Machine learning has fundamentally changed how we engage with technology. Today, it's able to curate social media feeds, recognize complex images, drive cars down the interstate, and even diagnose medical conditions, to name a few tasks.

But while machine learning technology can do some things automatically, it still requires a lot of input from human engineers to set it up, and point it in the right direction. Inevitably, that means human biases and limitations are baked into the technology.

So, what if scientists could minimize their influence on the process by creating a system that generates its own machine-learning algorithms? Could it discover new solutions that humans never considered?

To answer these questions, a team of computer scientists at Google developed a project called AutoML-Zero, which is described in a preprint paper published on arXiv.

"Human-designed components bias the search results in favor of human-designed algorithms, possibly reducing the innovation potential of AutoML," the paper states. "Innovation is also limited by having fewer options: you cannot discover what you cannot search for."

Automatic machine learning (AutoML) is a fast-growing area of deep learning. In simple terms, AutoML seeks to automate the end-to-end process of applying machine learning to real-world problems. Unlike other machine-learning techniques, AutoML requires relatively little human effort, which means companies might soon be able to utilize it without having to hire a team of data scientists.

AutoML-Zero is unique because it uses simple mathematical concepts to generate algorithms "from scratch," as the paper states. Then, it selects the best ones, and mutates them through a process that's similar to Darwinian evolution.

AutoML-Zero first randomly generates 100 candidate algorithms, each of which then performs a task, like recognizing an image. The performance of these algorithms is compared to hand-designed algorithms. AutoML-Zero then selects the top-performing algorithm to be the "parent."

"This parent is then copied and mutated to produce a child algorithm that is added to the population, while the oldest algorithm in the population is removed," the paper states.

The system can create thousands of populations at once, which are mutated through random procedures. Over enough cycles, these self-generated algorithms get better at performing tasks.

"The nice thing about this kind of AI is that it can be left to its own devices without any pre-defined parameters, and is able to plug away 24/7 working on developing new algorithms," Ray Walsh, a computer expert and digital researcher at ProPrivacy, told Newsweek.

If computer scientists can scale up this kind of automated machine-learning to complete more complex tasks, it could usher in a new era of machine learning where systems are designed by machines instead of humans. This would likely make it much cheaper to reap the benefits of deep learning, while also leading to novel solutions to real-world problems.

Still, the recent paper was a small-scale proof of concept, and the researchers note that much more research is needed.

"Starting from empty component functions and using only basic mathematical operations, we evolved linear regressors, neural networks, gradient descent... multiplicative interactions. These results are promising, but there is still much work to be done," the scientists' preprint paper noted.

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New AI improves itself through Darwinian-style evolution - Big Think

Paramedics bring a patient into the emergency center at Maimonides Medical Center in the Brooklyn, NY. April 14, 2020. Brendan McDermid

The COVID-19 pandemic is raising critical questions regarding the dynamics of the disease, its risk factors, and the best approach to address it in healthcare systems. MIT Sloan School of Management Prof. Dimitris Bertsimas and nearly two dozen doctoral students are using machine learning and optimization to find answers. Their effort is summarized in the COVIDanalytics platform where their models are generating accurate real-time insight into the pandemic. The group is focusing on four main directions; predicting disease progression, optimizing resource allocation, uncovering clinically important insights, and assisting in the development of COVID-19 testing.

The backbone for each of these analytics projects is data, which weve extracted from public registries, clinical Electronic Health Records, as well as over 120 research papers that we compiled in a new database. Were testing our models against incoming data to determine if it makes good predictions, and we continue to add new data and use machine-learning to make the models more accurate, says Bertsimas.

The first project addresses dilemmas at the front line, such as the need for more supplies and equipment. Protective gear must go to healthcare workers and ventilators to critically ill patients. The researchers developed an epidemiological model to track the progression of COVID-19 in a community, so hospitals can predict surges and determine how to allocate resources.

The team quickly realized that the dynamics of the pandemic differ from one state to another, creating opportunities to mitigate shortages by pooling some of the ventilator supply across states. Thus, they employed optimization to see how ventilators could be shared among the states and created an interactive application that can help both the federal and state governments.

Different regions will hit their peak number of cases at different times, meaning their need for supplies will fluctuate over the course of weeks. This model could be helpful in shaping future public policy, notes Bertsimas.

