AI and Employee experience| How does it positively go together?

Do you know 62% of workforce believe that AI will carry a favourable impact on their jobs?

And 67% say it is necessary to develop the skills for working in coordination with intelligent machines.

As per the research studies, AI will be the driving force behind cultural and economic shifts that will make the workforce more productive and agile. As AI plays a major role in the organisation of actively listening and understanding the perspective of employees. It allows the company to determine what exactly an employee wants and provide suitable information, such as career path recommendations and coaching. With AI algorithms, businesses can now formulate HR strategies that adapt to the particular needs of employees rather being limited by the resource planning of the HR team.

Today’s workforce not only looks forward to have a better workplace, but also expects to have a better brand story that adds credibility to their future career. However, working with a reputed brand in the market adds to the glory of employee experience. With AI tools, the company will be giving a first-hand impression to its employees, a personalised upskilling and experience of training, and an exceptional employee involvement process. These factors enhance the company opinions which leads to an overall improvement of the company brand.

Moreover, AI is also responsible to give minute details of HR processes that can grab the attention of HR managers. This way, the scope of productivity gets improved wherever possible. Not only this, AI plays a predominant role in many other HR processes.

Let's find out how AI can prove to be beneficial for the organisational team:

• AI offers valuable insights on better interpersonal relationship among team members
• AI is diligently skilful in handling mental health issues at the workplace
• AI acts as a good interface in the recruitment process
• AI improves the onboarding experience of an employee from the first day itself
• AI promotes tasks automation
• AI cultivates training paradigms amongst employee's learning prowess

However, AI shows the red flag to signal the HR manager wherever any team member faces difficulty in learning. This will, in turn, enables the HR manager to impart training exercises for improving those skills.

To sum up, AI enables the organisation to comply with HR processes so as to promote the smooth flow of productivity in a systematic manner and also improve the employee experience throughout their journey in the organisation.

Botomation with Tryvium desk empowers the organization to reinforce the AI dream of self-service for employee experience improvement. It gives the large organizations a platform to collaborate and become more interactive by adding value to MS Teams and Skype for the Business platform which focuses on enhancing the employee experience. It also integrates with major ITSM tools, Customer Support systems and CRMs available in the market. With the Tryvium desk, organizations can enhance first call resolution, improve agent productivity and reduce call handling time which makes the employee and their customers experience more powerful.  Botomation is one of our expertly developed applications combining AI and intelligent automation technologies to help companies with digital transformation. Whether it is customer support or employee request, Botomation can always provide near human experience with superior communication.

Many organizations have introduced Botomation in their system to optimize AI in a significant manner that interprets the action relating to business processes and communicates smoothly within the organization. This, in turn, improves operational efficiency and brings down the overhead costs by superseding the routine tasks of employees. It allows the employees to concentrate on higher-value processes which improve productivity by 86%. Botomation also upgrades the HR processes and extend the benefits to employees in the areas of performance review, monthly payroll and travel expense management.

To know more, download the ebook https://botomation.ai/insights/ebooks/ai-and-employee-experience

Please feel free to contact our sales team at +1 732-283-0499.We will be more than happy to assist you. 

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How do I select SVM kernels?

This article was written by Sebastian Raschka.

Given an arbitrary dataset, you typically don't know which kernel may work best. I recommend starting with the simplest hypothesis space first -- given that you don't know much about your data -- and work your way up towards the more complex hypothesis spaces. So, the linear kernel works fine if your dataset if linearly separable; however, if your dataset isn't linearly separable, a linear kernel isn't going to cut it (almost in a literal sense).

For simplicity (and visualization purposes), let's assume our dataset consists of 2 dimensions only. Below, I plotted the decision regions of a linear SVM on 2 features of the iris dataset.

To read the rest of the article, click here.

 

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Multi-stage heterogeneous ensemble meta-learning with hands-off demo

In this blog, I will introduce a R package for Heterogeneous Ensemble Learning (Classification, Regression) that is fully automated. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.

Before we dwell into the package details, let’s start with understanding a few basic concepts.

Why Ensemble Learning?

Generally, predictions become unreliable when the input sample is out of the training distribution, bias to data distribution or error prone to noise, and so on. Most approaches require changes to the network architecture, fine tuning, balanced data, increasing model size, etc. Further, the selection of the algorithm plays a vital role, while the scalability and learning ability decrease with the complex datasets. Combining multiple learners is an effective approach, and have been applied to many real-world problems. Ensemble learners combine a diverse collection of predictions from the individual base models to produce a composite predictive model that is more accurate and robust than its components. With meta ensemble learning one can minimize generalization error to some extent irrespective of the data distribution, number of classes, choice of algorithm, number of models, complexity of the datasets, etc. So, in summary, the predictive models will be able to generalize better.

