Friday, October 6, 2017

Focus On Learning and Creating Rather Than Being Entertained and Distracted

https://medium.com/the-mission/focus-on-learning-and-creating-rather-than-being-entertained-and-distracted-e6573de1bc84

Focus On Learning and Creating Rather Than Being Entertained and Distracted

“Ordinary people seek entertainment. Extraordinary people seek education and learning.” -Benjamin Hardy
Most people are distracted right now.
They’re distracted while they’re at work. They’re distracted when they’re with family and friends.
They’re distracted at the gym, on their commute, and even in the shower.
The mediocre majority will continue going through life this way, never experiencing the fullness of a life filled with deep focus and purpose.
Most people don’t prioritize learning and creating. They don’t care enough to invest in their personal development and growth.
Entertainment is more important. Most people have replaced achieving their life dreams and goals with TV, partying, and social media.
Their life is characterized by entertainment and distraction, not learning and creating.
As a result, they don’t have close relationships. They’re stuck in jobs they hate. Their life is on the fast-track to disappointment, and they don’t know what to do.
Entertainment and distraction is the enemy of creation and learning. They will keep you in mediocrity.
If you don’t want to end up living a life of mediocrity, focus on learning and education. It’s the fastest way to become extraordinary, wealthy, and successful.

The Quality of Your Learning Determines the Quality of Your Success

“Your level of success will rarely exceed your level of personal development, because success is something you attract by the person you become.” -Hal Elrod
Your income, relationships, and success are determined by your learning.
Most people spend more money on entertainment and gadgets than self-education. This is why they remain poor and broke with superficial relationships.
The quality of your learning and how much you invest in yourself directly determines the extent of your growth.
In the words of Ryan Holiday:
“The extent of the struggle determines the extent of the growth.”
You are defined by what you’re willing to struggle for. Most people aren’t willing to really struggle for anything. They blindly accept what they’re given, and spend their free time disengaging from reality.
Renowned motivational speaker Jim Rohn once said the most successful people in the world are always lifelong learners. They fully understand that their level of education directly determines their quality of life.
Formal education makes a living, but self-education makes a fortune,” Rohn explained.
It’s not just about a college degree. Most of what you learn in college isn’t applicable to real-world success. It’s your self-education, the kind of learning that helps you develop into the next evolution of yourself.
Most people will continue to go through life “in quiet desperation, with their songs left unsung.” Others dictate their income, their happiness, and their fulfillment.
If you don’t want to end up with regret on your death-bed, choose self-education over entertainment.
“You’ve got to wake up every morning with determination if you’re going to go to bed with satisfaction.” George Larimer
Photo by Jason Strull on Unsplash

We Attract Into Our Lives What We Are

Darren Hardy, author of The Compound Effect, once told a story about how he ended up with his wife.
When he was in his 20’s, he began compiling an enormous list of every attribute he wanted his future wife to have. He eventually filled 40 pages detailing the most exquisite and perfect match he could think of.
At the end of his writing, he realized something very important:
Any woman like that wouldn’t want to have anything to do with someone like him!
Hardy realized he needed to become a far better version of himself to attract a woman like that.
You must become a far better version of yourself to achieve the enormous success you want.
We attract into our lives what we are. Ask yourself — if you want a million dollars, do you have the mindset of a millionaire? Do you have the financial knowledge and self-discipline to own that kind of money?
Most people don’t, yet they continue complaining about being poor. You’ll never become a millionaire if you don’t become someone who could be a millionaire!
Every day, millions of people attract mediocre opportunities into their lives — jobs, time investments, dating partners, etc. — not realizing they are attracting exactly who they are!
Extremely successful people are magnets of extraordinary opportunities. They are individuals who gravitate towards success, because they are successful themselves.
If you constantly engage in mediocre, low-frequency activities with toxic individuals, you’ll only receive similar opportunities. You’ll never experience the type of success of the world’s most extraordinary people, because success is something you attract by the person you become.
“Every next level of your life will demand a different you.” -Leonardo DiCaprio