Recently, the researchers connected with long-time collaborators at Hartford HealthCare to deploy the model, helping the network of seven campuses to assess their needs. Coupling county level data with the patient records, they are rethinking the way resources are allocated across the different clinics to minimize potential shortages.

The third project focuses on building a mortality and disease progression calculator to predict whether someone has the virus, and whether they need hospitalization or even more intensive care. He points out that current advice for patients is at best based on age, and perhaps some symptoms. As data about individual patients is limited, their model uses machine learning based on symptoms, demographics, comorbidities, lab test results as well as a simulation model to generate patient data. Data from new studies is continually added to the model as it becomes available.

We started with data published in Wuhan, Italy, and the U.S., including infection and death rate as well as data coming from patients in the ICU and the effects of social isolation. We enriched them with clinical records from a major hospital in Lombardy which was severely impacted by the spread of the virus. Through that process, we created a new model that is quite accurate. Its power comes from its ability to learn from the data, says Bertsimas.

By probing the severity of the disease in a patient, it can actually guide clinicians in congested areas in a much better way, says Bertsimas.

Their fourth project involves creating a convenient test for COVID-19. Using data from about 100 samples from Morocco, the group is using machine-learning to augment a test previously designed at the Mohammed VI Polytechnic University to come up with more precise results. The model can accurately detect the virus in patients around 90% of the time, while false positives are low.

The team is currently working on expanding the epidemiological model to a global scale, creating more accurate and informed clinical risk calculators, and identifying potential ways that would allow us to go back to normality.

We have released all our source code and made the public database available for other people too. We will continue to do our own analysis, but if other people have better ideas, we welcome them, says Bertsimas.

Excerpt from:

Research Team Uses Machine Learning to Track Covid-19 Spread in Communities and Predict Patient Outcomes - The Ritz Herald

As the fuel that powers their progressing digital transformation endeavors, organizations wherever are searching for approaches to determine as much insight as could reasonably be expected from their data. The accompanying increased demand for advanced predictive and prescriptive analytics has, thus, prompted a call for more data scientists capable with the most recent artificial intelligence (AI) and machine learning (ML) tools.

However, such highly-skilled data scientists are costly and hard to find. Truth be told, theyre such a valuable asset, that the phenomenon of the citizen data scientist has of late emerged to help close the skills gap. A corresponding role, as opposed to an immediate substitution, citizen data scientists need explicit advanced data science expertise. However, they are fit for producing models utilizing best in class diagnostic and predictive analytics. Furthermore, this ability is incomplete because of the appearance of accessible new technologies, for example, automated machine learning (AutoML) that currently automate a significant number of the tasks once performed by data scientists.

The objective of autoML is to abbreviate the pattern of trial and error and experimentation. It burns through an enormous number of models and the hyperparameters used to design those models to decide the best model available for the data introduced. This is a dull and tedious activity for any human data scientist, regardless of whether the individual in question is exceptionally talented. AutoML platforms can play out this dreary task all the more rapidly and thoroughly to arrive at a solution faster and effectively.

A definitive estimation of the autoML tools isnt to supplant data scientists however to offload their routine work and streamline their procedure to free them and their teams to concentrate their energy and consideration on different parts of the procedure that require a more significant level of reasoning and creativity. As their needs change, it is significant for data scientists to comprehend the full life cycle so they can move their energy to higher-value tasks and sharpen their abilities to additionally hoist their value to their companies.

At Airbnb, they continually scan for approaches to improve their data science workflow. A decent amount of their data science ventures include machine learning and numerous pieces of this workflow are tedious. At Airbnb, they use machine learning to build customer lifetime value models (LTV) for guests and hosts. These models permit the company to improve its decision making and interactions with the community.

Likewise, they have seen AML tools as generally valuable for regression and classification problems involving tabular datasets, anyway, the condition of this area is rapidly progressing. In outline, it is accepted that in specific cases AML can immensely increase a data scientists productivity, often by an order of magnitude. They have used AML in many ways.

