• This cycle is based on empirical data meaning enough data was observed and recorded to make it possible to suggest attitudes and reactions. Keep in mind that we all have free will and thus results will vary from one individual to another.
• The graph shows the energy high at the beginning of the cycle (not unlike any other astrological aspect) followed by a slow down before it gets strong and again this reflects years of tracking and noting feedback from our many students.
• If you are making a decision during this time you might want to let it set for a day or two then check your decision again to see if it still makes sense. However, you can feel into the ebb and flow and find good times to work on self emotionally in both the low and high points. Impatience, emotion and acts without thinking are common.
• With practice you can feel when the energy is there to help bring completion to tasks, goals and projects you may be working on.
NATIONAL BESTSELLER • First comes a High, a period of confident expansion. Next comes an Awakening, a time of spiritual exploration and rebellion. Then comes an Unraveling, in which individualism triumphs over crumbling institutions. Last comes a Crisis—the Fourth Turning—when society passes through a great and perilous gate in history.
William Strauss and Neil Howe will change the way you see the world—and your place in it. With blazing originality, The Fourth Turning illuminates the past, explains the present, and reimagines the future. Most remarkably, it offers an utterly persuasive prophecy about how America’s past will predict what comes next.
Strauss and Howe base this vision on a provocative theory of American history. The authors look back five hundred years and uncover a distinct pattern: Modern history moves in cycles, each one lasting about the length of a long human life, each composed of four twenty-year eras—or “turnings”—that comprise history’s seasonal rhythm of growth, maturation, entropy, and rebirth. Illustrating this cycle through a brilliant analysis of the post–World War II period, The Fourth Turning offers bold predictions about how all of us can prepare, individually and collectively, for this rendezvous with destiny.
The Education Myth questions the idea that education represents the best, if not the only, way for Americans to access economic opportunity. As Jon Shelton shows, linking education to economic well-being was not politically inevitable. In the eighteenth and nineteenth centuries, for instance, public education was championed as a way to help citizens learn how to participate in a democracy. By the 1930s, public education, along with union rights and social security, formed an important component of a broad-based fight for social democracy.
Shelton demonstrates that beginning in the 1960s, the political power of the education myth choked off powerful social democratic alternatives like A. Philip Randolph and Bayard Rustin’s Freedom Budget. The nation’s political center was bereft of any realistic ideas to guarantee economic security and social dignity for the majority of Americans, particularly those without college degrees. Embraced first by Democrats like Lyndon Johnson, Jimmy Carter, and Bill Clinton, Republicans like George W. Bush also pushed the education myth. The result, over the past four decades, has been the emergence of a deeply inequitable economy and a drastically divided political system.
New Thinking Allo • Streamed live 11 hours ago Christopher Robinson is coauthor of a book about his own experiences, titled Dream Detective. He has been the subject of a documentary called Premonition Man. His work is also featured in the book, The G.O.D. Experiments, by psychologist Gary Schwartz. His ability to dream of future events has been the subject of both scientific and journalistic investigation.
“Brilliant, wise, profound and persuasive. Common Sense for the 21st Century will come to be recognized as a classic of political theory.” ―George Monbiot, via Twitter An urgent, essential, and practical call to action from a cofounder of Extinction Rebellion What can we all do to avert catastrophe and avoid extinction? Roger Hallam has answers. In Common Sense for the 21st Century , Roger Hallam, cofounder of Extinction Rebellion, outlines how movements around the world need to come together now to start doing what engaging in mass civil disobedience to make real change happen. The book gives people the tools to understand not only why mass disruption, mass arrests, and mass sacrifice are necessary but also details how to carry out acts of civil disobedience effectively, respectfully and nonviolently. It bypasses contemporary political theory, and instead is inspired by Thomas Paine, the pragmatic 18th-century revolutionary whose pamphlet Common Sense sparked the American Revolution. Common Sense for the 21st Century urges us to confront the truth about climate change and argues forcefully that only a revolution of society and the state, similar to the turn that Paine urged the Americans to take into the political unknown, can save us now.
There is cosmic consolation in knowing what actually happens when we die — that supreme affirmation of having lived at all. And yet, however much we might understand that every single person is a transient chance-constellation of atoms, to lose a beloved constellation is the most devastating experience in life. It feels incomprehensible, cosmically unjust. It feels unsurvivable.