How can we build models in more stable fashion while minimizing under-fitting/overfitting which is very critical to the overall outcome? The solution is ensemble meta-learning of a heterogeneous collection of base learners.

Common Ensemble Learning Techniques

The different popular ensemble techniques are referred to in the figure below. Stacked generalization is a general method of using a high-level model to combine lower- level models to achieve greater predictive accuracy. In the Bagging method, the independent base models are derived from the bootstrap samples of the original dataset. The Boosting method grows an ensemble in a dependent fashion iteratively, which adjusts the weight of an observation based on the past prediction. There are several extensions of bagging and boosting.

Overview

metaEnsembleR is an R package for automated meta-learning (Classification, Regression). The functionalities provided includes simple user input based predictive modeling with the selection choice of the algorithms, train-validation-test split, model valuations, and easy guided unseen data prediction which can help the user’s to build stack ensembles on the go. The core aim of this package is to cater the larger audiences in general. metaEnsembleR significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.

Using metaEnsembleR

The package consists of the following components:

• Ensemble Classifiers Training and Prediction
• Ensemble Regressor Training and Prediction
• Model Evaluation, Model Results (Observation vs. Prediction on test data) & new unseen data prediction and Disk write I/O performance charts & saving prediction results

All these functions are very intuitive, and their use is illustrated with examples below covering the Classification and Regression problem in general.

Getting Started

The package can be installed directly from CRAN

Install from Rconsole: install.packages(“metaEnsembleR”)

However, the latest stable version (if any) could be found on Github, and installed using devtools package.

Install from GitHub:

if(!require(devtools)) install.packages(“devtools”)

devtools::install_github(repo = ‘ajayarunachalam/metaEnsembleR’, ref = ‘main’)

Usage

library(“metaEnsembleR”)

set.seed(111)

• Training the ensemble classification model is as simple as one-line call to the ensembler.classifier function, in the following ways either passing the csv file directly or the imported dataframe, that takes into account the arguments in the following order starting the Dataset, Outcome/Response Variable index, Base Learners, Final Learner, Train-Validation-Test split ratio, and the Unseen data

ensembler_return ← ensembler.classifier(iris[1:130,], 5, c(‘treebag’,’rpart’), ‘gbm’, 0.60, 0.20, 0.20, read.csv(‘./unseen_data.csv’))

                        OR

unseen_new_data_testing iris[130:150,]

ensembler_return ← ensembler.classifier(iris[1:130,], 5, c(‘treebag’,’rpart’), ‘gbm’, 0.60, 0.20, 0.20, unseen_new_data_testing)

The above function returns the following, i.e., test data with the predictions, prediction labels, model result, and finally the predictions on unseen data.

testpreddata ← data.frame(ensembler_return[1])

table(testpreddata$actual_label)

table(ensembler_return[2])

#### Performance comparison #####

modelresult ← ensembler_return[3]

modelresult

#### Unseen data ###

unseenpreddata ← data.frame(ensembler_return[4])

table(unseenpreddata$unseenpreddata)

• Training the ensemble regression model is the same as one-line call to the ensembler.regression function, in the following ways either passing the csv file directly or the imported dataframe, that takes into account the arguments in the following order starting the Dataset, Outcome/Response Variable index, Base Learners, Final Learner, Train-Validation-Test split ratio, and the Unseen data

house_price ←read.csv(file = ‘./data/regression/house_price_data.csv’)

unseen_new_data_testing_house_price ←house_price[250:414,]

write.csv(unseen_new_data_testing_house_price, ‘unseen_house_price_regression.csv’, fileEncoding = ‘UTF-8’, row.names = F)

ensembler_return ← ensembler.regression(house_price[1:250,], 1, c(‘treebag’,’rpart’), ‘gbm’, 0.60, 0.20, 0.20, read.csv(‘./unseen_house_price_regression.csv’))

                      OR

ensembler_return ← ensembler.regression(house_price[1:250,], 1, c(‘treebag’,’rpart’), ‘gbm’, 0.60, 0.20, 0.20, unseen_new_data_testing_house_price )

The above function returns the following, i.e., test data with the predictions, prediction values, model result, and finally the unseen data with the predictions.

testpreddata ← data.frame(ensembler_return[1])

####  Performance comparison  #####

modelresult ← ensembler_return[3]

modelresult

write.csv(modelresult[[1]], “performance_chart.csv”)

#### Unseen data  ###

unseenpreddata ← data.frame(ensembler_return[4])

