The lovely tale of Liquor
during Lockdown and before
At every stage, addiction is driven by one of the most powerful, mysterious, and
vital forces of human existence. What drives addiction is longing —
a longing not just of brain, belly, or loins but finally of the heart.
Cornelius Platinga
The use of alcohol in India for drinking purposes dates back to somewhere between 3000 and 2000 BC. An alcoholic beverage called Sura which was distilled from the rice was popular at that time in India for common men to unwind at the end of a stressful day. . Yet the first mention of Alcohol appears in Rig Veda (1700BC). It mentions intoxicants like soma and prahamana. Although the soma plant might not exist today, it was famous for delivering a euphoric high. It was also recorded in the Samhita, the medical compendium of Sushruta that he who drinks soma will not age and will be impervious to fire, poison, or weapon attack. The sweet juice of Soma was also said to help establish a connection with the gods. Such was the popularity of alcohol. Initially used for medicinal purposes, with time it evolved and became the beverage that brought life to social gatherings, and eventually consuming alcohol has become a habit for many.
With such a rich history of not just humans but also of the gods,
what is a worldwide pandemic to stop anybody from drinking?
. . .
According to a report released by the World Health Organisation (WHO) in 2018, an average Indian drinks approximately 5.7 liters of alcohol every year. In a population of casual and excessive drinkers, with the shutters of liquor stores down, it must have been extremely difficult for “certain” people to survive lockdown. In the first two phases of lockdown, the desperation had quadrupled prices of alcohol in the Grey Market of India. Also, According to Google Trends, online searches for “how to make alcohol at home” peaked in India during the fourth week of March, which was the same when the lockdown was announced. As a consequence, a few people died drinking home-brewed liquor. People committed suicide due to alcohol withdrawal syndrome. Owing to the worsening situation and to reboot the economy, some states decided to open licensed liquor stores in the third phase of the COVID-19 Pandemic lockdown in India. This decision was the worst best decision the state governments could take. The kilometer-long queues in front of liquor stores were evidence that a pandemic can turn your life upside down yet your relationship with alcohol cannot move an inch.
The love in the hearts of those who are addicted was explicit. We might have seen addiction, we might have witnessed desperation but what happened in the month of May was madness, not just in terms of the way people pounced but also in the way the government earned. According to a report by Hindustan Times, on the first day of the third phase of Lockdown, the Indian state of Uttar Pradesh recorded a sale of over Rs 100 Crore from liquor. On the second day of the reopening of Liquor stores, Karnataka reported sales of 197 crores in a single day which was the largest ever. Eventually, the prices of Liquor were hiked to 100% to discourage people from drinking.
. . .
There was a special corona fee that was imposed in Delhi by Chief Minister Arvind Kejriwal. A 70% corona fee was imposed in Delhi, yet the sales did not drop. The entire situation was a disaster for the law enforcement officers, social distancing was easily abandoned and a basic code of conduct was happily violated. Despite the chaos created, the states continued to collect revenues. Home delivery of alcohol was allowed in Maharashtra and e-tokens were sold in Delhi.
Demand for liquor is inelastic which means that
the sale of alcohol is not much responsive to change in prices.
In general, since alcohol policy is a state subject in India, revenue from Liquor is a cash cow for state governments. In 2018 and 2019, four states collectively collected about 20,000 crores in taxes from the sale of liquor. As much as the state earns from the sale of Liquor it is undoubtedly, a threat to the Economy. Consumption of alcohol has dire health consequences. When a person consumes an alcoholic beverage, there is a rise in BAC because of which there is a gradual and progressive loss of driving ability because of an increase in reaction time, overconfidence, degraded muscle coordination, impaired concentration, and decreased auditory and visual acuity. This is known as drunken driving. (V. M. Anantha Eashwar, 2020) Drunken driving is the third biggest cause of road accidents and over speeding in India. Road accidents are not it; alcoholism causes sleep problems, heart, and liver issues. Also, it is not about an individual’s life, it ruins the lives of all people concerned.
Addiction also causes economic loss. In 2000, Vivek Benegal and his team assessed 113 patients admitted to a special de-addiction service for alcohol dependence. They found that
the average individual earned a mean of ₹1,661 but
spent ₹1,938 per month on alcohol, incurring high debt.
They also found that 95% did not work for about 14 days in a month. They concluded that it led to a loss of ₹13,823 per person per year in terms of foregone productivity. A more recent study, Health Impact and Economic Burden of Alcohol Consumption in India, led by Gaurav Jyani, concluded that alcohol-attributable deaths would lead to a loss of 258 million life-years between 2011 and 2050. The study placed the economic burden on the health system at $48.11 billion, and the societal burden (including health costs, productivity loss, and so on) at $1,867 billion. “This amounts to an average loss of 1.45% of the gross domestic product (GDP) per year to the Indian economy,” the study said. (Mint, 2020)
Setho ka Gaon
With each passing day, the ‘curtain of separation’ weighs down on the women of Afghanistan, paving the way for tyranny to thrive.
Arth
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AN INTRODUCTION TO MACHINE LEARNING
Ramneet and Nidhi
Have you ever wondered how Facebook's News Feed is personalized or how does Apple's Siri recognize voices? This is all because of machine learning.Machine Learning is the new buzzword of the times. What is Machine learning? Is it same as Artificial Intelligence? We know that humans learn by past experiences and machines follow instructions. But what if machines could also enhance their ability by past experiences? Well, that’s exactly what machine learning is about. Beginning with the most basic and layman definition- Machine learning is when the machines learn by past experiences just like humans. For machines, past experience is the past data. The machine becomes more and more accurate and reliable with experience without any explicit instructions given to it.