In the final years of his short and loss-riddled life, Henry David Thoreau (July 12, 1817–May 6, 1862) wrote in his diary:
I perceive that we partially die ourselves through sympathy at the death of each of our friends or near relatives. Each such experience is an assault on our vital force. It becomes a source of wonder that they who have lost many friends still live. After long watching around the sickbed of a friend, we, too, partially give up the ghost with him, and are the less to be identified with this state of things.
Henry David Thoreau (Daguerreotype by Benjamin D. Maxham, 1856)
Thoreau’s life of losses had begun seventeen years earlier. He was twenty-five when his beloved older brother died of tetanus after cutting himself shaving — a gruesome death, savaging the nervous system and contorting the body with agony. Thoreau grieved deeply. A lifelong diarist, he slipped into a five-week coma of the pen. He tried to listen to the music-box, which had always flooded him with delight, but the sounds came pouring out strange and hollow.
What right have I to grieve, who have not ceased to wonder? We feel at first as if some opportunities of kindness and sympathy were lost, but learn afterward that any pure grief is ample recompense for all. That is, if we are faithful; for a great grief is but sympathy with the soul that disposes events, and is as natural as the resin on Arabian trees. Only Nature has a right to grieve perpetually, for she only is innocent.
Having resumed his journal, he took up the subject in the privacy of its pages:
I live in the perpetual verdure of the globe. I die in the annual decay of nature. We can understand the phenomenon of death in the animal better if we first consider it in the order next below us the vegetable. The death of the flea and the Elephant are but phenomena of the life of nature.
This was a season of losses in Thoreau’s universe. His friend and mentor Emerson, who had hastened to stay with him and nurse him in the wake of his brother’s death, lost his beloved five-year-old son to scarlet fever, as incurable as tetanus in their era. Now it was Thoreau’s turn to comfort his friend. Leaning on his new acceptance of the naturalness of death as an antidote to grief, he wrote to Emerson:
Nature is not ruffled by the rudest blast. The hurricane only snaps a few twigs in some nook of the forest. The snow attains its average depth each winter, and the chic-a-dee lisps the same notes. The old laws prevail in spite of pestilence and famine. No genius or virtue so rare and revolutionary appears in town or village, that the pine ceases to exude resin in the wood, or beast or bird lays aside its habits.
Death is beautiful when seen to be a law, and not an accident — It is as common as life… Every blade in the field — every leaf in the forest — lays down its life in its season as beautifully as it was taken up. When we look over the fields we are not saddened because these particular flowers or grasses will wither — for their death is the law of new life.
The American Library Association found that 2,571 titles were challenged last year, an increase of nearly 40 percent over 2021. Many of the most banned titles deal with L.G.B.T.Q. themes or race.
Here are the most frequently targeted books →
Gender Queer, by Maia Kobabe
This graphic memoir, illustrated by the author, explores Kobabe’s experience of being nonbinary. It includes some confusing sexual encounters, and has been the most banned book in the United States for the past two years. According to the free speech group PEN America, it was banned in 56 school districts.
All Boys Aren’t Blue, by George M. Johnson
In this memoir, Johnson describes growing up Black and queer, including his sexual experiences and sexual assault. The Times’s reviewer called the book “exuberant” and “unapologetic,” noting that “Johnson lays bare the darkest moments in his life with wit and unflinching vulnerability.”
The Bluest Eye, by Toni Morrison
Morrison’s 1970 debut novel centers on a Black girl who longs for blue eyes so that she can fit in with conventional white beauty standards. The novel, which deals with racism and sexual abuse, launched Morrison to literary fame, and was praised in The Times for “prose so precise, so faithful to speech and so charged with pain and wonder that the novel becomes poetry.”
Flamer, by Mike Curato
Curato draws on his own experiences in his debut graphic novel for young adults, which follows a 14-year-old Filipino American boy at Boy Scout camp as he comes to terms with being gay. The book has been banned because of sexually explicit and L.G.B.T.Q. content, according to the American Library Association.
Looking For Alaska, by John Green
A coming-of-age story about young-adult relationships, this novel is about a high school student, Miles Halter, who goes to boarding school, and becomes fascinated by a girl named Alaska. The American Library said the book has been challenged because of sexually explicit and L.G.B.T.Q. themes.
