The first case of the 2019-2020 Coronavirus pandemic in India was reported on January 30, 2020, originating in China. Experts suggest the number of infections could be much higher as India’s testing rates are among the lowest in the world. The infection rate of COVID-19 in India is 1.7, significantly lower than in the worst affected countries.
The World Health Organisation chief executive director of health emergencies program Michael Ryan said that India has “tremendous capacity” to deal with the coronavirus outbreak, and as the second most populous country, will have enormous impact on the world’s ability to deal with it.
DexLab Analytics, in the first part of this blog series, studied the statewise breakup of COVID-19 cases in India through a Jupyter Notebook. Libraries were called, maps were drawnand data was taken from Kaggle.
The data and code sheet can be found below.
In this part of the blog series we will study how states are performing with regard to laboratories and testing. First we make three data sets – that of confirmed cases, recovered cases and cases of deaths.
We first plot this data on a graph and study it carefully. Then we make a pivot table and study the data. We then also study which state is performing how many tests on people. Kerala is found to have done the maximum number of tests (Fig.1.).
Fig. 1.
The purpose of this video is to teach you how to use visual graphs in Python. Now we aim to find why testing is underdone in states. Is there a possibility of a lesser number of labs in the first place? We get a graph (Fig. 2.) that shows us how many labs each state has for testing COVID-19 samples.
The first case of the 2019-2020 Coronavirus pandemic in India was reported on January 30, 2020, originating in China. Experts suggest the number of infections could be much higher as India’s testing rates are among the lowest in the world. The infection rate of COVID-19 in India is 1.7, significantly lower than in the worst affected countries.
The World Health Organisation chief executive director of health emergencies program Michael Ryan said that India has “tremendous capacity” to deal with the coronavirus outbreak, and as the second most populous country, will have enormous impact on the world’s ability to deal with it.
Other commentators worried about the economic devastation caused by the lockdown that has huge effects on informal workers, micro and small enterprises and farmers and self employed people who are left without a livelihood in the absence of transportation and access to markets.
The lockdown was justified by the government and other agencies for being pre-emptive to prevent India from entering a higher stage which could make handling very difficult and cause even more losses thereafter. According to a study by Shiv Nadar University, India could have witnesses a surge of 31,000 cases between March 24 and April 14 without lockdown.
So we call a Jupyter Notebook in Python to study India’s COVID-19 story.
The data and code sheet used in this study can be found below.
We will first import all libraries like pandas and numpy. All the data has been taken from kaggle. We then take the data and work a dataframe on it. And then we generate an India map to study the spread of SARS-CoV-2.
Artificial Intelligence has transformed the world of digital marketing by making it ultra intelligent and intuitive. Almost every platform used by the digital marketer is powered by some form of AI or an AI-powered machine learning model.
If we were to define what AI marketing is, according to a report by Forbes, it is a method of leveraging technology to improve the customer journey. It can also be used to boost the return on investment (ROI) of marketing campaigns.
How AI Works In Marketing Strategies
AI plays a very important role in eliminating guesswork when it comes to customer interactions online like in email marketing. Big Data Analytics, machine learning and other related processes gain insights into target audience behaviour. “With these insights, you can create more effective customer touch points.”
Moreover, it is gradually automating processes that were once dependent on human beings. Content generation, PPC ads, and even web design and video marketing are all possible applications for AI marketing.
Marketing Campaigns
AI, in the world of digital marketing, can streamline and optimize marketing campaigns. “It can also eliminate the risk of human error”. It acts as a support system to shore up efforts born out of human ingenuity with its data driven reports and analyses.
While AI might be able to launch a marketing campaign all on its own, human attributes like empathy, compassion and the art of storytelling are still needed to shape up the soul of an online marketing campaign.
Content Generation And Curation
“At present, content marketing has ballooned into a global industry. It’s so prevalent that some refer to it as the only type of marketing.” Moreover, AI powered content marketing strategies are also becoming a rage.
AI can be used potentially for both curating and generating content. Already, companies are using AI for automated content generation at a basic level. But in the long run, “AI could generate viable topics for writers, or even develop initial drafts of content based on certain parameters”.