Step Forward Into Growth or Backwards Into Security

“It’s absurd that we would prioritize the hottest new device, the cool car, or trendy toy over owning that which makes us feel the most engaged and most alive.” -Neil Patel
C.S. Lewis once described humans this way: “We are like eggs at present. And you cannot go on indefinitely being just an ordinary, decent egg. We must be hatched or go bad.
You do not have the luxury to “wait” to improve. Like an egg, you’ll either progress into a new version of yourself, or go rotten. There is no other way.
Most people continue choosing security and safety over the choice to become the fullest version of themselves.
Said Louis Sachar:
“So, what’s it going to be — safety, or freedom? You can’t have both.”
Right now, you have the choice — step forward into growth and development, or step backwards into safety and security.
Most people step backward, slowly killing their chances to become extraordinary.
Seth Godin once said, “The problem is that our culture has engaged in a Faustian bargain, in which we trade our genius and artistry for apparent stability.
The sad truth is: there’s no such thing as security. Money can be taken away (how many times have you heard of a multi-millionaire going bankrupt?). Our health could fail at any time. Millions of people are abruptly fired or laid off every year.
Security isn’t real.
We must choose to design our own life, and let go of our addiction to “apparent stability.” If you’ve traded your genius and creativity for “stability,” it’s time to get those back.
“The goal of life is not to relax on the beach, sipping mojitos all day. The purpose is to find something you love that adds value to the world.” -Ben Foley
Photo by frank mckenna on Unsplash

Most People Will Never Be Successful

“The mediocre have a very narrow perception of reality, and in turn, their lives. They see things as they are, not how they can be.” -Ryan Holiday
Learning and self-education aren’t popular. Most people don’t read books, attend seminars, or take even the most basic steps for self-improvement. They would rather choose distraction and entertainment.
Most people will never be successful because they will spend their life this way.
With just a few lifestyle tweaks, you could enter into the top 5% — 10% of your field. The competition is extremely low! Amazing, extraordinary opportunities are abundant, because so few people ever take the steps to discover them.
You can become one of the few truly successful people in the world by taking your self-education and learning seriously. In a world of mediocrity, you can become extraordinary with only a few simple tweaks.
So how do you start?
The more evolved you become, the more focused you must be on those few things which matter most.
Your days must consistently be spent on high quality activities.
Success is continuously improving who you are, how you live, how you serve, and how you relate.
James Altucher once said, “Every day, check these 4 boxes: Have I improved 1% on physical, emotional, mental, and spiritual health?
You can start small. Even the smallest improvement is more than most people will ever see. While most people change slowly and unconsciously over time, you can begin taking control of your success by choosing to make a few small improvements.
That’s all it takes.

If You Want to Live an Extraordinary Life, You Need to Give Up a Normal One

“If you want to live an extraordinary life, you have to give up many of the things that are part of a normal one.” -Srinivas Rao
Extraordinary people are very uncommon. Their lifestyle of learning and self-education aren’t popular, either. Odds are, once you begin investing in yourself, you’ll find it’s a lonely road.
Todd Brison wisely said, “The more bold you are, the more rejection you’ll experience.” The mob of the majority doesn’t like deserters.
If you want an extraordinary life, you’ll need to give up many parts of the “normal” life. That might mean giving up common toxic behaviors or hanging out with toxic people. If “everyone does it,” you’d do well to give it a long, hard look.
Most people will never achieve an extraordinary life. That’s OK. The extraordinary life isn’t for everyone. Learning and self-education take time, energy, and focus that most people would rather spend on entertainment and distraction — “ordinary” things.
“Great” opportunities cost “good” ones; you can’t have both.
Said Benjamin Hardy:
Before you evolve, you can reasonably spend time with just about anyone.
You can reasonably eat anything placed in front of you.
You can reasonably justify activities and behaviors that are, frankly, mediocre.
But as your vision expands, you realize you need to make adjustments. You can’t spend your money on junk anymore. You need to manage your time much more diligently. You need to invest in yourself and your future.
To avoid mediocrity, you can’t continue being with low-frequency, negative people.
You can’t keep eating crappy foods that slow you down.
You can’t do many of the things you used to do.
This is what extraordinary requires. Most people see the price and simply say “no thanks.”
But anything is possible if you pay the price.
“Every skill you acquire doubles your odds of success.” -Scott Adams

In Conclusion

“Who you are is a result of who you were, but where you end up depends entirely on who you choose to be from this moment forward.” -Hal Elrod
There are probably dozens of recurring activities you do on a daily basis that aren’t serving you.
Most people live their lives on other people’s terms. Their days are spent achieving other people’s goals and submitting to other people’s agendas.
Entertainment and distraction rule society. There will never be a shortage of TV shows, Buzzfeed articles, or Bad Lip Reading videos to take up your time.
If you want to avoid mediocrity and achieve enormous success, you must cut out distractions and prioritize learning instead.
Most people will go their whole lives as a slave to entertainment. They prioritize entertainment over improving their life, their family’s well-being, and their hopes and dreams.
This is how people can stay at jobs they hate for years. It’s how people stay in toxic relationships and remain perpetually broke. They don’t bother learning how to succeed.
If your lifestyle does not add to your healing, it will subtract from it.
Prioritize learning. Invest in self-education.
Choose success over mediocrity.