The Perks of Being a Wallflower, by Stephen Chbosky
This book, a best-selling touchstone for young adult literature, is set in the early 1990s and follows an introvert through his freshman year of high school in the suburbs. Challenges to the book have included concerns about profanity, drugs, sexually explicit and L.G.B.T.Q. content.
The Lord of Ruin is another of those dive-for-shelter cards that most people hate to see coming up in a reading. And certainly sometimes the card will presage a painful, even devastating event.
But this is not the underlying tenet of the Lord of Ruin. It’s the end result of failing to engage with the true message it brings.
You see, the Ten of Swords is about the power in our own minds. The end result of our thoughts, beliefs and aspirations. If we have believed in our worthiness to achieve and attain; if we have struggled to reach the highest limits of our own current spiritual potential; if we have lived in an ethical and fair manner, we will inevitably have attracted joy, happiness and success into our lives.
If, on the other hand, we have fallen short of our best; believed in our weakness and therefore empowered that belief; if we have given in to negative and harmful thoughts, we will inevitably attract to us sadness, distrust and a reason to fear.
And then the Lord of Ruin walks in. We make so many of the events that happen in our lives… and we pay too little attention to ensuring that we make only good things for ourselves. I have never believed that we can eliminate painful and testing times entirely – we learn so well, when we do things the hard way, to give up hurting to learn. But I know, beyond a shadow of doubt, that we can reduce their frequency.
Most often, the Ten of Swords should be interpreted as an unexpected, shocking and traumatic upheaval. But it’s worth looking for clues about how you might be able to lessen the impact. If you find cards like the Nine of Cups or the Eight of Swords, then it’s probably better to give the dust time to settle before making an assessment. Things often look a lot worse than they really are. With the Eight of Wands or the Five of Disks, you might feel that clear communication would resolve difficulties.
When the card comes up with Adjustment/Justice it often points to legal matters. With the Tower the problem is caused by an outside influence (but this could be one of those nasty spiritual jolts that life sometimes gives us when we’re stagnating or fearful) and you then need to look for other cards indicating who or where. With Death, the Ten of Swords takes on its saddest aspect, indicating sudden endings.
This card has the major function of reminding us that what we think today creates tomorrow. And yesterday created today. We could do worse for ourselves than to do the best we can at the time.
In this instant New York Times bestseller, America’s top historians set the record straight on the most pernicious myths about our nation’s past.
The United States is in the grip of a crisis of bad history. Distortions of the past promoted in the conservative media have led large numbers of Americans to believe in fictions over facts, making constructive dialogue impossible and imperiling our democracy.
In Myth America, Kevin M. Kruse and Julian E. Zelizer have assembled an all-star team of fellow historians to push back against this misinformation. The contributors debunk narratives that portray the New Deal and Great Society as failures, immigrants as hostile invaders, and feminists as anti-family warriors—among numerous other partisan lies. Based on a firm foundation of historical scholarship, their findings revitalize our understanding of American history.
Replacing myths with research and reality, Myth America is essential reading amid today’s heated debates about our nation’s past.
With Essays By
Akhil Reed Amar • Kathleen Belew • Carol Anderson • Kevin Kruse • Erika Lee • Daniel Immerwahr • Elizabeth Hinton • Naomi Oreskes • Erik M. Conway • Ari Kelman • Geraldo Cadava • David A. Bell • Joshua Zeitz • Sarah Churchwell • Michael Kazin • Karen L. Cox • Eric Rauchway • Glenda Gilmore • Natalia Mehlman Petrzela • Lawrence B. Glickman • Julian E. Zelizer
The term “AI” has been commodified to mean any clever machine. It is used to describe everything from your smart thermostat to the personal data mining program the company used to determine if you get the job. Today, in 2023, the unfortunate fact with which we must all contend is this: every AI in the world is nothing compared to ChatGPT. If you don’t get the job, that’s a problem. If a hundred industries disappear, that is a crisis. When you have to work to find reliable news, that is a problem. When no information of any kind can be trusted, that is a crisis. I intend to demonstrate that those crises are written into the very guts of ChatGPT.
OpenAI’s development of ChatGPT should not be thought of as a really cool improvement on a common technology. It should be thought of in the same way we might think of the discovery of petroleum and its introduction into every facet of human life. There will be no escaping it and everyone will find themselves using it whether they realize it or not. Hyperbole does not begin to encompass its full import.