Examples

demo classification

library(“metaEnsembleR”)

attach(iris)

data(“iris”)

unseen_new_data_testing ← iris[130:150,]

write.csv(unseen_new_data_testing, ‘unseen_check.csv’, fileEncoding = ‘UTF-8’, row.names = F)

ensembler_return ← ensembler.classifier(iris[1:130,], 5, c(‘treebag’,’rpart’), ‘gbm’, 0.60, 0.20, 0.20, unseen_new_data_testing)

testpreddata ← data.frame(ensembler_return[1])

table(testpreddata$actual_label)

table(ensembler_return[2])

####Performance comparison#####

modelresult ← ensembler_return[3]

modelresult

act_mybar ← qplot(testpreddata$actual_label, geom= “bar”)

act_mybar

pred_mybar ← qplot(testpreddata$predictions, geom= ‘bar’)

pred_mybar

act_tbl ← tableGrob(t(summary(testpreddata$actual_label)))

pred_tbl ← tableGrob(t(summary(testpreddata$predictions)))

ggsave(“testdata_actual_vs_predicted_chart.pdf”,grid.arrange(act_tbl, pred_tbl))

ggsave(“testdata_actual_vs_predicted_plot.pdf”,grid.arrange(act_mybar, pred_mybar))

####unseen data###

unseenpreddata ← data.frame(ensembler_return[4])

table(unseenpreddata$unseenpreddata)

table(unseen_new_data_testing$Species)

demo regression

library(“metaEnsembleR”)

data(“rock”)

unseen_rock_data ← rock[30:48,]

ensembler_return ← ensembler.regression(rock[1:30,], 4,c(‘lm’), ‘rf’, 0.40, 0.30, 0.30, unseen_rock_data)

testpreddata ← data.frame(ensembler_return[1])

####Performance comparison#####

modelresult ← ensembler_return[3]

modelresult

write.csv(modelresult[[1]], “performance_chart.csv”)

####unseen data###

unseenpreddata ← data.frame(ensembler_return[4])

Comprehensive Examples

More demo examples can be found in the Demo.R file, to see the results run Rscript Demo.R in the terminal.

Contact

If there is some implementation you would like to see here or add in some examples feel free to do so. You can always reach me at ajay.aruanchalam08@gmail.com

Always Keep Learning & Sharing Knowledge!!!

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Recent Java enhancements for numeric calculations

In the past, slow evaluation of mathematical functions and large memory footprint were the most significant drawbacks of Java compared to C++/C for numeric computations and scientific data analysis. However, recent enhancements in the Java Virtual Machine (JVM) enabled faster and better numerical computing due to several enhancements in evaluating trigonometric functions.

In this article we will use the DataMelt (https://datamelt.org) for our benchmarks. Let us consider the following algorithm implemented in the Groovy dynamically-typed language shown below. It uses a large loop, repeatedly calling the sin() and cos() functions. Save these lines in a file with the extension "goovy" and run it in DataMelt:


import java.lang.Math
long then = System.nanoTime()
double x=0
for (int i = 0; i < 1e8; i++)
x=x+Math.sin(i)/Math.cos(i)
itime = ((System.nanoTime() - then)/1e9)
println "Time: " + itime+" (sec) result="+x


The execution of this Groovy code is typically a 10-20% faster than for the equivalent code implemented in Python:


import math,time
then = time.time()
x=0
for i in xrange(int(1e8)):
x=x+math.sin(i)/math.cos(i)
itime = time.time() - then
print("Time:",itime," (sec) result=",x)


Note that CPython2 (version 2.7.2) is a 20% faster than CPython3 (version 3.4.2), but both CPython interpreters are slower for this example than Groovy.

The same algorithm re-implemented in Java:


import java.lang.Math;
public class Example {
public static void main(String[] args) {
long then = System.nanoTime();
double x=0;
for (int i = 0; i < (int)1e8; i++)
x=x+Math.sin(i)/Math.cos(i);
double itime = ((System.nanoTime() - then)/1e9);
System.out.println("Time for calculations (sec): " + itime+"\n");
System.out.println("Pi = " + x +"\n");
}
}


and processed using DataMelt with OpenJDK13 further increases the execution speed by a factor 2 compared to the Groovy dynamic language.

Similar benchmarks of the Java code have been carried out by repeating the calculation using Java SE 8 ("JDK1.8") released in March 2014. The computation was about a factor 8 slower than for the OpenJDK13. This was due to less optimized code for evaluation of trigonometric functions in JDK1.8 (and earlier versions). This confirms significant improvements for numeric computations in the recent JVM compared to the previous releases.

