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MLB pulled its All Star Game Out Of Georgia Because of a new voter law.

Florida Governor Ron DeSantis and the Florida legislature have not yet faced sports backlash after the state placed restrictions on voting by mail and ballot drop boxes. But DeSantis and Florida risk losing events like the Super Bowl in South Florida or Tampa or an NBA All-Star Game in Miami or Orlando, or the NHL All-Star Game in South Florida or Tampa or a Major League Baseball or a Major League Soccer All-Star Game. Miami Dolphins owner Stephen Ross gets big local government money if he scores a big event for his stadium, Ross could be a big loser here if his fellow owners decide to send a message to Florida politicos. Sports owners know who their future consumers are. They saw them in the streets in the summer of 2020 protesting. Major League Baseball pulled its July All-Star Game out of Cobb County after Georgia passed a more restrictive voter rights bill in April.

The National Collegiate Athletic Association, the Atlantic Coast Conference and the Southeastern Conference do business in Florida. The NCAA has boycotted states before because of political legislation. The NCAA did not stage any post season events in South Carolina and North Carolina in the past because of issues such as the flying of the Confederate flag at the South Carolinas capital grounds in Columbia and a bathroom transgender law in North Carolina. The Florida legislature also passed a transgender law that bans transgender women and girls from female sports teams at the high school and college levels. The NCAA never weighed in on the Georgia voters rights restrictions. Sports owners cannot afford to fight culture wars because young people are increasingly uninterested in culture wars and eventually will be the majority sports consumers.

Evan Weiners books are available at iTunes https://books.apple.com/us/author/evan-weiner/id595575191

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How Will Sports React To Florida's New Voting Law? - Sports Talk Florida

Times Insider explains who we are and what we do, and delivers behind-the-scenes insights into how our journalism comes together.

Today, the Sunday print edition of The New York Times is a thick bundle of news and features, with enough information and diversions to while away the day. But it wasnt always this way. In fact, for the first 10 years of publication, The Times did not print a Sunday edition at all. The New-York Daily Times is published every morning, (Sunday excepted), read the first words of the first issue, on Sept. 18, 1851.

One of the biggest news stories imaginable would change that.

Many of the Sunday newspapers printed in the United States early in the 19th century were weekly editions. A daily Sunday paper filled with the news was not customary, and one big obstacle was the Christian Sabbath. Many worshipers did not want anything competing with the clergy, and new entries were often met with public backlash.

In New York, defenders of Sunday morals railed against anything that smacked of commerce. Vending, drinking establishments and especially trains large, loud and carrying the mail were frequent subjects of ire. Newspapers distracted the devoted. The Observer, The Sunday Courier and The Citizen of the World were three examples of early New York papers that had tried, and failed, to overcome the religious custom in New York, according to the book The Daily Newspaper in America by Alfred McClung Lee.

But in 1851, The Times was founded in a changing city. Sunday distribution was increasing, a trend since cheap dailies began appearing in American cities in the 1830s. The New York Herald had published a regular Sunday edition since 1841. According to Mr. Lee, James Gordon Bennett Sr., who founded The Herald, had learned from Bostons Sabbath rows in the 1820s that the American reader consumes most avidly that which he detests most blatantly.

More generally, Sunday mores were softening. For growing numbers of working class immigrants, Sunday was the only day off and spent socializing in festive public gatherings.

The Times supported the New York Sabbath Committee, a body of civic leaders and clergy members formed in 1857 to rescue Sunday morals and arrest particular forms of Sabbath desecration. That its core readership was upper class Anglo-Saxon society probably played a role. Alarm at fading religious mores appeared frequently in the early pages of The Times, which published letters with complaints about the clamor of trade and German lager houses operating on Sundays. It also reported on the fuss over boats using the Erie Canal on Sundays.