Digital Advertising
AI is also gradually transforming the way businesses advertise. In fact, today’s digital advertising strategies all have a basic level of AI powered models processing them.
AI works with the help of algorithms in its systems. “These systems operate autonomously, placing the right kinds of ads in front of the right kinds of people based on complex algorithms and big data.” This feature service is known as “programmatic advertising.”
Chatbots
Chatbots have become the latest game changer when it comes to the marketing industry. They are the first interface customers encounter on many websites today, giving the website a human touch, excelling at answering customers’ frequently asked questions.
“The key fascination with chatbots is the impact they can have on the customer experience. For some businesses, there aren’t enough employees or hours in the day to answer customer queries quickly. Chatbots allow customers to help themselves.”
Behavior Analysis And Predictive Analytics
More and more companies are beginning to hire data scientists and programmers for their marketing departments. There “are so many data sets (on the Internet) that humans alone can’t possibly hope to analyze them all.”
“Using machine learning and big data analysis, AI is able to provide businesses with deep insights into their customer(s’ behaviour). Not only will businesses be able to hyper-personalize interactions, but…they’ll also be able to predict future customer behaviours based on the data collected.”
In the midst of a crisis as big as COVID-19 which has as per the current update taken more than 80,000 lives around the world and caused nations to go under a lockdown, machine learning and artificial intelligence has played a vital role in detecting COVID-19 positive cases and helping fast track the tests and analysis of drugs that can be a solution to the problem.
Countries around the world are building algorithms to better understand the molecular structure of the diseaseso that the so-called protein around the SARS-CoV-2, the virus that causes COVID-19, can be blocked.
Use of AI and machine learning around the world
The legendary story of a Canada-based AI named Bluedot is not something one can forget. Buledot is a low cost AI based tool that predicted the outbreak of the disease in December 2019 and also predicted the top 20 destinations where passengers from Wuhan, the origin of pandemic, would arrive.
Deargen a South-Korean company built an AI tool named MT-DTI to test and analyze the molecular bond of the SARS-CoV-2 protein and recommend medication on the basis of the same. Even though their recommendations are yet to be reviewed and tested properly, their find might prove to be a super solution to reduce the number of death counts and covid positive cases.
Insilico Medicine, a Hong-Kong based company instead of building a platform to recommend the currently available medication to break the protein of covid-19 virus and stop it from replicating, built an AI based tool to formulate and test the potency of new and chemically advanced medication to break the chain.
Benevolent AI, a British startup based on its machine learning tool discovered six important covid-19 virus fighting compounds that can prove to be helpful in curing and limiting the spread of the disease. Out of the six compounds discovered, ‘baricitinib’, a compound used to cure rheumatoid arthritis according to the sources, proves to be the safest compound that can be tested on the covid positive volunteers.
Use of AI and machine learning in India
The spread of the covid-19 pandemic in India began by the end of January 2020 and till date more than 5000 cases have been reported. The Government of India declared a lockdown by mid-March 2020 and in the middle of all this when misinformation and rumors were rising day by day, to tackle the problem the Government of India launched its covid-19 tracking app called Aarogya Setuthat means ‘a bridge of health’.
This AI-based app uses bluetooth and the mobile location of the user and informs them if there is any person covid positive within 6 feet of their location or not. The app checks the user’s data in their own database and in case the data matches a corona positive patient then it only notifies their presence keeping their identity anonymous.
India has also launched a whatsapp chatbot and any one using the whatsapp platform can access the chatbot by simply typing “MyGov Corona Helpdesk” and get the relevant and authentic information relating to the symptoms and how they can get help from direct sources.
Apart from the above initiatives to tackle the pandemic, medical practitioners, data analysts and data scientists from around the world are trying to find ways and means and analyzing the database to capture the trends and are working day and night to find best possible cure to fight the disease.
Artificial Intelligence has revolutionized the banking sector, transforming back end processes into faster mechanisms, making money transfers safer and back-end operations more efficient.
From fraud detection to customer service chatbots, AI is powering several banking wings. Here is a list of operations AI has been facilitating in banks across the world.
Customer Support & Front Office
Millennials have rendered brick and mortar banks dispensable. According to Business Insider, 40 per cent of this generation does not use the services extended in physical bank offices.