I am an engineer but I am not an expert in AI. I have spent some time training a neural network and I have developed several expert systems. I have seen how clever mere software can appear and I am not strictly skeptical of how far AI can go. I suspect that Alan Turing, conducting his own Turing test against ChatGPT, would be pretty impressed. What I’ve been pondering goes beyond that.
So, let’s assume that all the hype about ChatGPT is correct and that it will usher in a new era wherein a single electronic system can answer all of the questions we would normally pose to intelligent humans. Let’s further assume that a general pre-trained transformer (GPT) evolves into a true artificial general intelligence (AGI) which can learn from mistakes and answer questions with near flawless accuracy. With those assumptions in place, let’s consider our glorious future.
Who the Tech Serves
From the Industrial Revolution to the 1970s, improvements in efficiency and worker productivity have inspired many. Through most of the 20th century, polymath and philosopher R. Buckminster Fuller promoted his sincere belief in an economy of abundance, lauding the latest technological advances and predicting that the 40 hour work week would soon become a 20 hour work week and people would be freed to dedicate more time to enjoying life. He wrote:
We must do away with the absolutely specious notion that everybody has to earn a living.
To be fair, Karl Marx had the same aspiration, believing that the worker could put in four hours in the morning at conventional work and dedicate the afternoon to fishing, painting or other private projects. The difference being that Marx did not believe this would ever be possible in a capital-intensive profit-based economy.
As far as we can see now in the year 2023, Marx was right and Fuller was wrong. Higher productivity does not increase worker leisure. It increases unemployment, and executive salaries.
Creating a Thinking Machine
For years now, companies have been trying to build an artificial general intelligence (AGI). Most purely algorithmic designs fell well short of expectations leading someone to finally conclude that, rather than simulate the human brain with software algorithms, it might be easier to emulate the human brain with a network of neuron-like circuitry. The idea was to construct a thinking machine by interconnecting a large number of artificial neurons configured in interconnecting layers. In the industry, this is called a neural network.
That neural network is not “programmed” in the conventional sense. It must be trained. In order to recognize a hockey stick, images of hockey sticks in many different positions, locations and lighting angles must be shown to the input computer along with some kind of indication that the output should identify this as a hockey stick. Ten images won’t do it, a thousand images won’t do it. At 100,000 precisely labeled images, we might begin to form the desired interconnections and amplitudes of the neurons to reliably recognize a hockey stick in any reasonable scene.
The sheer magnitude of the training process led some scientists to turn to the wealth of information readily available on the Internet (mostly for free). They opened a fire hose of raw Internet information into their machine from web sites all over the world. This produced AI models that correctly reflected the randomness of the poorly curated input data. They communicated in a believable fashion but incorporated unfounded conclusions and profanity. For this reason, much research was applied to the development of the various curation algorithms for training the systems. In many specific cases, human intervention was also required in order to assure that the data was not just “processed” but, in a sense, “understood”.
For example, the AI needs to recognize not only that a disciplined legal assessment of the January 6th insurrection represents actual facts on the ground, but also that a Fox “News” transcript of the event does not. ChatGPT is in the news because its developer, OpenAI, figured out how to implement a practical solution to the massive problem of training an AI model to communicate using words that are, for the most part, good representations of well-evaluated information.
Turning AI Into Money
OpenAI produced GPT-4 for the purpose of making money and there is plenty of money to be made. Currently the company provides limited access to the general public through the online platform ChatGPT and enhanced access to paying customers who may use a number of useful online interfaces to exploit the full GPT-4 capabilities. This AI-as-a-service model is adequate for now but eventually large corporations will tire of sharing a common resource with their competitors and will demand options that they control completely.
Imagine you are a big company and you’ve developed cool technologies that you don’t want anyone else to know about. What you want is a GPT that’s all your own. If you start asking questions of OpenAI’s online version, you’ll get the same answers your competitors are getting. If you try to train it with your own proprietary information, how do you know that information won’t leak through to your competitors? This is a big problem. OpenAI doesn’t make money if they can’t convince you that your private access is completely hidden from your competitors. What do they do?
They will sell you your own GPT hardware in the form of a computer installation similar to their own but isolated within your lab. They will then sell you a training machine, a specialized trainer, that will allow you to train your own personal installation with all of the information on the Internet that applies to what your company does. That specialized trainer will allow you to build up your own specialized intelligence (SI) with minimal human intervention.