Unbiased presentation of challenger models: AML can rapidly introduce a plethora of challenger models utilizing a similar training set as your incumbent model. This can help the data scientist in picking the best model family. Identifying Target Leakage: In light of the fact that AML builds candidate models amazingly fast in an automated way, we can distinguish data leakage earlier in the modeling lifecycle. Diagnostics: As referenced prior, canonical diagnostics can be automatically created, for example, learning curves, partial dependence plots, feature importances, etc. Tasks like exploratory data analysis, pre-processing of data, hyper-parameter tuning, model selection and putting models into creation can be automated to some degree with an Automated Machine Learning system.

Companies have moved towards enhancing predictive power by coupling huge data with complex automated machine learning. AutoML, which uses machine learning to create better AI, is publicized as affording opportunities to democratise machine learning by permitting firms with constrained data science expertise to create analytical pipelines equipped for taking care of refined business issues.

Including a lot of algorithms that automate that writing of other ML algorithms, AutoML automates the end-to-end process of applying ML to real-world problems. By method for representation, a standard ML pipeline consists of the following: data pre-processing, feature extraction, feature selection, feature engineering, algorithm selection, and hyper-parameter tuning. In any case, the significant ability and time it takes to execute these strides imply theres a high barrier to entry.

In an article distributed on Forbes, Ryohei Fujimaki, the organizer and CEO of dotData contends that the discussion is lost if the emphasis on AutoML systems is on supplanting or decreasing the role of the data scientist. All things considered, the longest and most challenging part of a typical data science workflow revolves around feature engineering. This involves interfacing data sources against a rundown of wanted features that are assessed against different Machine Learning algorithms.

Success with feature engineering requires an elevated level of domain aptitude to recognize the ideal highlights through a tedious iterative procedure. Automation on this front permits even citizen data scientists to make streamlined use cases by utilizing their domain expertise. More or less, this democratization of the data science process makes the way for new classes of developers, offering organizations a competitive advantage with minimum investments.

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Automated Machine Learning is the Future of Data Science - Analytics Insight

The research described in this article has been published on a preprint server but has not yet been peer-reviewed by scientific or medical experts.

Every day for the past few weeks, charts and graphs plotting the projected apex of Covid-19 infections have been splashed across newspapers and cable news. Many of these models have been built using data from studies on previous outbreaks like SARS or MERS. Now, a team of engineers at MIT has developed a model that uses data from the Covid-19 pandemic in conjunction with a neural network to determine the efficacy of quarantine measures and better predict the spread of the virus.

Our model is the first which uses data from the coronavirus itself and integrates two fields: machine learning and standard epidemiology, explains Raj Dandekar, a PhD candidate studying civil and environmental engineering. Together with George Barbastathis, professor of mechanical engineering, Dandekar has spent the past few months developing the model as part of the final project in class 2.168 (Learning Machines).

Most models used to predict the spread of a disease follow what is known as the SEIR model, which groups people into susceptible, exposed, infected, and recovered. Dandekar and Barbastathis enhanced the SEIR model by training a neural network to capture the number of infected individuals who are under quarantine, and therefore no longer spreading the infection to others.

The model finds that in places like South Korea, where there was immediate government intervention in implementing strong quarantine measures, the virus spread plateaued more quickly. In places that were slower to implement government interventions, like Italy and the United States, the effective reproduction number of Covid-19 remains greater than one, meaning the virus has continued to spread exponentially.

The machine learning algorithm shows that with the current quarantine measures in place, the plateau for both Italy and the United States will arrive somewhere between April 15-20. This prediction is similar to other projections like that of the Institute for Health Metrics and Evaluation.

Our model shows that quarantine restrictions are successful in getting the effective reproduction number from larger than one to smaller than one, says Barbastathis. That corresponds to the point where we can flatten the curve and start seeing fewer infections.

Quantifying the impact of quarantine

In early February, as news of the virus troubling infection rate started dominating headlines, Barbastathis proposed a project to students in class 2.168. At the end of each semester, students in the class are tasked with developing a physical model for a problem in the real world and developing a machine learning algorithm to address it. He proposed that a team of students work on mapping the spread of what was then simply known as the coronavirus.

Students jumped at the opportunity to work on the coronavirus, immediately wanting to tackle a topical problem in typical MIT fashion, adds Barbastathis.

One of those students was Dandekar. The project really interested me because I got to apply this new field of scientific machine learning to a very pressing problem, he says.

As Covid-19 started to spread across the globe, the scope of the project expanded. What had originally started as a project looking just at spread within Wuhan, China grew to also include the spread in Italy, South Korea, and the United States.

