certainly call for an efficient and effective method of farming to ensure better sustainability.

There are several terabytes of data from farms.  With the help of AI technologies and the Internet of Things, farmers can analyze real-time data on weather conditions, soil, temperature, and so on.  AI technologies also help in optimizing planning and utilization.  Farmer’s Weekly Magazine states that the use of AI-powered equipment based on the computer vision technology increases crop yields by about 30% and enables to predict weekly and seasonal crop yields with more than 90% accuracy.

The quality of crops can also be improved by identifying infected plants. Improving harvest quality and accuracy using AI systems, known as Precision agriculture technology, has been known to detect crop disease and pest damage with 98% accuracy with the help of GPS(  Global positioning system) and GIS (geographic information system) Guidance systems.  AI sensors can detect and target weeds and then decide which herbicides to apply. This not only reduces cost but also prevents excessive toxins that find their way in our food.  VRT (variable-rate technology) is automatic and can be used for a variety of farming activities. It sets the delivery of inputs depending on the soil condition noted by soil maps. Also, Rate controllers are used to control the delivery rate of a chemical (liquid/granular). These control speed, flow rate, and pressure of material in real-time.

Digital Soil mapping is a geographic representation showing the diversity of soil types in the interested area. It helps one determine what land use management will be sustainable at a particular site. These can estimate weather, soil conditions, temperature, and water usage to make better decisions. Farming can be made more cost-efficient by ensuring only the required inputs are used.

A yield map can be used to compare yield distribution within the field over a period of time. This allows farmers to know which area of the field needs improvement. With this, they can change their field management technique and develop nutrient strategies too. These yield records can support as proof to avail farmer loans.

Though all of this seems like a farfetched idea in a developing country, it has experimented. Large scale farmers use machinery for areas as large as 30 meters. However, the precision application of fertilizers and pesticides can be done in an area of 5-10 meters. Hence it will be only a one-time investment in the technology with better yields in subsequent times.

The swift increase in the outbreak of coronavirus across every nation is pressing its capabilities in medical care and a reminder to us that health transcends all borders.  Countries recently started slow adoption of artificial intelligence in their healthcare industries but with this pandemic, they are accelerating this adoption curve because the number of casualties is greater than the number of medical resources available. Due to these scarce resources governments of various countries are calling for a nationwide lockdown to limit the number of the virus spread. Countries like Iran, Pakistan, Italy, Poland, Africa and others currently in the developing stage, are facing multiple healthcare challenges in this fight against coronavirus.

Medical data collected is too huge for human intelligence to handle this is where AI plays a pivotal role. AI is trained to identify and understand patterns from big data and acts as a trump card in saving the entire humanity from this crisis.  With the help of AI, medical facilities can effectively use their limited essential supplies & medical staff to contain a situation like COVID-19. AI can predict the sudden increase in demand on healthcare services which will help them know what services are required to treat the infected patients.

Artificial intelligence is a key resource in healthcare as it can reap actionable insights from huge amounts of medical data, predict the trends, patterns of the virus spread and can make important decisions that can save millions of human lives in different parts of the world.

Hospitals are using AI to screen and classify infected patients and also identify the ones who are most likely to develop coronavirus symptoms. AI-powered cameras and drones are being used by the medical staff to scan the faces to check the temperature by maintaining a social distancing because of the virus’s ability to communicate through physical contact. In today’s world, the majority of the tech-savvy people are using gadgets like fitness tracking watches or apps related to physical monitor. AI can track these data, learn about the person’s health from the data and help identify potential people that can easily get affected or are already infected from the virus based on their health score. Medicals, as well as health tech companies, are predominantly using AI in their regions to identify infected people, people came in contact with infected people and also tracking the medical supply chain to stay alert about the usage & reduction in the health supplies in coming time.

AI applications in healthcare are not limited only to the patient’s health conditions but also for the health workers who are fighting their day and night to treat the patients to end this pandemic. Researchers in the San Francisco’s University of California are using Oura’s wearable rings designed using AI to track COVID-19 symptoms in the health of the healthcare workers. It captures the early symptoms of the illness especially for the healthcare workers who are high at risk of contracting the virus.  The AI algorithm in the ring measures primarily two signs that include elevated heart rate & temperature above 99.1 degrees Fahrenheit so that hospitals can isolate those workers for the operations & provide them with proper medical care.

Healthcare professionals around various nations are now switching on to AI capabilities to find a cure against this deadly virus. While the global pandemic outbreak is increasing the number of confirmed cases and the number of deaths across nations, many biotech firms are creating drugs to target the virus; thanks to AI, AI is helping firms and scientists find a vaccine. Vaccines imitate an infection, causing the production of defensive white blood cells and antigens.  AI is not only suggesting components of a vaccine by understanding protein structures, but it is also useful in the development of subunit and nucleic acid vaccines.