Call To Action

If you want to become extraordinary and get results 10x faster than most people, check out my checklist.


"Is Bayesian deep learning the most brilliant thing ever?" - a panel dis...

Tuesday, September 19, 2017

netdata.io

http://sanfrancisco.my-netdata.io/

/machine-learning-for-humans/why-machine-learning-matters

https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12 Machine Learning for Humans🤖👶 Simple, plain-English explanations accompanied by math, code, and real-world examples. [Update 9/2/17] This series is now available as a full-length e-book! Download here. For inquiries, please contact ml4humans@gmail.com. Roadmap Part 1: Why Machine Learning Matters. The big picture of artificial intelligence and machine learning — past, present, and future. Part 2.1: Supervised Learning. Learning with an answer key. Introducing linear regression, loss functions, overfitting, and gradient descent. Part 2.2: Supervised Learning II. Two methods of classification: logistic regression and SVMs. Part 2.3: Supervised Learning III. Non-parametric learners: k-nearest neighbors, decision trees, random forests. Introducing cross-validation, hyperparameter tuning, and ensemble models. Part 3: Unsupervised Learning. Clustering: k-means, hierarchical. Dimensionality reduction: principal components analysis (PCA), singular value decomposition (SVD). Part 4: Neural Networks & Deep Learning. Why, where, and how deep learning works. Drawing inspiration from the brain. Convolutional neural networks (CNNs), recurrent neural networks (RNNs). Real-world applications. Part 5: Reinforcement Learning. Exploration and exploitation. Markov decision processes. Q-learning, policy learning, and deep reinforcement learning. The value learning problem. Appendix: The Best Machine Learning Resources. A curated list of resources for creating your machine learning curriculum. Who should read this? Technical people who want to get up to speed on machine learning quickly Non-technical people who want a primer on machine learning and are willing to engage with technical concepts Anyone who is curious about how machines think This guide is intended to be accessible to anyone. Basic concepts in probability, statistics, programming, linear algebra, and calculus will be discussed, but it isn’t necessary to have prior knowledge of them to gain value from this series. This series is a guide for getting up-to-speed on high-level machine learning concepts in ~2-3 hours. If you're more interested in figuring out which courses to take, textbooks to read, projects to attempt, etc., take a look at our recommendations in the Appendix: The Best Machine Learning Resources. Why machine learning matters Artificial intelligence will shape our future more powerfully than any other innovation this century. Anyone who does not understand it will soon find themselves feeling left behind, waking up in a world full of technology that feels more and more like magic. The rate of acceleration is already astounding. After a couple of AI winters and periods of false hope over the past four decades, rapid advances in data storage and computer processing power have dramatically changed the game in recent years. In 2015, Google trained a conversational agent (AI) that could not only convincingly interact with humans as a tech support helpdesk, but also discuss morality, express opinions, and answer general facts-based questions. (Vinyals & Le, 2017) The same year, DeepMind developed an agent that surpassed human-level performance at 49 Atari games, receiving only the pixels and game score as inputs. Soon after, in 2016, DeepMind obsoleted their own achievement by releasing a new state-of-the-art gameplay method called A3C. Meanwhile, AlphaGo defeated one of the best human players at Go — an extraordinary achievement in a game dominated by humans for two decades after machines first conquered chess. Many masters could not fathom how it would be possible for a machine to grasp the full nuance and complexity of this ancient Chinese war strategy game, with its 10¹⁷⁰ possible board positions (there are only 10⁸⁰atoms in the universe). Professional Go player Lee Sedol reviewing his match with AlphaGo after defeat. Photo via The Atlantic. In March 2017, OpenAI created agents that invented their own language to cooperate and more effectively achieve their goal. Soon after, Facebook reportedly successfully training agents to negotiate and even lie. Just a few days ago (as of this writing), on August 11, 2017, OpenAI reached yet another incredible milestone by defeating the world’s top professionals in 1v1 matches of the online multiplayer game Dota 2. See the full match at The International 2017, with Dendi (human) vs. OpenAI (bot), on YouTube. Much of our day-to-day technology is powered by artificial intelligence. Point your camera at the menu during your next trip to Taiwan and the restaurant’s selections will magically appear in English via the Google Translate app. Google Translate overlaying English translations on a drink menu in real time using convolutional neural