The duo started modeling the spread of the virus in each of these four regions after the 500th case was recorded. That milestone marked a clear delineation in how different governments implemented quarantine orders.

Armed with precise data from each of these countries, the research team took the standard SEIR model and augmented it with a neural network that learns how infected individuals under quarantine impact the rate of infection. They trained the neural network through 500 iterations so it could then teach itself how to predict patterns in the infection spread.

Using this model, the research team was able to draw a direct correlation between quarantine measures and a reduction in the effective reproduction number of the virus.

The neural network is learning what we are calling the quarantine control strength function, explains Dandekar. In South Korea, where strong measures were implemented quickly, the quarantine control strength function has been effective in reducing the number of new infections. In the United States, where quarantine measures have been slowly rolled out since mid-March, it has been more difficult to stop the spread of the virus.

Predicting the plateau

As the number of cases in a particular country decreases, the forecasting model transitions from an exponential regime to a linear one. Italy began entering this linear regime in early April, with the U.S. not far behind it.

The machine learning algorithm Dandekar and Barbastathis have developed predictedthat the United States will start to shift from an exponential regime to a linear regime in the first week of April, with a stagnation in the infected case count likely betweenApril 15 and April20. It also suggests that the infection count will reach 600,000 in the United States before the rate of infection starts to stagnate.

This is a really crucial moment of time. If we relax quarantine measures, it could lead to disaster, says Barbastathis.

According to Barbastathis, one only has to look to Singapore to see the dangers that could stem from relaxing quarantine measures too quickly. While the team didnt study Singapores Covid-19 cases in their research, the second wave of infection this country is currently experiencing reflects their models finding about the correlation between quarantine measures and infection rate.

If the U.S. were to follow the same policy of relaxing quarantine measures too soon, we have predicted that the consequences would be far more catastrophic, Barbastathis adds.

The team plans to share the model with other researchers in the hopes that it can help inform Covid-19 quarantine strategies that can successfully slow the rate of infection.

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Model quantifies the impact of quarantine measures on Covid-19's spread - MIT News

MELBOURNE, Fla.Qligent is now sharing its second-generation, cloud-based predictive analysis platform Foresight, which uses AI, machine learning and big data to handle content distribution issues. Foresight is designed to provide real-time 24/7 data analytics based on system performance and user behavior.

The Foresight platform aggregates data points from end user equipment, including set-top boxes, smart TVs and iOS and Android devices, as well as CDN logs, stream monitoring data, CRMs, support ticketing systems, network monitoring systems and other hardware monitoring systems.

With scalable cloud processing, Foresights integrated AI and machine learning provide automated data collection, while deep learning technology mines data from layers of data. Big data technology then correlates and aggregates the data for quality assurance.

Foresight features networked and virtual probes that create a controlled data mining environment, which Qligent says is not compromised by operator error, viewer disinterest, user hardware malfunction or other variables.

Users can access customizable reports that summarize key performance indicators, key quality indicators and other criteria for multiplatform content distribution. All findings are presented on Qligents dashboard, which is accessible on a computer or mobile device.

The Qligent Foresight system is available immediately. For more information, visit http://www.qligent.com.

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Qligent Foresight Released as Predictive Analysis Tool - TV Technology

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As companies amass more data than ever, the employees best able to interpret it and apply key insights to important decision-making processes become increasingly valuable.

But while the skillset grows more desirable, the supply of workers with the correct skills isn't sufficient making data science skills among the most in-demand hard skills in 2020, according to LinkedIn's research.

Thankfully, there are plenty of online learning opportunities to help you prepare for a career in data science, whether it's a course that helps you master a specific skill or an intensive year-long program that helps you jump up the ladder in your current role. Many classes are offered by top schools such as Harvard and MIT, and many programs were designed by major companies like IBMand Google specifically for educating a useful future workforce. Some of them offer students the opportunity to join their talent network after completing a specific course level.

Below are a few of the most popular data science options online, including MicroMasters, professional certificates, and individual courses.

Professional certificates are bundles of related courses that help you master a specific skill, and they tend to be most useful for breaking into a new industry or getting you to the next level of your career. They can take anywhere from a few months to more than a year to complete. At Coursera, professional certificate programs typically have a 7-day free trial and a monthly fee afterward. So, the faster you complete it, the more money you'll save. At edX, professional certificates typically have a flat one-time fee.