Since the Sabbath Committees first meeting on April 1, 1857, its doings were covered closely by The Times. One of the committees first moves was to write to the heads of the major railroads, through which traffic and travel and moral influences perpetually flow, about their Sunday passages in the city. Soon after (even before liquor), the committee went after the newsboys hawking papers. The Times reported that after an appeal by the committee to Sunday publishers failed to silence the vending, a police order had it suppressed.

The result of this action revealed the true power possessed by the Sunday press, for its course was condemned and the question settled that the Sabbath was a day that the strong arm of the law might keep sacred, read a Times article from a committee meeting in 1859.

If The Times, which was still edited by its co-founder Henry J. Raymond, was equivocating while more Sunday editions cropped up in New York, it wouldnt have to for much longer.

When South Carolina militia bombarded the U.S. Army at Fort Sumter on April 12, 1861, the country, and newspapers, were changed. And the Sabbath taboo, which had already been weakening, was essentially shattered.

By April 18, with Fort Sumter fallen and war apparent, The Times had to explain to readers who found the paper delivered late and the news stands sold out that we can only urge in excuse that our recent surge in circulation has been far more rapid than we were prepared for.

Two days later, subscribers were told to expect a special Sunday edition the following day.

The culture wars would not fully dissipate during the Civil War. The New York Sabbath Committee regretted that the Battle of Bull Run was fought on a Sunday, and worried that a generation of young soldiers would forget piety. But the news was urgent the United States was cracking up and by the second Sunday after Fort Sumter, The Times committed to a Sunday edition during the war excitement. It even announced that special trains will run over the Hudson River and New-Haven Railroads on Sunday morning, for the newspaper accommodation of the people along the line.

Once readers were accustomed to Sunday editions, there was no going back.

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When The Times Didnt Print on Sundays - The New York Times

AI, RPA, and machine learning, you must have heard these words echoing in the tech industry. Be it blogs, websites, videos, or even product descriptions, disruptive technologies have made their presence bold. The fact that we all have AI-powered devices in our homes is a sign that the technology has come so far.

If you are under the impression that AI, robotic process automation, and machine learning have nothing in common, then heres what you need to know, they are all related concepts. Oftentimes, people use these names interchangeably and incorrectly which causes confusion among businesses that are looking for the latest technological solutions.

Understanding the differences between AI, ML, and RPA tools will help you identify and understand where the best opportunities are for your business to make the right technological investment.

According to IBM, Robotic process automation (RPA), also known as software robotics, uses automation technologies to mimic back-office tasks of human workers, such as extracting data, filling in forms, moving files, etc. It combines APIs and user interface (UI) interactions to integrate and perform repetitive tasks between enterprise and productivity applications. By deploying scripts which emulate human processes, RPA tools complete autonomous execution of various activities and transactions across unrelated software systems.

In that sense, RPA tools enable highly logical tasks that dont require human understanding or human interference. For example, if your work revolves around inputting account numbers on a spreadsheet to run a report with a filter category, you can use RPA to fill the numbers on the sheet. Automation will mimic your actions of setting up the filter and generate the report on its own.

With a clear set of instructions, RPA can perform any task. But theres one thing to remember, RPA systems dont have the capabilities to learn as they go. If there is a change in your task, (for example if the filter has changed in the spreadsheet report), you will have to manually input the new set of instructions.

The highest adopters of this technology are banking firms, financial services, insurance, and telecom industries. Federal agencies like NASA have also started using RPA to automate repetitive tasks.

According to Microsoft, Artificial Intelligence is the ability of a computer system to deal with ambiguity, by making predictions using previously gathered data, and learning from errors in those predictions in order to generate newer, more accurate predictions about how to behave in the future.

In that sense, the major difference between RPA and AI is intelligence. While these technologies efficiently perform tasks, only AI can do it with similar capabilities to human intelligence.

Chatbots and virtual assistants are two popular uses of AI in the business world. In the tax industry, AI is making tax forecasting increasingly accurate with its predictive analytics capabilities. AI can also perform thorough data analysis which makes identifying tax deductions and tax credits easier than before.