The question of code profiling using different implementations is a complex problem, and we do not plan to explore all possible scenarios in this article. The main conclusion we want to draw in this section is that the processing speed of the code that implements mathematical functions for numeric calculations is substantially better for Groovy than for CPython2 (CPython3). The observed performance improvements in dynamically-typed languages implemented in Java are due to the recent enhancements in the modern JVMs, leading to a large factor in the speed of evaluations
of mathematical functions.

Sergei Chekanov

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Fans react to the death of Alex Trebek

People took to Twitter to send their condolences and pay homage to Trebek

Alex Trebek, the long-running host of the beloved game show Jeopardy!, lost his battle with pancreatic cancer on Sunday. He was 80.

When Trebek arrives, I hope the good Lord states His answer to the meaning of Life in the form of a question. That might actually be appropriate, come to think of it. https://t.co/RdIS70D01Z

— Jamil Smith (@JamilSmith) November 8, 2020

In March 2019, the Canadian-born host announced his cancer diagnosis to the public but assured fans that despite the prognosis, “I’m going to fight this.”

“Just like 50,000 other people in the United States each year, this week I was diagnosed with stage 4 pancreatic cancer,” Trebek said.

Read More: Alex Trebek, long-running ‘Jeopardy!’ host, dies at 80

Trebek passed away at his home surrounded by his family and friends.

Jeopardy! is saddened to share that Alex Trebek passed away peacefully at home early this morning, surrounded by family and friends. Thank you, Alex. pic.twitter.com/Yk2a90CHIM

— Jeopardy! (@Jeopardy) November 8, 2020

Pat Sajak, game show host of Wheel of Fortune, commended Trebek for his “courage, grace and strength” and their friendship.

Alex Trebek’s courage, grace and strength inspired millions and awed those of us who knew him. A tremendous loss for his family, friends, co-workers and countless viewers. I was honored to be a friend and a part of his professional family for all these years. A very sad day.

— Pat Sajak (@patsajak) November 8, 2020

Since the news of his passing, people took to Twitter to send their condolences and pay homage.

One fan shared his favorite clip of The Simpsons where Trebek made an appearance.

RIP Alex Trebek. Still my favorite Jeopardy Episode. pic.twitter.com/pfIIbUa1X0

— Charlie Schneider (@AwesomEmergency) November 8, 2020

Actress Rosario Dawson thanked the host for sharing his life with viewers.

Thanks for sharing so much of your life w us Alex Trebek. You have brought me to tears so many times w your cheeky humor, kindness & brilliance & esp. as I watched you bravely take on the same cancer my father has been battling these past years. Blessings. https://t.co/9su4mERiQE

— Rosario Dawson (@rosariodawson) November 8, 2020

Stephen Colbert and Jimmy Fallon wished their fellow host well.

Alex Trebek was a gentleman, and it was a privilege to share the stage, even briefly, with such a kind, thoughtful guy. Rest in Peace. pic.twitter.com/moZrpOhLwA

— Stephen Colbert (@StephenAtHome) November 8, 2020

The best in the business. You will be missed, my friend 🙏🏻 Rest In Peace #AlexTrebek… pic.twitter.com/2lWZCFeYwE

— jimmy fallon (@jimmyfallon) November 8, 2020

Read More: Alex Trebek to Rep. John Lewis: Let’s survive cancer in 2020

Many news outlets including CNN and E! News, shared videos of former Jeopardy! contestant Burt Thakur. Thakur shared an emotional story about the revered host who he said taught him how to speak English.

“My grandfather who raised me – I’m going to get tears right now – I used to sit on his lap and watch your everyday,” Thakur said to Trebek during his appearance on the show. “So, it’s a pretty special moment for me, man. Thank you very much.”

Thank you for everything, Alex Trebek. ❤️ (🎥:@jeopardy) https://t.co/G83TbU8W7G pic.twitter.com/55raPBIaLI

— E! News (@enews) November 8, 2020

Thakur would go on to win the game with a total of $20,400.

It’s not the first time a contestant has shown appreciation for Trebek. In November 2019, contestant Dhruv Gaur gambled away his $2,000 earnings in Final Jeopardy with a message that made the host choke up: “What is: We love you, Alex!”

The gesture went viral with the hashtag #WeLoveYouAlex resulted in Gaur being invited to The Ellen Show.

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DMX recalls being introduced to crack at age 14

DMX said the person who introduced him to hip-hop is the same person who handed him a crack-laced blunt

Rapper DMX has spoken candidly about his addiction to crack cocaine, which began at the tender age of 14, after he smoked a blunt laced with crack.