MicroMasters are a bundle of graduate-level courses that are designed to help you advance your career. Students have the option of applying to the university that's offering credit for the MicroMasters program certificate and, if accepted, can pursue an accelerated and less expensive Master's Degree. You can learn more here.

If you end up taking a Coursera course, and you think you'll realistically spend more than $399 in monthly fees or on individual classes throughout the year, you may want to consider Coursera Plus if all the courses and programs you plan to take are included in the annual membership (90% of the site is). And, if your employer offers to cover educational costs that include online-learning programs, you may even be able to get reimbursed for the following courses.

Read this article:

10 of the best affordable online data science courses and programs - Business Insider - Business Insider

A couple of decades ago, Jeff Alger, then a senior manager at Microsoft, was coaching state-level soccer teams and realised that there was very little science to player development.

There were no objective ways of measuring how good players are, Alger said, and without being able to measure, you have nothing.

He said it offended his sense of systems design to recognise a problem but do nothing about it, so he quit his job, got a masters degree in sports management and started a company that would use artificial intelligence (AI) to assess athletic talent and training.

His company, Seattle Sports Sciences, is one of a handful using the pattern-recognising power of machine learning to revolutionise coaching and make advanced analytics available to teams of all kinds.

The trend is touching professional sports and changing sports medicine. And, perhaps inevitably, it has altered the odds in sports betting.

John Milton, architect of Seattle Sports Sciences artificial intelligence system, spent a week in October with Spanish soccer team Mlaga, which plays in Spains second division, capturing everything that happened on the pitch with about 20 synchronised cameras in 4K ultra high-definition video.

Its like omniscience, Milton said. The system, ISOTechne, evaluates a players skill and consistency and who is passing or receiving with what frequency, as well as the structure of the teams defence. It even tracks the axis of spin and rate of rotation of the ball.

That is not the only way that the companys technology is being used. Professional soccer teams derive a growing slice of revenue from selling players. Soccer academies have become profit centers for many teams as they develop talented players and then sell them to other teams. It is now a $7 billion business. But without objective measurements of a players ability, putting a value on an athlete is difficult.

Its a matter of whether that players movements and what they do with the ball correspond to the demands that they will have on your particular team, said Alger, now the President and Chief Executive of Seattle Sports Sciences. He said, for example, that his company could identify a player who was less skilled at other phases of the game but was better at delivering the ball on a corner kick or a free kick a skill that a coach could be looking for.

Some systems can also detect and predict injuries. Dr Phil Wagner, Chief Executive and founder of Sparta Science, works from a warehouse in Silicon Valley that has a running track and is scattered with equipment for assessing athletes physical condition.

The company uses machine learning to gather data from electronic plates on the ground that measure force and balance. The system gathers 3,000 data points a second and a test jumping or balancing takes about 20 seconds.

Athletes dont recognise that theres an injury coming or theres an injury that exists, Wagner said, adding that the system has a proven record of diagnosing or predicting injury. Were identifying risk and then providing the best recommendation to reduce that risk.

Tyson Ross, a pitcher competing for a roster spot with the San Francisco Giants, has been using Sparta Sciences system since he was drafted in 2008. He visits the companys facilities roughly every other week during the offseason to do vertical jumps, sway tests, a single leg balance test and a one-arm plank on the plate, blindfolded.

Based on the data thats collected, it tells me how Im moving compared to previously and how Im moving compared to my ideal movement signature, as they call it, Ross said. Sparta Science then tailors his workouts to move him closer to that ideal.

The Pittsburgh Steelers, the Detroit Lions and the Washington Redskins, among others, use the system regularly, Wagner said. Sparta Science is also used to evaluate college players in the National Football Leagues annual scouting combine.

Of course, it is inevitable that machine learnings predictive power would be applied to another lucrative end of the sports industry: betting. Sportlogiq, a Montreal-based firm, has a system that primarily relies on broadcast feeds to analyse players and teams in hockey, soccer, football and lacrosse.

Mehrsan Javan, the companys Chief Technology Officer and one of its co-founders, said the majority of National Hockey League teams, including the last four Stanley Cup champions, used Sportlogiqs system to evaluate players.