AI has come to the rescue, however, by powering chatbots and voice assistants in most major financial institutions. Kasisto, for example, is one such neo-banking institution that has been using AI to the hilt.
“Kasisto’s major contribution is its conversational AI platform, KAI, which banks can use to build their own chatbots and virtual assistants. It’s rooted in AI reasoning and natural-language understanding and generation, which means it can handle sophisticated questions about finance management that other bank customer-service digital assistants…can’t”, says a report.
Kasisto has supported and shored up AI assistants for several reputed banking institutions including the UAE-based digital bank Liv., DBS Bank, Standard Chartered Bank and TD.
“The bank’s KAI-based bot walks customers through how to make international transfers, block credit card charges and transfer you to human help when the bot hits a wall.”
Fraud Protection & Middle Office
Artificial Intelligence has truly transformed “middle office functions” – where banks manage risk and protect themselves from bad actors. These functions include fraud detection, anti-money laundering initiatives and customer identity verification.
“And sometimes that means incorporating AI into legacy, rules-based anti-fraud platforms. But some the most innovative and secure countermeasures are other, from-the-ground-up models, built by companies like the ones below.”
“Up to $2 trillion is laundered every year — or five percent of the global GDP, according to UN estimates.” The sheer number of investigations across the globe, coupled with the complexity of data and reliance on human involvement makes anti-money laundering (AML) difficult work.
AML processes also cost a lot. Ayasdi’s AI-powered AML incorporates three key advancements: “intelligent segmentation, or optimizing the data-sifting process to produce the fewest number of false positives; an advanced alert system, which auto-categorizes alert priorities; and advanced transaction monitoring, which uses machine learning to spot suspicious anomalies”.
Lending And Risk Management
“AI holds real promise for more equitable credit underwriting — as long as practitioners remain diligent about fine-tuning the algorithms.
Beyond credit scoring and lending, AI has also influenced the way banks assess and manage risk and how they build and interpret contracts.”
Denying credit to persons because of a class or racial bias is something that ails the banking industry across the world. ZestFinance’s“AI-based software purportedly generates fairer models, essentially by downgrading credit data that it has “learned” results in unfair decisions”.
While the spread of the COVID-19 pandemic has become a bane for economies across the world, slowing down or bringing to a halt markets and trade, the series of lockdowns declared by states has had a positive impact on the environment.
According to China’s Ministry of Ecology and Environment, datarecorded between January and March 2020 reflects an 84.5 per cent increase in days with good air quality in 337 cities, and satellite data from the United States National Aeronautics and Space Administration shows a decline in nitrogen dioxide over China.
This piece of news is certainly welcome. Climate change is one of the biggest crises ailing our world today, with scientists and stakeholders worried. However, technological advancements like those in the field of Artificial Intelligence are to a large extant helping tackle the crisis of climate change. Here is how.
Improved climate predictions
At the intersection of data science and climate science is the piece of technology called climate informatics.
It includes areas like“improving prediction of extreme events such as hurricanes, paleoclimatology, like reconstructing past climate conditions using data collected from things like ice cores, climate downscaling, or using large-scale models to predict weather on a hyper-local level, and the socio-economic impacts of weather and climate.”
AI can also uncover new insights from the massive amounts of complex climate simulations generated “by the field of climate modeling, which has come a long way since the first system was created at Princeton in the 1960s.”
Better predictions can help officials make informed climate policy, allow governments to prepare for change, and potentially uncover areas that could reverse some effects of climate change.
Revealing the effects of extreme weather
AI is helping scientists reveal to common persons the effects of extreme weather conditions so they can work towards reversing the effects.
“To make it (the effects) more realistic for more people, researchers from Montreal Institute for Learning Algorithms (MILA), Microsoft, and ConscientAI Labs used GANs, a type of AI, to simulate what homes are likely to look like after being damaged by rising sea levels and more intense storms.”
This was done to inculcate in people habits that are eco-friendly and ecologically sustainable.
Measuring sources of carbon
By monitoring coal plant emissions with satellite imagery, Carbon Tracker, an independent financial think-tank, can use the data it gathers to convince the finance industry that carbon plants aren’t profitable.