Data scientists are using machine learning to screen for antibodies that can kill coronavirus with the highest success probability score. Unlike the traditional way of discovering antibodies in the labs that take years of experiments and trials before finding the right one, machine learning algorithms can identify antibodies that can beat the virus in a matter of weeks reducing the time and cost associated with it.  AI is accelerating the development of antibodies and vaccines with the help of ML that can scan through various existing drugs to see if any of the molecular compounds in those drugs can be reused with a mix n match with other drug compounds and develop a drug to fight the current as well as the future outbreaks. Nine potential drugs have been identified by an AI-platform out of which six are already approved in many countries are being used by doctors to treat the infected patients helping to recover fast.

Conversational AI is being used by some of the healthcare companies to provide quick information about COVID to the public hence making the work of a doctor much easier in delivering the severities of contracting the disease. The information is gathered from several websites and social media channels. The conversational AI engine is trained using information collected from authorized sources to give accurate results. Deep learning is used to study information available in an unorganized/unstructured format. Here conversational AI isn’t a replacement for a doctor but it aids them to stay updated about COVID developments. Conversational AI can learn quickly and pick up buzzwords related to the objective more briskly than any other technologies & also it provides real-time information that helps the public to stay up-to-date about the outbreak news.

AI is integrated into various healthcare systems, machines & devices to empower it with the automation with a maximum level of accuracy. If AI is engineered and trained with more quality healthcare data including MRI & CT scan images, it would be easier to predict & combat such deadly diseases in the future.

The coronavirus started in Wuhan city of China. The first positive case in India was recorded on 30th January 2020.  It took almost 3 months to reach 100 cases. At the current rate, it is predicted that the ‘ending phase’ of the spread in India should start from May 9.  Japan saw a 13% daily increase in cases before reaching 100 cases and an 8.1% daily increase in cases from its 100th case to its latest. change in daily average cases was lower after the 100th in China, Singapore, South Korea, and Japan than before. They appear to have successfully flattened their curves compared to western Europe, the US, and India. As of April 14,  11,438 cases have been registered out of which 1305 have been cured and 377 have succumbed to it.

(Source: https://www.statista.com/)

A recent poll conducted by us on the expected outbreak of the virus in India showed that 61% of people anticipated an uncontrolled outbreak in India while the rest thought otherwise or have no clue about how the outbreak will go.

However, global reports state that the nation has one of the lowest numbers of COVID-19 cases per capita in the world.  5 out of 1000000 have been the reported cases in India while in the US and Italy it has been over 1000 and 2000 respectively.

A country with more than 1.3 billion population is supposedly doing better than the other countries in handling the COVID-19 spread by imposing severe measures to contain the virus spread including a nationwide lockdown declared for a period of 21 days on 24th March 2020 with essentials and medical facilities operating on a restricted time frame on a daily basis.  Working from home, social distancing, lighting lamps, whipping coffee and digital entertainment have kept us going!

The end of the lockdown period raised many questions on the minds of people. 80% of the political parties requested the central government for an extension while a few people also demanded the same by using the hashtag  #extendthelockdown when Tamil Nadu, Delhi, and Maharashtra announced a complete lockdown till the 30th of April.

On 14th April, the Prime minister extended the lockdown to 3rd May 2020 to combat the crucial stage of the spread while indicating that in safe areas, small scale economic activity might resume after the 20th of April. India has also placed travel restrictions relatively early with many states shutting off access to public places and intercity travels.

For a highly populous country, a stage of unattended community spread would be a disaster on socio-economic and political terms. Only strict adherence to government guidelines can ensure this situation does not escalate further.  The lockdown is paying off by slowing down the spread. Compared to other countries that were slow to impose the lockdowns, India has taken an extreme measure to lockdown the entire nation that is helping control the situation.

However, the country’s high population density, overburdened public health, and the possibility of transmission from younger people (50% of the population below the age of 25) to the elderly in joint families are worrying factors.  India’s GDP decelerated to the lowest in over 6 years in the 3rd quarter of 2019-20., and the outbreak of COVID-19 has posed a fresh challenge. The Abrupt cease of economic activity will drop the demand for non-essential goods. With further supply chain disruption, even the availability of essential commodities might get affected. (source: KPMG)

According to  a study, A peak in new cases is expected in week 3 of June and a potential or actual  lift of lockdown between week 4 of June and week 2 of September [source: John Hopkins University (Coronavirus Resource Center), BCG Analysis]

Agriculture, trade, education, and livelihood of daily wage workers are just a few things that have been impacted and/ or come to a standstill. Decisions to tackle the slowdown caused include better wage schemes under MGNREGA, free cooking fuel, increased pension for senior citizens and free medical insurance for medical staff.

Railway services were supposed to resume after lockdown and all tickets had been booked already. Nevertheless Fear of a rapid spread post lockdown is still fogging the minds of people.  The lack of certainty with regard to transport and work is also getting exasperating with various ideas on people’s minds at the moment. While many believe India has handled it better than any other country, some fear that the worst is yet to come. When humans are being defeated by the virus,  With the help of AI and other technology in medicine, governance, and finance we can certainly overcome these testing times.

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