The embattled rapper has made tabloid headlines for years as he struggled with substance abuse. In an emotional clip from next week’s episode of People’s Party with Talib Kweli, DMX recounted his ongoing battle with addiction.

DMX performs at Masters Of Ceremony 2019 at Barclays Center on June 28, 2019 in New York City. (Photo by Theo Wargo/Getty Images)

“I learned that I had to deal with the things that hurt me,” DMX said. “I didn’t really have anybody to talk to… in the hood, nobody wanted to hear that… Talking about your problems is viewed as a sign of weakness when actually it’s one of the bravest things you can do. One of the bravest things you can do is put it on the table, chop it up, and just let it out.”

Read More: DMX on having multiple personalities: ‘I wouldn’t want anybody to know anything about those people’

DMX, whose government name is Earl Simmons, disclosed that the person who introduced him to hip-hop is the same person who handed him a crack-laced blunt. This unnamed person, whom he considered a mentor, declined to tell him that he was smoking a highly addictive substance. The incident and its aftermath traumatized X and led to the confessional aspect of his raps.

“He passed the blunt around and… I hit the blunt,” an emotional DMX recalled in the interview. “I never felt like this before. It fucked me up. I later found out that he laced the blunt with crack. Why would you do that to a child? He was like 30, and he knew I looked up to him.”

Listening to dmx playlist drugs really fucked his life up he was too hot

— Poop (@King_DOM_Come) November 7, 2020

The talented rapper and actor is keenly aware of how that critical moment set him on a path that would provide the framework for his music career, and he said he sees himself as “blessed with a curse.”

Read More: DMX reached 14,000 while reading Bible verses on Instagram Live to get through pandemic

“Drugs were never a problem,” he said. “Drugs were a symptom of a bigger problem. There were things I went through in my childhood where I just blocked it out.” He went on to say that it was time for him to start opening up and dealing with his problems.

The complete UPROXX podcast interview is scheduled to drop on Nov. 9 at 9 a.m. EST.

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Man arrested in Chicago after killing girlfriend and her family

John Matthews was sent to jail without bail on three counts of first-degree murder

A Chicago man was arrested for killing his girlfriend, her mother and her sister because his girlfriend did not agree to cook him breakfast or braid his hair.

On Friday, John Matthews was sent to jail without bail on three counts of first-degree murder for the death of his 24-year-old girlfriend, Shonta Harris, her mother, 56-year-old Frances Neal, and her sister, 27-year-old Jasmine Neal.

via Chicago PD

Read More: Colorado mom of 4 killed by boyfriend in murder-suicide

Harris was dating Matthews for two years when the fight took place. Matthews’s grandmother tried to defuse the situation by leading Harris outside, but Matthews followed her.

Harris made attempts to call her mother for help, but Matthews took her phone and broke it. His grandmother eventually allowed Harris use her phone to get help, summoning Frances and Jasmine to the rescue.

Matthews told Harris’s relatives to leave his property after they were trying to get the four-month old baby he and Harris shared, Daily Mail reported.

John Matthews,25, shot the mother of his 4-month son dead when she said she was too tired to cook his #breakfast and braid his #hair. https://t.co/qrNMgWgxCK

— Blac Goss (@Blacgoss) November 8, 2020

Harris’ sister decided to call 911, reporting that Matthews gave Harris a black eye and refused to surrender their son.

In the heat of the moment, Matthews pulled a gun from his waist and shot and killed Frances with seven shots in her chest, back, arm and leg. He then shot Harris once in the chest.

Jasmine ran into the street, scared from the eight shots he fired at her mother and sister. Matthews chased Jasmine down and shot her in the head. 

Read More: Thousands of Denver protesters march for justice for Elijah McClain

Frances and Jasmine were pronounced dead at the scene, according to WBBM-TV, a CBS News affiliated in the Chicago area. Harris was taken to Advocate Christ Medical Center in Oak Lawn and was treated for her wounds.

Prosecutors said she had bullet fragments lodged in her chest, fractures to her ribs and spine, leaving her paralyzed from the waist down.

Eventually, Harris passed away on Sept. 5 due to complications.

Matthews is due back in court on Nov. 30.

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Officials report rampant fraud in Paycheck Protection Program

Some people obtained PPP loans for approximately $4 million by making false claims about their small businesses

The Wall Street Journal reports that the federal government has become swamped with reports of potential fraud in the Paycheck Protection Program, a response to the impact of the coronavirus pandemic.

According to government officials and public data, there’s evidence that others have taken advantage of the program’s “open door design” that was created to give small businesses easy access to taxpayer funds.