Josh Flynn, Assistant General Manager for the Columbus Blue Jackets, Ohios professional hockey franchise, said the team used Sportlogiq to analyse players and strategy. We can dive levels deeper into questions we have about the game than we did before, Flynn said.

But Sportlogiq also sells analytic data to bookmakers in the United States, helping them set odds on bets, and hopes to sell information to individual bettors soon. Javan is looking to hire a vice president of betting.

They key to all of this sports-focused technology is data.

Algorithms come and go, but data is forever, Alger is fond of saying. Computer vision systems have to be told what to look for, whether it be tumours in an X-ray or bicycles on the road. In Seattle Sports Sciences case, the computers must be trained to recognise the ball in various lighting conditions as well as understand which plane of the foot is striking the ball.

To do that, teams of workers first have to painstakingly annotate millions of images. The more annotated data, the more accurate the machine-learning analysis will be. Basically, whoever has the most labelled data wins, said Milton, the AI architect.

Seattle Sports Sciences uses Labelbox, a training data platform that allows Miltons data science team in Seattle to work with shifts of workers in India who label data 24 hours a day. Thats how fast you have to move to compete in modern vision AI, Milton said. Its basically a labelling arms race.

Wagner of Sparta Science agrees, noting that with algorithms readily available and cloud computing power now available everywhere, the differentiator is data. He said it took Sparta Science 10 years to build up enough data to train its machine-learning system adequately.

Sam Robertson, who runs the sports performance and business programme at Victoria University in Melbourne, Australia, said it would take time for the technology to transform sports. The decision-making component of this right now is still almost exclusively done by humans, he said.

We need to work on the quality of the inputs, he said, meaning the labelled data. Thats whats going to improve things.

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Want to Be Better at Sports? Listen to the Machines - Moneycontrol

A Great Historian, statesman and writer, Winston S. Churchill, is quoted on many occasions during his illustrious career on the subjects of history and politics:

"The Farther Backward You Can Look, The Farther Forward You Can See"

Our present COVID-19 pandemic is fearful for all Americans during during this spring season. Our Easter and Passover holidays celebrate the renewal of life and of new beginnings during the spring. America has not been through a serious worldwide pandemic for over 100 years. In 2020, America now has the advantages of over 100 years with state of the art advances in science, medical, computer, mass communication, technologies, etc. combined with the required skills, knowledge, talents and historical memory and data unheard of and unknown in 1918. In 2020, America must wage a common cause of nonpartisan total warfare against the COVID-19 pandemic. Moreover, America has 100 years of accumulated wisdom and knowledge on how to wage an effective war on modern pandemics.

The so-called "Spanish Flu" of 1918 (which actually began in America!) killed more than 500,000 Americans more deaths than caused by WWI of American Soldiers in 1917-1918. The estimated, staggering and still unbelievable, death toll from the Spanish Flu number at least 50 million deaths.

A short historical comparison/analysis on the communication of knowledge and know-how between worldwide pandemics in 1918 versus 2020:

In 1918: America had nationwide telegraph and cable transmissions. However, public telephone usage was in its infancy and there were no public national or local radio broadcast stations nor radios for public use in 1918. In 1918, there were no national or worldwide mass communication networks for the dissemination of data, information, news to the public and only paper-printed newspapers, weekly magazines and periodicals for distribution and use.

In 2020: America has mass communications for social media distribution, use of satellite communications to transfer data and information and use of worldwide internet for 24/7 news, TV and radio, news in electronic and print form, etc. All of which were unheard of nor were available to wage total war on worldwide health and medical pandemics in 1918.

George Bernard Shaw (who was a great writer and a good friend of Winston S. Churchill) is quoted on whether or not "history repeats itself":

"If history repeats itself, and the unexpected always happens, how incapable must man be of learning from experience?"

Finally, there is no easy way out of the COVID-19 pandemic except time and patience, and a vaccine, however, the essential question remains to be answered: "When?"

The fear of the unknown is a terrible fear.

Weintraub writes from Stroudsburg, Pa.

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For What Its Worth: The fear of the unknown is a terrible fear - Pocono Record

April is alcohol awareness month, and in the spirit of things, it seems that Americans are reacting to shelter-in-place orders by drinking more wine.

Well leave the reason for skyrocketing sales of Chardonnay to what playwright George Bernard Shaw said about it in the past, Grit Daily readers.