networks. Today AI is used to design evidence-based treatment plans for cancer patients, instantly analyze results from medical tests to escalate to the appropriate specialist immediately, and conduct scientific research for drug discovery. A bold proclamation by London-based BenevolentAI (screenshot from About Us page, August 2017). In everyday life, it’s increasingly commonplace to discover machines in roles traditionally occupied by humans. Really, don’t be surprised if a little housekeeping delivery bot shows up instead of a human next time you call the hotel desk to send up some toothpaste. In this series, we’ll explore the core machine learning concepts behind these technologies. By the end, you should be able to describe how they work at a conceptual level and be equipped with the tools to start building similar applications yourself. The semantic tree: artificial intelligence and machine learning One bit of advice: it is important to view knowledge as sort of a semantic tree — make sure you understand the fundamental principles, ie the trunk and big branches, before you get into the leaves/details or there is nothing for them to hang on to. — Elon Musk, Reddit AMA Machine learning is one of many subfields of artificial intelligence, concerning the ways that computers learn from experience to improve their ability to think, plan, decide, and act. Artificial intelligence is the study of agents that perceive the world around them, form plans, and make decisions to achieve their goals. Its foundations include mathematics, logic, philosophy, probability, linguistics, neuroscience, and decision theory. Many fields fall under the umbrella of AI, such as computer vision, robotics, machine learning, and natural language processing. Machine learning is a subfield of artificial intelligence. Its goal is to enable computers to learn on their own. A machine’s learning algorithm enables it to identify patterns in observed data, build models that explain the world, and predict things without having explicit pre-programmed rules and models. The AI effect: what actually qualifies as “artificial intelligence”? The exact standard for technology that qualifies as “AI” is a bit fuzzy, and interpretations change over time. The AI label tends to describe machines doing tasks traditionally in the domain of humans. Interestingly, once computers figure out how to do one of these tasks, humans have a tendency to say it wasn’t really intelligence. This is known as the AI effect. For example, when IBM’s Deep Blue defeated world chess champion Garry Kasparov in 1997, people complained that it was using "brute force" methods and it wasn’t “real” intelligence at all. As Pamela McCorduck wrote, “It’s part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something — play good checkers, solve simple but relatively informal problems — there was chorus of critics to say, ‘that’s not thinking’”(McCorduck, 2004). Perhaps there is a certain je ne sais quoi inherent to what people will reliably accept as “artificial intelligence”: "AI is whatever hasn't been done yet." - Douglas Hofstadter So does a calculator count as AI? Maybe by some interpretation. What about a self-driving car? Today, yes. In the future, perhaps not. Your cool new chatbot startup that automates a flow chart? Sure… why not. Strong AI will change our world forever; to understand how, studying machine learning is a good place to start The technologies discussed above are examples of artificial narrow intelligence (ANI), which can effectively perform a narrowly defined task. Meanwhile, we’re continuing to make foundational advances towards human-level artificial general intelligence (AGI), also known as strong AI. The definition of an AGI is an artificial intelligence that can successfully perform any intellectual task that a human being can, including learning, planning and decision-making under uncertainty, communicating in natural language, making jokes, manipulating people, trading stocks, or… reprogramming itself. And this last one is a big deal. Once we create an AI that can improve itself, it will unlock a cycle of recursive self-improvement that could lead to an intelligence explosion over some unknown time period, ranging from many decades to a single day. Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control. — I.J. Good, 1965 You may have heard this point referred to as the singularity. The term is borrowed from the gravitational singularity that occurs at the center of a black hole, an infinitely dense one-dimensional point where the laws of physics as we understand them start to break down. We have zero visibility into what happens beyond the event horizon of a black hole because no light can escape. Similarly, after we unlock AI’s ability to recursively improve itself, it’s impossible to predict what will happen, just as mice who intentionally designed a human might have trouble predicting what the human would do to their world. Would it keep helping them get more cheese, as they originally intended? (Image via WIRED) A recent report by the Future of