According to Gartner, Advanced machine learning algorithms are composed of many technologies (such as deep learning, neural networks, and natural language processing), used in unsupervised and supervised learning, that operate guided by lessons from existing information.

Machine learning is a part of AI, so the two terms cannot be used interchangeably. And thats the difference between RPA and ML, machine learnings intelligence comes from AI but RPA lacks all intelligence.

To understand better, let us apply these technologies in a property tax scenario. First, you can create an ML model based on a hundred tax bills. The more bills you feed the model, the more accurately it will make predictions for the future bills. But if you want to use the same machine learning model to address an assessment notice, the model will be of no use. You would then have to build a new machine learning model that knows how to work with assessment notices. This is where machine learnings intelligence capabilities draw a line. Where ML fails to recognize the similarities of the document, an AI application would recognize it, thanks to its human-like interpretation skills.

The healthcare industry uses ML to accurately diagnose and treat patients, retailers use ML to make the right products available at the right stores at the right time, and pharmaceutical companies use machine learning to develop new medications. These are just a few use cases of this technology.

No, but they can work together. The combination of AI and RPA is called smart process automation, or SPA.

Also known as intelligent process automation or IPA, this duo facilitates an automated workflow with advanced capabilities than RPA using machine learning. The RPA part of the system works on doing the tasks while the machine learning part focuses on learning. In short, SPA solutions can learn to perform a specific task with the help of patterns.

The three technologies, AI, RPA, and ML, and the duet, SPA hold exciting possibilities for the future. But only when companies make the right choice, the rewards can be reaped. Now that you have an understanding of the various capabilities of these technologies, adapt and innovate.

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AI, RPA, and Machine Learning How are they Similar & Different? - Analytics Insight

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While there have been strides taken in filling up the gender gap across fields, especially engineering and technology-based, there are still miles to go. At times, though, it is due to part societal misconceptions and part lack of knowledge about different fields that we have a gap to fill. That said, what we need is information on all available career and educational prospects that help with choosing the path forward. One such option is machine learning. Machine learning (ML), for the uninitiated like me, is the science of getting computers ie the machines to study and behave like humans, and improve their learning over time automatically, from the fed information and data that comes in the form of observations and real-world interactions. It is a subset of artificial intelligence (AI).

Photo: Vaishali Kasture

With digitisation and AI being a huge part of the future, a career in ML could be successful and rewarding, as Vaishali Kasture, Leader Strategic Projects, AISPL, Amazon Web Services (AWS) India and South Asia, can attest to. Machine learning is one of the most disruptive technologies we will encounter in our generation. Were seeing ML adopted across all industries, verticals, and businesses. For example, Zomato uses machine learning for menu digitisation and enabling consumers to run advanced searches for dishes, and RedBus uses ML to improve click-through rates on their website by 25% and conversion rates by 5%.

Importance Of Machine Learning For The Future

In her over two-decade-old career, one thingKasture has realised is that technology is one of the most important driving factors in any business, be it banking where she started her career or the Knowledge Process Outsourcing (KPO) industry. Even when working at one of Indias prominent credit bureaus, she saw that technology was the key differentiator. There she used the cloud, machine learning and artificial intelligence to drive faster and better outcomes for our banking customers. This really opened my eyes to the power of the cloud and new emerging technologies, she notes, I am convinced that every business will be reimagined using new and emerging technologies, and only those that adapt and embrace this change will survive. She joined AWS in 2019 on the back of this conviction.

The AWS DeepRacer Womens League India 2021 is intentionally designed to create awareness of ML among women students in India, enable them to explore ML, learn collaboratively, and inspire them to take up careers in ML. We were delighted that over 17,000 women students from all corners of India showed interest to participate in the competition, she smiles. DeepRacer as the AWS website states is an autonomous 1/18th scale race car designed to test real-life models by racing them on a physical track. Using cameras to view the track and a reinforcement model to control throttle and steering, the car shows how a model trained in a simulated environment can be transferred to the real-world.