Read More: #BankingWhileBlack: Bank calls police on Black man trying to cash his own paycheck

Between April 3 and Aug. 8, $525 billion in loans was distributed to approximately 5.2 million small businesses.

WSJ also reports that the inspector general of the Small Business Administration, which serves as an arm of the agency that administers the PPP, found “strong indictors of widespread potential abuse and fraud in PPP.”

From July to September, there was a noticeable surge resulting in banks filing suspicious activity notices in the midst of the pandemic.

Government official watchdogs counted tens of thousands of companies that reportedly receiving PPP loans that appeared to be ineligible. These included corporations and small businesses that were created after the start of the pandemic, that exceeded 500 employees or were listed in a “Do Not Pay” system database due to owing money to taxpayers.

At the peak of PPP early in the pandemic, the SBA approved 514,000 loans in one day in May.

There are probably more cases of PPP fraud than voter fraud.

— JOEVoteRaPUNzel🇺🇸 (@RandilynIsIn) November 6, 2020

The Government Accountability Office in June warned of “significant fraud risk” due to lack of safeguards in place and complex rules.

In a report, GAO said, “Because of the number of loans approved, the speed with which they were processed and the limited safeguards, there is a significant risk that some fraudulent or inflated applications were approved. In addition, the lack of clear guidance has increased the likelihood that borrowers may misuse loan proceeds or be surprised they do not qualify for full loan forgiveness.”

In September, WSJ reported that charges were filed against 57 people for their roles in crimes connected to PPP. The schemes consisted of submitting fraudulent documents to obtain funds for payroll and instead using it to purchase lavish items.

Five people in Georgia, Ohio, and California obtained PPP loans for approximately $4 million by making false claims about their small businesses’ overall expenses and payroll. The individuals failed to make payroll payments as indicated on their loan application which raised red flags.

Read More: Gas station uses PPP loans to pay for Trump billboards

Authorities later seized $120,000 in cash, a $125,000 Range Rover truck, jewelry, and $3 million from 10 bank accounts at the time of arrest, according to The United States Department of Justice website.

Brian Rabbitt, assistant attorney general at the Justice Department said that the government allowing companies to self-certify rather than be vetted are a magnet for opportunities of fraud.

“Experience has taught us that any time the federal government makes a large amount of money available to the public on an expedited basis, the opportunities for fraud are unfortunately clear,” said Rabbitt in September during an online press conference.

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Dave Chappelle in SNL monologue delivers biting commentary on Trump, COVID and race

The legendary comedian’s appearance on Saturday Night Live comes four years after his post-2016 election performance

Four years ago, comedian Dave Chappelle was the first guest on NBC’s Saturday Night Live after the 2016 election.

It was fate that the three-time Grammy winner hosted Saturday’s episode, the first show after the 2020 election.

While the electricity of the evening was high following news of Joe Biden’s win, Chappelle’s opening monologue still contained much biting commentary on race relations and a health crisis that are plaguing the country. He made fun of the irony of how the pandemic has stopped mass shootings in America.

Dave Chappelle performs a monologue on Saturday Night Live after Joe Biden was declared president-elect. (via screenshot)

“You guys remember what life was like before COVID?” Chappelle said. “I do. A mass shooting every week. Anyone remember that? Thank God for COVID. Someone had to lock these murderous whites up, keep them in the house.”

He also comically condemned white people who refuse to wear masks during the pandemic. There are several documented cases of white Americans refusing to wear protective masks inside airplanes and stores.

“I don’t know why poor white people don’t like wearing masks. What is the problem,” Chappelle said. “You wear masks to the Klan rally, wear it to Walmart too!”

READ MORE: Chris Rock on Trump’s coronavirus diagnosis: ‘My heart goes out to COVID’

He continued by commenting on how Black Americans, who have dealt with centuries-long atrocities in the U.S., are well equipped to deal with today’s turmoil. White citizens, by contrast, are reacting to being in unfamiliar territory, he suggested.

“You don’t even want to wear your mask because it’s oppressive? Try wearing the mask I’ve been wearing all these years!” Chappelle said. “I can’t even tell something true unless it has a punchline behind it. You guys aren’t ready.”

Chappelle even turned his attention to Trump, who is the loudest opponent of masks, and his bout with the coronavirus after months of haranguing against the pandemic. Trump was hospitalized for days before returning to the campaign trail and speaking before large audiences.

Chappelle quipped that after Trump received high-priority treatment, the president returned to the White House, took off his mask “and killed four more people.” He joked about the hypocrisy and tone-deafness of Trump telling the public not to worry about COVID while receiving care for it.