Humanity Institute surveyed a panel of AI researchers on timelines for AGI, and found that “researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years” (Grace et al, 2017). We’ve personally spoken with a number of sane and reasonable AI practitioners who predict much longer timelines (the upper limit being “never”), and others whose timelines are alarmingly short — as little as a few years. Image from Kurzweil’s The Singularity Is Near, published in 2005. Now, in 2017, only a couple of these posters could justifiably remain on the wall. The advent of greater-than-human-level artificial superintelligence (ASI) could be one of the best or worst things to happen to our species. It carries with it the immense challenge of specifying what AIs will want in a way that is friendly to humans. While it’s impossible to say what the future holds, one thing is certain: 2017 is a good time to start understanding how machines think. To go beyond the abstractions of a philosopher in an armchair and intelligently shape our roadmaps and policies with respect to AI, we must engage with the details of how machines see the world — what they “want”, their potential biases and failure modes, their temperamental quirks — just as we study psychology and neuroscience to understand how humans learn, decide, act, and feel. There are complex, high-stakes questions about AI that will require our careful attention in the coming years. How can we combat AI’s propensity to further entrench systemic biases evident in existing data sets? What should we make of fundamental disagreements among the world’s most powerful technologists about the potential risks and benefits of artificial intelligence? What will happen to humans' sense of purpose in a world without work? Machine learning is at the core of our journey towards artificial general intelligence, and in the meantime, it will change every industry and have a massive impact on our day-to-day lives. That’s why we believe it’s worth understanding machine learning, at least at a conceptual level — and we designed this series to be the best place to start. How to read this series You don’t necessarily need to read the series cover-to-cover to get value out of it. Here are three suggestions on how to approach it, depending on your interests and how much time you have: T-shaped approach. Read from beginning to end. Summarize each section in your own words as you go (see: Feynman technique); this encourages active reading & stronger retention. Go deeper into areas that are most relevant to your interests or work. We’ll include resources for further exploration at the end of each section. Focused approach. Jump straight to the sections you’re most curious about and focus your mental energy there. 80/20 approach. Skim everything in one go, make a few notes on interesting high-level concepts, and call it a night. 😉 About the authors “Ok, we have to be done with gradient descent by the time we finish this ale.” @ The Boozy Cow in Edinburgh Vishal most recently led growth at Upstart, a lending platform that utilizes machine learning to price credit, automate the borrowing process, and acquire users. He spends his time thinking about startups, applied cognitive science, moral philosophy, and the ethics of artificial intelligence. Samer is a Master’s student in Computer Science and Engineering at UCSD and co-founder of Conigo Labs. Prior to grad school, he founded TableScribe, a business intelligence tool for SMBs, and spent two years advising Fortune 100 companies at McKinsey. Samer previously studied Computer Science and Ethics, Politics, and Economics at Yale. Most of this series was written during a 10-day trip to the United Kingdom in a frantic blur of trains, planes, cafes, pubs and wherever else we could find a dry place to sit. Our aim was to solidify our own understanding of artificial intelligence, machine learning, and how the methods therein fit together — and hopefully create something worth sharing in the process. And now, without further ado, let’s dive into machine learning with Part 2.1: Supervised Learning! Enter your email below if you’d like to stay up-to-date with future content 💌 On Twitter? So are we. Feel free to keep in touch — Vishal and Samer 🙌🏽. And if you enjoyed reading this, you can contribute good vibes (and help more people discover this series) by hitting the 👏 below — it means a lot! More from Machine Learning for Humans 🤖👶 Part 1: Why Machine Learning Matters ✅ Part 2.1: Supervised Learning Part 2.2: Supervised Learning II Part 2.3: Supervised Learning III Part 3: Unsupervised Learning Part 4: Neural Networks & Deep Learning Part 5: Reinforcement Learning Appendix: The Best Machine Learning Resources Contact: ml4humans@gmail.com A special thanks to Edoardo Conti, Jonathan Eng, Grant Schneider, Sunny Kumar, Stephanie He, Tarun Wadhwa, and Sachin Maini (series editor) for their significant contributions and feedback. Machine LearningArtificial IntelligenceDeep LearningReinforcement LearningTech Show your support Clapping shows how much you appreciated Vishal Maini’s story.