“Agrant from Googleis expanding the nonprofit’s satellite imagery efforts to include gas-powered plants’ emissions and get a better sense of where air pollution is coming from.”
AI can help make analysis of power plants images automated to get regular updates on emissions. “It also introduces new ways to measure a plant’s impact, by crunching numbers of nearby infrastructure and electricity use. That’s handy for gas-powered plants that don’t have the easy-to-measure plumes that coal-powered plants have.”
Artificial Intelligence has made advancements in various sectors of the economy. But it has not yet taken the business world by storm. Business leaders, however, are excited about implementing AI in their companies’ business functions to start reaping its benefits. Here is a list of ways in which AI and machine learning will impact business functions across the globe.
Marketing
AI can assist in working out business strategies as well as implementing them. “Already AI sorts customers according to interest or demography, can target ads to them based on browsing history, powers recommendation engines, and is a critical tool to give customers what they want exactly when they want it,” says a report. Also, AI is being used as a marketing tool in the form of chatbots. These chatbotshelp solve problems, suggest products or services, and support sales. Artificial intelligence also helps marketers build and make adjustments to marketing campaigns according to consumer behavior analyzed accurately by AI systems.
Sales
AI improves sales functions by improving forecasting, predicting customer needs, and improving communication.
Research and Development
AI can help analyze a large amount of information in industries like healthcare, pharmaceuticals, finance, and more. It can help us research problems and find solutions to them efficiently and accurately. “AI can automate many tasks, but it will also open the door to novel discoveries, ways of improving products and services as well as accomplishing tasks. Artificial intelligence helps R&D activities be more strategic and effective.”
IT Operations
Also known as AIOps, AI for IT operations is the application of AI and machine learning to IT operations in an organization. “AI is commonly used for IT system log file error analysis, with IT systems management functions as well as to automate many routine processes.”AI helps alert the IT team so they can fix problems before the IT systems crash. AIOps helps the IT component of businesses improve system performance and services.
Human Resources
AI can help human resource acquisition by effectively scouting for talented workers and prospective hires. “AI can help human resources departments with data-based decision-making and make candidate screening and the recruitment process easier. Chatbots can also be used to answer many common questions about company policies and benefits.”
Contact Centers and Customer Experience
The contact centers of an organization serve as important points of data collection “that can be used to learn more about customers, predict customer intent, and improve the “next best action” for the customer for better customer engagement.” The unstructured data collected from contact centers can also be studies and analyzed by machine learning systems to uncover customer trends and then improve products and services. Also, AI helps improve customer experience by offering loyalty points to customers and recommending what they can shop for according to their preferences.
Manufacturing
Companies like Heineken use data analytics at every stage of the manufacturing process from the supply chain to tracking inventory on store shelves. “Predictive intelligence can not only anticipate demand and ramp production up or down, but sensors on equipment can predict maintenance needs. AI helps flag areas of concern in the manufacturing process before costly issues erupt.”
Accounting and Finance
Human finance professionals will be freed of repetitive tasks so they can focus on more serious activities while the use of AI in accounting will reduce errors. “AI is also able to provide real-time status of financial matters to organizations because it can monitor communication through natural language processing.”
Artificial Intelligence (AI) is revolutionizing innumerable aspects of our lives, education being one of them. AI has transformed the way we learn, the relationship between the student and the teacher and the very manner in which our curriculum is perceived. This article, the third part of a series on the applications of artificial intelligence, delineates how AI has come to transform the education sector, as we know it.
The biggest contribution of AI to the education sector has been towards enhancing and streamlining the system of teaching students with varying needs across the spectrum, from elementary schools to adult learning centers. Students can be mentally developed in the left side of the brain with more analytical skills or they can be mentally developed in the right side of the brain with more creative and literary skills. Likewise, there may be students with different interests and passions. A strictly uniform curriculum does not suit all students of the same class because people differ in their learning ability and interests.
AI-Enabled Hyper-Personalization
AI is thus being used to customise curricula according to specific needs of each student of a single class. This is being done through the power of machine learning via a method called hyper-personalization. The AI powered system studies and examines the profile of a student and prescribes suitable curricula for her/him. According to a report, it is expected that by the year 2024 onwards, almost 50 percent of learning management tools will be powered by AI capabilities. These AI-enabled e-Learning tools will touch over $6 Billion in market size by 2024.