Shaw, who famously quipped, Alcohol is the anesthesia by which we endure the operation of life, aptly described what we are seeing today. In other words, people are turning to their alcoholic drink of choice as a coping mechanism for the extreme worry and isolation that goes hand-in-hand with the pandemic quarantine.

C.Vaile Wright, director of clinical research and quality in the Practice Directorate for the American Psychological Association,spoke out recentlyabout why the stress COVID-19 is leaving in its wake is causing you to reach for that soothing glass of wine.

Wright said,

Stress is a common trigger for drinking, and this pandemic has led to an unprecedented period of stress. We are seeing more people using alcohol as a way to cope with the anxiety and stress and uncertainty of this situation.

I think a lot of people use it to numb out, Wright added. She also cautioned against self-medicating yourself with alcohol, indicating that it isnt the best way to fight stress. She went on to say,

For one thing, alcohol is a depressant, both physically and mentally, so those already prone to depression or sadness may find those feelings exacerbated by drinking.

And it can actually increase ones anxiety because it interferes with the ability to get quality sleep at night. Without quality sleep, it can be hard to manage stress the next day.

If you are part of the shelter-in-place scenario, it sometimes feels as if wine and other alcoholic beverages are essential items. Of course, they are not, but many isolated people such as Noelle Farrara are turning en masse to them.

Noelle Farrara is a human resources officer who lives in New Jersey. She told reporters that cocktails definitely assuaged her anxiety. Since the pandemic unfolded in her hard-hit state, she indicated that the cocktail she normally drank on the weekend has morphed into one or sometimes two that she has every night.

The human resources officer blamed a real fear on the virus, coupled with fear manufactured by the media, for her unease. She said, Getting to the end of the day without paralyzing anxiety is almost a goal. I reward myself with a glass of wine or light beer.

Recently, she realized that she had a new worry to think about. And that was that her husband might run out of wine.

Thats not a thought that wouldve cropped up in the past, Farrara said.

Alcohol represents one of the few creature comforts that are still obtainable. Whether its because its a good way to take the edge off or because people are maintaining some semblance of a social life through virtual happy hours on Zoom, wine sales shot up66 percent.

One liquor store owner Erik Conley said that some vino is selling better than others. Conley, who owns Conleys Wine and Spirits with his wife Kate, told Grit Daily that customers like to hunker down with white wines the most during the shelter-in-place orders.

Conley said go-to brands of choice for them included Chateau Ste Michelle (Riesling), Dark Horse (Rose), Manu (Pinot Grigio), McManis (Chardonnay), Josh (Sauvignon Blanc) and Blindfold White Blend by the Prisoner Wine Company.

Another survey reported that in the week ending March 21, off-premises sales of all alcoholic beverages in the U.S. grew 55 percent. Whats more, the online purchases of beer, wine, and spirits were up 243 percent more than they were the same week of the previous year.

The growth is most likely attributed to bars and restaurants that closed their doors due to the pandemic. COVID-19 has been tough on the industry. Its aftermath left many bars, restaurant owners, tasting rooms and liquor stores fighting for survival.

Many had to move fast to stay in business. Some shifted their focus to home delivery in areas that it is legal. While others worked to get online sites that facilitate alcohol purchases live.

When the governor of Pennsylvaniaordered liquor storesto shut their doors, customers flooded the establishments. They emptied the stores in record time. In response to that void, a site geared to facilitate online alcohol purchases went live. Unfortunately, it proceeded to crash soon after because of the high demand.

Is the rush for wine and other spirits to pass the time going to lead to a wave of alcoholics? That isnt likely to happen, according toColumbia University neuroscientist Carl Hart.Hart said there is no indication that a wave of alcohol addiction will follow this pandemic. And new cases of addiction will be caused by the devastation the pandemic inflicted. Not because of one drink too many.

Thats good news for us oenophiles. However, remember to consume everything in moderation except for Grit Daily articles extolling the virtues of wine. Reading them helps you chill, and you can consume as many of them as you like.

See more here:
America Reacts To Shelter-in-place Orders By Drinking More Wine - Grit Daily

I started reading MAD on a regular basis starting around the 2nd grade back in 1981. Especially the parodies of the great Mort Drucker, who died on April 8th in his Woodbury, NY home at age 91.