Wednesday, August 30, 2017

Thursday, August 24, 2017

WHAT STILL UNITES US?



  


Decades ago, a debate over what kind of nation America is roiled the conservative movement.
Neocons claimed America was an “ideological nation” a “creedal nation,” dedicated to the proposition that “all men are created equal.”
Expropriating the biblical mandate, “Go forth and teach all nations!” they divinized democracy and made the conversion of mankind to the democratic faith their mission here on earth.
With his global crusade for democracy, George W. Bush bought into all this. Result: Ashes in our mouths and a series of foreign policy disasters, beginning with Afghanistan and Iraq.
Behind the Trumpian slogan “America First” lay a conviction that, with the Cold War over and the real ideological nation, the USSR, shattered into pieces along ethnic lines, it was time for America to come home.
Contra the neocons, traditionalists argued that, while America was uniquely great, the nation was united by faith, culture, language, history, heroes, holidays, mores, manners, customs and traditions. A common feature of Americans, black and white, was pride in belonging to a people that had achieved so much.
The insight attributed to Alexis de Tocqueville – “America is great because she is good, and if America ceases to be good, she will cease to be great” – was a belief shared by almost all.
What makes our future appear problematic is that what once united us now divides us. While Presidents Wilson and Truman declared us to be a “Christian nation,” Christianity has been purged from our public life and sheds believers every decade. Atheism and agnosticism are growing rapidly, especially among the young.
Traditional morality, grounded in Christianity, is being discarded. Half of all marriages end in divorce. Four-in-10 children are born out of wedlock. Unrestricted abortion and same-sex marriage – once regarded as marks of decadence and decline – are now seen as human rights and the hallmarks of social progress.
Tens of millions of us do not speak English. Where most of our music used to be classic, popular, country and western, and jazz, much of it now contains rutting lyrics that used to be unprintable.
Where we used to have three national networks, we have three 24-hour cable news channels and a thousand websites that reinforce our clashing beliefs on morality, culture, politics and race.
Consider but a few events post-Charlottesville.
“Murderer” was painted on the San Fernando statue of Fr. Junipero Serra, the Franciscan who founded the missions that became San Diego, San Francisco, San Juan Capistrano and Santa Clara.
America’s oldest monument honoring Columbus, in Baltimore, was vandalized. Sen. Tim Kaine of Virginia called for Robert E. Lee’s statue to be removed from the Capitol and replaced by – Pocahontas.
According to legend, this daughter of Chief Powhatan saved Capt. John Smith from being beheaded by throwing herself across his neck. The chief was a “person of interest” in the disappearance of the “Lost Colony” of Roanoke Island, among whose missing was Virginia Dare, the first European baby born in British America.
Why did Kaine not call for John Smith himself, leader of the Jamestown Colony that fought off Indian attacks, to be so honored?
In New Orleans, “Tear It Down” was spray-painted on a statue of Joan of Arc, a gift from France in 1972. Besides being a canonized saint in the Catholic Church and a legendary heroine of France, what did the Maid of Orleans do to deserve this?
Taken together, we are seeing the discoverers, explorers and missionaries of North America demonized as genocidal racists all. The Founding Fathers are either slave owners or sanctioners of slavery.
Our nation-builders either collaborated in or condoned the ethnic cleansing of Native Americans. Almost to the present, ours was a land where segregationists were honored leaders.
Bottom line for the left: Americans should be sickened and ashamed of the history that made us the world’s greatest nation. And we should acknowledge our ancestors’ guilt by tearing down any and all monuments and statues that memorialize them.
This rising segment of America, full of self-righteous rage, is determined to blacken the memory of those who have gone before us.
To another slice of America, much of the celebrated social and moral “progress” of recent decades induces a sense of nausea, summarized in the lament, “This isn’t the country we grew up in.”
Hillary Clinton famously described this segment of America as a “basket of deplorables … racist, sexist, homophobic, xenophobic, Islamaphobic … bigots,” and altogether “irredeemable.”
So, what still unites us? What holds us together into the indefinite future? What makes us one nation and one people? What do we offer mankind, as nations seem to recoil from what we are becoming, and are instead eager to build their futures on the basis of ethnonationalism and fundamentalist faith?
If advanced democracy has produced the disintegration of a nation that we see around us, what is the compelling case for it?
A sixth of the way through the 21st century, what is there to make us believe this will be the Second American Century?