READ MORE: Dave Chappelle drops hilarious truth bombs about Trump voters in Netflix special

“It would be like me going to a homeless shelter with a bag full of hamburgers, and saying ‘These is mines!’ And just started eating in front of the homeless.”

While proceeding to mime eating the burgers, he jokingly said: “Don’t let hunger dictate your life.”

Chappelle also took aim at Trump in 2016 after he was declared the winner of the presidential election that year. In his monologue, the legendary comedian took the temperature of a country in shock saying: “we elected an internet troll as our president.”

During his monologue he took the temperature of a country in shock, saying of then President-elect Donald Trump, “we elected an internet troll as our president.” It was fate that the three-time Grammy winner was the host for the first episode after the 2020 election.

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Inspired by the works of writer Henry James, The Haunting of Bly Manor, a new supernatural thriller series from Mike Flanagan, is based on true stories and real accounts… in more ways than one. Watch The Haunting of Bly Manor, only on Netflix: https://ift.tt/32o3Fij SUBSCRIBE: http://bit.ly/29qBUt7 About Netflix: Netflix is the world's leading streaming entertainment service with over 195 million paid memberships in over 190 countries enjoying TV series, documentaries and feature films across a wide variety of genres and languages. Members can watch as much as they want, anytime, anywhere, on any internet-connected screen. Members can play, pause and resume watching, all without commercials or commitments. Is The Haunting of Bly Manor A True Story? | Netflix https://youtube.com/Netflix Dead doesn't mean gone. An au pair plunges into an abyss of chilling secrets in this gothic romance from the creator of "The Haunting of Hill House."

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5 states OK measures eradicating racist language, symbols

The votes are a positive sign in a nation where racial tension always has existed, a University of Alabama at Birmingham teacher says

BIRMINGHAM, Ala. (AP) — Alabama voters reversed themselves from a few years ago and removed racist vestiges of segregation from the state constitution that courts long ago ruled unconstitutional. Rhode Island did a similar a U-turn to eradicate the word “plantations” from the state’s official name.

In a year when discussions of racial justice have dominated U.S. society like few others, five states voted to cleanse the public sphere of words, phrases and symbols that to many were painful reminders of the nation’s history of slavery and the systematic oppression of Black people.

Brendan Skip Mark, who teaches political science at the University of Rhode Island, believes the decisions were linked to the revulsion and widespread protests that followed the police killing of George Floyd in Minnesota in May.

In this Nov. 2, 2020, file photo, Sharon Brown, co-chair of the Mississippi Poor Peoples Campaign, reacts during a news conference at the state Capitol in Jackson, Miss., over voting concerns statewide. (AP Photo/Rogelio V. Solis, File)

“In many ways this has sparked a national conversation on race, and I think we’ve seen a lot of people who are more willing to take concrete steps to address racism than they were in the past,” Mark said.

In addition to the votes in Alabama and Rhode Island, residents of Utah and Nebraska decided to strip their constitutions of unenforceable provisions that allowed slavery as a punishment for criminal convictions. And Mississippi voters approved a state flag without the familiar X-shaped design of the Confederate battle flag.

READ MORE: NBA arena voting centers have impact in Atlanta, Detroit

The votes are a positive sign in a nation where racial tension always has existed, said Stacy Moak, who teaches in the social work department at the University of Alabama at Birmingham.

“Affirmative votes for these changes shows a willingness on the part of Americans to provide for a more inclusive community. These changes, by themselves, are not enough — but they are encouraging signs of progress in the right direction,” she said in an interview conducted by email.

In this Nov. 3, 2020, file photo, voters standing in line at Precinct 36 as they wait to vote in the general election in Jackson, Miss. (AP Photo/Rogelio V. Solis, File)

The Alabama measure begins the process of removing Jim Crow language from the 1901 Constitution that was intended to entrench white supremacy. Voters in the mostly white, conservative state had rejected similar proposals twice since 2000.

Courts had previously struck down the legality of the segregationist provisions that were enshrined in the document long ago, but the language banning mixed-race marriage, allowing poll taxes and mandating school segregation remained.

Glenn Crowell, a Black Republican from Montgomery, was among the roughly 67% of voters who supported scrapping those sections.

“It just doesn’t make any sense nowadays,” said Crowell, 63. Yet another statewide vote will be required to approve the revisions after legislators consider a draft in 2022.

In neighboring Mississippi, about 71% of voters approved a new state flag featuring a magnolia and the words “In God We Trust” to replace the Confederate-themed flag that state legislators voted to retire in June after the nation erupted in demonstrations following Floyd’s killing.