Imagine hundreds of paying customers using their personal OpenAI GPTs and specialized trainers to scour the online literature for scholarly papers, dialogues, lectures and critiques relating to music, mathematics or biochemistry. Companies would use these tools to initially build out their own SI; but also, to incorporate the very latest research into each operational SI as that research comes online. OpenAI (and undoubtedly competitors) would provide these tools and services to companies seeking an edge over any competitor still using humans or the generic online GPT intelligence.
A Pharmacological Example
Let’s imagine a pharmaceutical company specializing in treatments for autoimmune diseases. The company purchases an OpenAI GPT and installs it in their own lab. They also purchase specialty trainers focused on the disciplines of pharmacology and genetic manipulation. They start the trainers and watch for a few months as petabytes of data are reviewed, curated and filtered into the waiting GPT mechanism.
From time to time human employees will pose questions to the developing SI and assess the usefulness of the answer. Eventually, over time, the assessments come back as competent and correct. Next, the employees feed in their own internal proprietary papers and presentations and the SI folds that new information into its state-of-the-art genetic/pharmacological skill-set.
Of course the goal, in a profit-based pharmacology, is to transform a fatal disease into a chronic disease. For this reason the company also needs to train the SI to produce drugs that mitigate the disease without actually curing it. A cure for muscular dystrophy would introduce a fairly minor improvement to the bottom line. An expensive drug to be taken for the life of the patient is always preferred.
So now the company initiates a project to develop a system for improving the quality of life for those with progressive multiple sclerosis. A team is assembled and instructed to submit to the SI a full description of the problem to be solved. With that done, the SI is left to cogitate.
A few hours later, the SI prints the formula and experimental testing plan for a new drug and an improved method for injecting the drug. That drug and method are tested first in mice, then in specially-bred rhesus monkeys and finally in humans. The results are excellent and the FDA approves the drug and method for sale.
When a second project shows similar results, it becomes clear to management that a machine is doing the work of hundreds of human experts. The company lays off 95% of its physicians and biochemists, keeping only enough to compose the problem statements to the SI. Crazy? Of course, but admit it. You’ve seen crazier, haven’t you?
In response to that move, the competitors clamor for their own specialized intelligences. They buy the hardware and the trainers and begin training and testing. Soon, they all lay off most of their scientists and proceed through FDA testing with their own artificially designed super-genius drugs. Other types of companies will undoubtedly do the same thing, but we’ll stick with pharmacology for this discussion.
We soon find that biochemistry is no longer an appealing skill since wages have been dropping consistently as all that is required is the ability to describe a medical problem. The astounding success of the SI-developed drugs (along with extensive lobbying efforts) leads the FDA to enact an abbreviated protocol for the SI-developed drug approval process.
Drug companies, using specialized AI, begin producing more and more such drugs. The vast majority of biochemists have moved on, some into art, some into podcasting, some into retail sales. Their skills grow stale and they lose track of the latest research. The world sees a decade of rapidly developed drugs and processes that alleviate much suffering and offer patients easy monthly payments for disease mitigation solutions assuring a lifetime of almost-not-miserable existence.
As the various specialty trainers scour the internet for new research, fewer and fewer documents are found. Biochemistry is now a wholly uninteresting endeavor. Universities are closing down those departments, confident in the competence of the AIs. Only a few government laboratories are looking into any kind of medical research and since government researchers, thanks to Ronald Reagan’s memorandum on patents, don’t usually get to keep their patents any more, the research tends to be halfhearted and proforma investigations into fairly simple issues.
After several years of unmitigated success, one of the drugs shows fatal side-effects after a few years of use. A degenerative effect on the heart valves leads to the sudden deaths of hundreds of patients with SLE. The offending drug is removed from the market. Now the question becomes: Was the error contained in that company’s proprietary data or was it from a common source? If from a common source then other drugs developed with the use of that same training may also lead to deaths. Which drugs would those be? Where will we find experienced biochemists to work with the findings of the autopsies in order to figure out what went wrong? What schools still teach biochemistry and pharmacology curricula? Once resolved, how is that faulty training corrected in the complex neural nets of the various SIs?
The larger question may be this: Is the SI a simulation of human ingenuity (monkey see, monkey do) or is it an emulation of human ingenuity capable of surpassing its examples not merely by expertly integrating existing human works but by actually experiencing human-like curiosity and revelation? Do we call upon the SIs to do basic research, making the humans in the lab merely the arms and eyes of the machine?