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ML proved to be useful in the current pandemic too! It is playing a key role in better understanding and addressing the COVID-19 pandemic. In the fight against the pandemic, organisations have been quick to apply their machine learning expertise in several areas including scaling customer communications, understanding how COVID-19 spreads and speeding up research and treatment.

Overcoming The Gender Disparity In Technology

Despitethe strides women have made in engineering, IT and beyond, there is still a gender gap in the field. Kasture gives a clear idea on what can be and should be done: At the grassroots level, there is a strong gender stereotype about women in STEM in general. We need to remove this stereotype. Encourage girls from a very young age in schools and colleges to opt for STEM programmes. Once women join the workforce, encourage them to actively raise their hands and ask for roles in hot technologies areas like ML, AI, analytics, augmented and virtual reality, blockchain, and quantum computing. Organisations need to partner with women, support, and reward them for working in new and emerging technologies. A mentoring programme to encourage women to participate in enhancing their knowledge and giving them an edge is also very useful. A knowledge series designed to give women deeper learning in a safe environment will go a long way.

Also read: 5 Indian Women Making Waves In The Field Of Science And Technology

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Future Calling: Machine Learning Is The Next Big Thing! - Femina

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Neurosurg Focus. 2021 May;50(5):E5. doi: 10.3171/2021.2.FOCUS201113.

ABSTRACT

OBJECTIVE: Frailty is recognized as an important consideration in patients with cancer who are undergoing therapies, including spine surgery. The definition of frailty in the context of spinal metastases is unclear, and few have studied such markers and their association with postoperative outcomes and survival. Using national databases, the metastatic spinal tumor frailty index (MSTFI) was developed as a tool to predict outcomes in this specific patient population and has not been tested with external data. The purpose of this study was to test the performance of the MSTFI with institutional data and determine whether machine learning methods could better identify measures of frailty as predictors of outcomes.

METHODS: Electronic health record data from 479 adult patients admitted to the Massachusetts General Hospital for metastatic spinal tumor surgery from 2010 to 2019 formed a validation cohort for the MSTFI to predict major complications, in-hospital mortality, and length of stay (LOS). The 9 parameters of the MSTFI were modeled in 3 machine learning algorithms (lasso regularization logistic regression, random forest, and gradient-boosted decision tree) to assess clinical outcome prediction and determine variable importance. Prediction performance of the models was measured by computing areas under the receiver operating characteristic curve (AUROCs), calibration, and confusion matrix metrics (positive predictive value, sensitivity, and specificity) and was subjected to internal bootstrap validation.

RESULTS: Of 479 patients (median age 64 years [IQR 55-71 years]; 58.7% male), 28.4% had complications after spine surgery. The in-hospital mortality rate was 1.9%, and the mean LOS was 7.8 days. The MSTFI demonstrated poor discrimination for predicting complications (AUROC 0.56, 95% CI 0.50-0.62) and in-hospital mortality (AUROC 0.69, 95% CI 0.54-0.85) in the validation cohort. For postoperative complications, machine learning approaches showed a greater advantage over the logistic regression model used to develop the MSTFI (AUROC 0.62, 95% CI 0.56-0.68 for random forest vs AUROC 0.56, 95% CI 0.50-0.62 for logistic regression). The random forest model had the highest positive predictive value (0.53, 95% CI 0.43-0.64) and the highest negative predictive value (0.77, 95% CI 0.72-0.81), with chronic lung disease, coagulopathy, anemia, and malnutrition identified as the most important predictors of postoperative complications.

CONCLUSIONS: This study highlights the challenges of defining and quantifying frailty in the metastatic spine tumor population. Further study is required to improve the determination of surgical frailty in this specific cohort.

PMID:33932935 | DOI:10.3171/2021.2.FOCUS201113

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Performance assessment of the metastatic spinal tumor frailty index using machine learning algorithms: limitations and future directions - DocWire...