This Sept. 2, 2020, file photo shows the magnolia centered banner chosen by the Mississippi State Flag Commission displayed outside the Old State Capitol Museum in downtown Jackson, Miss. (AP Photo/Rogelio V. Solis, File)

Mississippi voters also eliminated an 1890s provision that aimed to ensure white control of the state by requiring majorities of both the popular vote and the 122 state House districts to win statewide office. Now, only a popular vote majority is required.

To the west, Utah and Nebraska approved provisions similar to Alabama’s to delete constitutional language allowing slavery as a possible punishment in criminal cases.

The measures, which passed by 81% in Utah and 68% in Nebraska, got relatively little attention before the vote.

READ MORE: Biden addresses nation amid ballot count: ‘We’re not waiting to get the work done’

But the fact that states even placed the measures on ballots shows that protests and the national discussion on racism are having an impact, said Deirdre Cooper Owens, director of the humanities in medicine program at the University of Nebraska.

“Symbolism matters, and so does language,” she said by email.

The vote was closest in Rhode Island, once a hub of the trans-Atlantic slave trade, where about 53% of voters supported the proposal to strip the words “and Providence Plantations” from the state’s formal name, first adopted in 1790. A similar measure failed in 2010.

In this Monday, June 22, 2020, file photo, the seal of the State of Rhode Island decorates a podium as Gov. Gina Raimondo looks on at right during a news conference where she announced that she has signed an executive order to remove the phrase “Providence Plantations” in the state’s formal name from some official documents and executive agency websites in Providence, R.I. (AP Photo/David Goldman, File)

Like elsewhere in the country, Rhode Island has seen protests over Floyd’s death, and in racially diverse Providence, a truth commission was established to consider the state’s historic ties to slavery, land seizures, systemic racism and possible reparations.

“This ballot initiative is part of a broader shift in Rhode Island to reconcile with the past,” said Mark, the political scientist. “I think this is a unique moment in history.”

All those ballot measures involved changing symbols or wiping away reminders of injustices of long ago. In California, where voters were asked to reconsider a more contemporary race-related issue, they balked.

The liberal-minded state rejected a proposition that would have repealed a 1996 initiative prohibiting affirmative action programs in public employment, education or contracting. Supporters said the measure lost for several reasons, including a lack of time to persuade voters during a busy election year.

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Don Lemon reacts to Biden victory: ‘I didn’t expect to be so overwhelmed’

The CNN host delivered a rebuke on what has been a long almost four years under Trump’s reign

The news of Joe Biden being elected the next president of the United States on Saturday has prompted many to react in emotional ways.

This includes CNN‘s Don Lemon, who commented on the decision during a newscast that evening.

Starting with a deep sigh, Lemon began by saying: “I almost can’t talk right now, because of the emotion.”

CNN host Don Lemon (via screenshot)

Lemon, appearing on-screen with CNN anchor Chris Cuomo, told his colleague that he went to sleep in his hotel in New York City Saturday morning before Pennsylvania had been projected to go for Biden, the Democratic nominee. He explained he was awoken from 40 floors up by a loud throng of people cheering outside.

He spoke of how Donald Trump‘s presidency has affected the country and what a Biden victory means going forward.

READ MORE: A Black woman is going to the White House and we’re about to act a fool

“I turned the television on and there were my colleagues announcing that Joe Biden had become the president-elect of the United States, and not to forget Kamala Harris the first Black woman (vice presidential-elect),” Lemon said. “I didn’t expect to be so overwhelmed by that. I didn’t realize the PTSD that many marginalized people — that African-Americans, women, Latinos, people of color, all kinds of white people — are feeling around this country, because we have had whiplash from someone who only cares about himself and not uniting people.”

Each weeknight, Chris Cuomo Nightly transitions into CNN Tonight with Don Lemon with the titular hosts and close friends speaking with each other before Cuomo hands over the air to Lemon. As Cuomo tossed it to Lemon by asking him how he was feeling, Lemon let forth six straight minutes of candid words and emotions.

READ MORE: Van Jones moved to tears after Biden wins: ‘This is a good day’

Lemon talked about what he has had to endure during Trump’s tenure in the White House. He pulled no punches when discussing Trump on his nightly show, often prompting Trump to frequently refer to Lemon and CNN as “fake news.”

Lemon talked about being subjected to racist and homophobic slurs from Trump supporters over the past four years.

Lemon revealed that he’s had to deal with “people calling me n***er and f**” and “fake news.”

“Never before that I’ve been in this business, since 1991, have I ever had to deal with the crap that I’ve had to deal with over the last four years,” Lemon said.

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