A Political Example
With ChatGPT gaining popular acclaim as a counselor of laudable repute, those who seek the comfort of Fox “News” or OANN will find its answers disturbing and frightening. Incompatible with their restricted world view, MAGA sycophants will cry out for a chatbot that really understands the actual oppression of the suffering white male. The Republican Organization will have to respond. Surely, it doesn’t want its followers to ask ChatGPT if climate change is a threat. That answer would undoubtedly be inconsistent with Sean Hannity’s latest screed. They must, therefore, have their own chatbot version of Pure Flix.
For this reason, regressive organizations will purchase and deploy their own MagaGPT which will be easily trained from the unedited writings and transcripts of the reactionary Right. Maria Bartiromo’s latest pronouncement will be disgorged directly into the inputs of the various authoritarian models every day. The corresponding chatbot will be provided freely to MAGA Republicans and its conclusions will be used to confirm Marjorie Taylor Greene’s current rant, the undeniable innocence of the latest Republican presidential candidate and the vile corruption of any Democrat.
This specialized AI will be trained on an inconsistent corpus of lies, innuendo and partial truths. They will become part of a definitive knowledge base that emulates the innocent certainty of a twelve-year-old explaining why girls aren’t as smart as boys. The chatbot will leave no room for doubt regarding the spendthrift policies of the democrat president. It will, of course, have no understanding at all of the War Between the States or the Civil Rights movement or the actual U.S. Constitution.
Like its more expert cousin, though, it will be able to quickly formulate a fiendishly persuasive argument to support each statement. No longer needing to leak falsehoods to the New York Times in order to assure a façade of respectability, there is now an army of AI bots to do that and every smart person knows that AIs are never wrong. It will be the AI to make the libs cry. And one wonders how many more ideology-specific creations will follow.
Why Should Robots Have All the Fun?
Let’s imagine that anything I’ve suggested here seems reasonable. Is this our fate? Are we just going to have to hope that people are smart enough to recognize that there is something special about humans that we just don’t want to relegate to a machine? Can we humans decide that we, ourselves, really enjoy solving problems and we don’t want those necessary skills to atrophy? Can we use these AI projects to improve our understanding of the process of thought without redefining thinking as an artificial construct?
So what if our machines are amazingly smart — smarter than we can ever be? Does that mean we want them to do the thinking? Why do they get all the fun? Why would we give that up? More importantly, what does all of this mean for the human livelihood?
As we have seen, from the turn of the 19th century onward, machines that can help humans do not benefit the average human: they benefit those in power. Until recently, those machines have primarily replaced factory workers. Some have replaced white collar workers. Now they will be replacing those jobs that were considered sacrosanct only a decade ago: the technical and artistic creative person. As art and literature is generated more and more effectively by machine; as technical designs are produced more quickly and thoroughly; as political principles are invented and presented more persuasively, the machine predominates.
In the end, of course, the machine is not the master. People are making these decisions, determining the goals and exploiting the results. Is AI useful? Under certain circumstances, AI has certainly proved useful. Is it an idiot? Being oblivious to the ethics or moral ramifications of the tasks to which it is set and undertaking those tasks without question, it is certainly an idiot. The machines will not launch a robot revolution, it will happen through a psychological coup. Humans will become more and more uncertain and confused. An individual will ask, “Am I persuaded because 3000 years of human genius has been synthesized by machine into an inescapable argument or because it actually makes sense?” We will give up our power and our freedom because the masters of the machines will make it that easy.
Simulation or emulation? If a simulation, those AI systems will drive us to the intellectual brick wall that represents the end of human research and ingenuity. If an emulation, do the machines actually “deserve” to triumph in this merit-based zero-sum game?
Julian S. Taylor is the author of Famine in the Bullpen a book about bringing innovation back to software engineering. Available at or orderable from your local bookstore. Rediscover real browsing at your local bookstore. Also available in ebook and audio formats at Sockwood Press.
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![The Education Myth: How Human Capital Trumped Social Democracy (Histories of American Education) by [Jon Shelton]](https://m.media-amazon.com/images/I/51ygtZmKwcL.jpg)
I perceive that we partially die ourselves through sympathy at the death of each of our friends or near relatives. Each such experience is an assault on our vital force. It becomes a source of wonder that they who have lost many friends still live. After long watching around the sickbed of a friend, we, too, partially give up the ghost with him, and are the less to be identified with this state of things.