I remember how dense it was to get throughit would take me literally the entire month to read. And I had like this rhythm to reading it. Id always, always start at Spy Vs. Spy, then the Don Martin page before checking out what kind of super fresh 70s clothing that the folks on Dave Bergs The Lighter Side were rocking. My curiosity about the foldable folly on the back cover would usually get the best of me as well, rendering every issue I ever owned worthless on the collectors market.

But I saved the best part for last. The movie spoofs. One of the very first issues I picked up with my allowance money was the January 81 edition, which featured Alfred E. Newmans iconic face drawn on the body of Yoda. I got it strictly for The Empire Strikes Out, the very parody that would spark the ire of Star Wars creator George Lucas, or rather his team of lawyers.

What piqued my particular interest in this parody, however was not just my undying love of all things Star Wars. Albeit unwittingly because I was eight, it was the first time I got to read a Mort Drucker film parody penned by writer Dick DeBartolo. The first time Drucker and DeBartolo worked together was Issue 98 from October 196 on Flapper, a super funny piss-take on the beloved porpoise sitcom Flipper. And from that issue until their last collaboration in 2009, the duo spoofed hundreds of shows and movies across five decades. Oftentimes their targets were the summer blockbusters of that year, be it The Flying Ace or The Towering Inferno or Alien or Robocop or even Teenage Mutant Ninja Turtles.

The team of Mort Drucker and Dick DeBartolo were so good at what they did that George Lucas himself wound up writing a letter to MAD Magazine. Only this time it wasnt to cease and desist but rather hail the duo as the Leonardo da Vinci and George Bernard Shaw of comic satire.

Here are five Drucker/DeBartolo productions that, as a lifelong reader of MAD, I believe serve as ample justification for such a bold declaration.

Even a cinematic mind as brilliant as Stanley Kubrick has been called an idiot. But in the case of the iconic director of The Killing and Full Metal Jacket, it was the team of Drucker and DeBartolo who first did the honors in this purposefully prolonged and drawn-out pisstake on the Oscar winning space epic. And, as it turns out, that giant monolith turns out to be a book entitled How To Make An Incomprehensible Sci-Fi Movie and Several Million Dollars.

Milos Formans asylum life masterpiece was turnkey ready for a satirical spin the moment it hit box offices nationwide. But the way by which Drucker and DeBartolo not only lampooned Cuckoo but cranked up the films manic energy to 11 gave the story a whole new dimension. It was features like this that made these guys the dream team of MADison Avenue.

None of the flicks parodied in this issue dated October 1982 are not exactly household favorites (though Im sure there are cult audiences for each of them). The only reason I remember On Golden Pond was on account of all the old folks taking up room on line at the LOEWS box office while I was going to see Clash of the Titans for the 10th time, and Death Wish II because Jimmy Page did the soundtrack. As for Deathtrap, I owe it to myself as a lifelong Michael Caine fan to give it a revisit. However, what makes this particular edition so special is that its the only issue of MAD to feature three Drucker/DeBartolo spoofs between its pages, making this a highly sought-after collectors item for MAD folk.

As a sixth grader in 1985, Back To The Future hit my demo like an atom bomb. And indeed Issue no. 260 was high on my list when I picked it up at my local Waldenbooks. Having seen the Robert Zemeckis-directed sci-fi comedy classic at least twice before I copped this edition of MAD, I felt especially prepared for anything DeBartolo and Drucker was gonna throw our way. But leave it to them to pretty much whittle the entire premise of the movie down to the bizarre love triangle between Marquee McFly and his parents. Not to mention a few jabs at producer Steven Spielberg for good measure.

Drucker and DeBartolo worked together only a handful more times before turning in their last feature in 2009 (a sendup of the Chronicles of Narnia). However, clearly their best work during this time was the pairs return to doing what they did bestbeating up George Lucas. And their parody of The Phantom Menace spoke to so many of us who grew up on these fellas poking fun at the Star Bores saga our whole lives. Only there was a shared sense of cynicism between reader and creators because most of us felt gypped by this movie, as awash of Gungan jibjab piss away any new hopes of a classic Star Wars chapter.

Rest in Peace, Mort Drucker. Thank you for your part in my idiocy. I mean literacy.

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The Best of Drucker and DeBartolo in MAD - Book and Film Globe