Arthur Samuel , a pioneer in Artificial intelligence who actually coined this term defines it in this way- “Field of study that gives computers the capability to learn without being explicitly programmed”.1
There can be numerous Machine Learning models developed by using different algorithms. Choice of Algorithm depends on the task we expect our machine to perform and the data that is to be used. For example- K- nearest neighbour algorithm is one of the simplest algorithms used in ML. It classifies a data point based on how its neighbours are classified. Suppose a fruit needs to be classified as an apple or a melon based on its two characteristics namely weight and diameter. Weight is represented on the y- axis and diameter on the x- axis. To begin with, there are 10 apples and 10 melons. Apples ( marked as red dots) would have low weight and small diameter whereas Melons (marked as green dots) would have high weight and large diameter.
Now suppose, there is a fruit which lies somewhere between these red and green dots. Which category is it likely to belong? The machine using the K-NN algorithm will draw a circle centred around this point and will classify it as an apple if there are more red dots in the circle or as a melon if there are more green dots.
It’s this simple, isn’t it? ML has evolved a lot over the time. Machine learning today is not like machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in artificial intelligence wanted to see if computers could learn from data. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development.
ML algorithms can be classified as Supervised , Unsupervised and Reinforcement learning. The basic essence of Supervised learning is that the machine is provided with a number of inputs and their desired output. The machine then learns the general rule which associates the input to the output. When provided a new input, the machine identifies its features and gives it the desired label. In unsupervised learning, the machine finds the given pattern in
the input on its own. Reinforcement learning is much more dynamic in the sense that machine performs the given task while constantly taking feedback in form of rewards and punishments.2 The K-NN algorithm discussed above is a type of supervised learning algorithm.
One needs to learn Linear Algebra, Statistics and Probability, Calculus, Graph theory, Programming skills such as Python ,R,C++ etc. as pre- requisites to learn ML.3
The use of machine learning is more ubiquitous than one might think. One sees facebook and other social media suggesting items that one views on online shopping apps?? And wonder, HOW? Well, that’s Machine Learning being used. Facebook's News Feed uses machine learning to personalize each member's feed. If a member frequently stops scrolling to read or like a particular friend's posts, the News Feed will start to show more of that friend's activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user's data and use those patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend's posts, that new data will be included in the data set and the News Feed will adjust accordingly.4
Netflix suggesting us various shows and series (and getting one hooked) based on what one views is also an application of ML. Google Photos where it recognizes faces, Google Lens where the ML image-text recognition model can extract text from the images you feed in, Gmail which categories E-mail as social, promotion, updates or forum using text classification are some
other applications of ML.5
Also, many people confuse ML with AI. Both are closely related but not the same. While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn.
At the end one can only say that ML is the future and one cannot ignore it.
Applications of Machine learning in Economics:
Machine learning along with Artificial Intelligence and Economics is a hot topic. One important area where ML is used is Prediction. Econometric models help in understanding casual relationship between economic variables. But when it comes to prediction, they often produce a large forecasting error. (SILVIA MERLER, 2018)6
For decades, economists have done -what they are best at- making assumptions about prices, wages, and inflation on data sets only as large as they or their research assistants could calculate. Machine learning has the potential to dramatically enlarge those data sets and allow economists to test their models faster than ever. (Edward Mason, 2018) 7
The software R is widely used by econometricians and data scientists for analysing huge sets of data. R is one of the most powerful machine learning platforms. Any techniques that you can think of for data analysis, visualization, sampling, supervised
learning and model evaluation are provided in R. The best thing about R is that you can download it for free and can start using it right now. It provides built in commands for basic statistics and data handing. The machine learning features of R come from third party packages. Packages are plug-ins to the R platform. One can search for, download and install them within the R environment. Each package will vary in quality as it is developed by a third party. One can search for best available packages which can serve one’s need. (Jason Brownlee , 2016) 8
Other areas where ML’s being used.
Government Government agencies such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.
Financial services Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cybersurveillance to pinpoint warning signs of fraud. 9
References:
1 “ ML | What is Machine Learning”Geeks for Geeks- A Computer Science Portal For Geeks. Retrieved, from https://www.geeksforgeeks.org/ml- machine-learning/
2 “ ML | What is Machine Learning”Geeks for Geeks- A Computer Science Portal For Geeks. Retrieved, from https://www.geeksforgeeks.org/ml- machine-learning/
3 “ ML | What is Machine Learning”Geeks for Geeks- A Computer Science Portal For Geeks. Retrieved, from https://www.geeksforgeeks.org/ml- machine-learning/
4 Margaret Rouse WhatIs.com “Machine learning (ML)” Retrived , from https://searchenterpriseai.techtarget.com/definition/machine- learning-ML
5 “ ML | What is Machine Learning”Geeks for Geeks- A Computer Science Portal For Geeks. Retrieved, from https://www.geeksforgeeks.org/ml- machine-learning/
6 SILVIA MERLER , November 29, 2018 “Innovation & Competition Policy”. Retrieved, from http://bruegel.org/2018/11/machine-learning-and-economics/
7 Edward Mason,Oct 29, 2018 “Why a leading economist is embracing machine learning” Retrieved, from https://mitsloan.mit.edu/ideas-made-to-matter/why-a-leading-economist- embracing-machine-learning
8 Jason Brownlee , January 15, 2016 “ R Machine Learning” Retrieved, from https://machinelearningmastery.com/use-r-for-machine- learning/
9 “Machine Learning- What it is and why it matters” Retrieved, from https://www.sas.com/en_in/insights/analytics/machine-learning.html