Smart Learning Tools
Machine Learning and AI are also defining the way hyperper sonalized and on-demand digital content is created to digitise the learning environment. Now students do not have to rote-learn chapter after chapter from textbooks. They are absorbing learning material in the form of condensed bits of information in the form of smaller study guides, chapter summaries, flashcards, as well as short smart notes designed for better reading and comprehension. Learning is therefore becoming gradually paperless. AI systems also have an online interactive interface that helps in putting in place a system of feedback from students to professors regarding areas they are facing trouble understanding.
Digital Conversations
AI systems are also being used to develop the system of tutoring with personalized conversational education assistants. These autonomous conversational agents are capable of answering questions, providing assistance with learning or assignments, and strengthening concepts by throwing up additional information and learning material to reinforce the curriculum. “These intelligent assistants are also enhancing adaptive learning features so that each of the students can learn at their own pace or time frames”.
Adoption of Voice Assistants
In addition, educators are relying heavily on using voice assistants in the classroom environment. Voice assistants such as Amazon Alexa, Google Home, Apple Siri, and Microsoft Cortana have transformed the way students interact with their study material. In the higher education environment, universities and colleges are distributing voice assistants to students in place of traditionally printed handbooks or hard-to-navigate websites.
Assisting Educators
AI powered systems are not only helping students with course work, they are also empowering teachers with teaching material and new innovative ways to educationally express themselves. It is easier to explain a theory with the help of picture cues and graphical representation than mere definitions. The Internet has become a treasure trove of teaching material for teachers to borrow from. Also, teachers are burdened with responsibilities “such as essay evaluation, grading of exams…ordering and managing classroom materials, booking and managing field trips, responding to parents, assisting with conversation and second-language related issues…Educators often spend up to 50% of their time on non-teaching tasks.”AI powered systems can help streamline these tasks and handle repetitive and routine work, digitise interaction with parents and guardians and leave educators with more time to teach students.
The fields of Artificial Intelligence, Machine Learning and Data Science cover a vast area of study and they should not be confused with each other. They are distinct branches of computational sciences and technologies.
Artificial Intelligence
Artificial intelligence is an area of computer science wherein the computer systems are built such that they can perform tasks with the same agility as that done through human intelligence. These tasks range from speech recognition to image recognition and decision making systems among others.
This intelligence in computer systems is developed by human beings using technologies like Natural Processing Language (NLP) or computer vision among others. Data forms an important part of AI systems. Big Data, vast stashes of data generated for computer systems to analyze and study to find patterns in is imperative to Artificial Intelligence.
Machine learning
Machine learning is a subset of artificial intelligence. Machine learning is used to predict future courses of action based on historical data. It is the computer system’s ability to learn from its environment and improve on its findings.
For instance, if you have marked an email as spam once, the computer system will automatically learn to mark as spam all future emails from that particular address. To construct these algorithms developers need large amounts of data. The larger the data sets, the better the predictions. A subset of Machine Learning is Deep Learning, modeled after the neural networks of the human brain.
Data Science:
Data science is a field wherein data scientists derive valuable and actionable insights from large volumes of data. The science is based on tools developed with the knowledge of various subjects like mathematics, computer programming, statistical modeling and machine learning.
The insights derived by data scientists help companies and business organizations grow their business. Data science involves analysis of data and modelling of data among other techniques like data extraction, data exploration, data preparation and data visualization. As data volumes grow more and more vast, the scope of data science is also growing each passing day, data that needs to be analyzed to grow business.
Data Science, Machine Learning and Artificial Intelligence
Data Science, Artificial Intelligence and Machine Learning are all related in that they all rely on data. To process data for Machine Learning and Artificial Intelligence, you need a data scientist to cull out relevant information and process it before feeding it to predictive models used for Machine Learning. Machine Learning is the subset of Artificial Intelligence – which relies on computers understanding data, learning from it and making decisions based on their findings of patterns (virtually impossible for the human eye to detect manually) in data sets. Machine Learning is the link between Data Science and Artificial Intelligence. Artificial Intelligence uses Machine Learning to help Data Science get solutions to specific problems.
