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FYI https://www.frontiersin.org/articles/10.3389/fnsyn.2021.635879/full

#science #learning #psychology
 

This infinite #book tower in #Prague. There's a mirror on the bottom and the top of it. It's supposed to represent the #infinity of #learning.


-> https://9gag.com/gag/a8Epzn1

#education #learn #art
 
Many much-learned men have no intelligence.

[Πολλοὶ πολυμαθέες νοῦν οὐκ ἔχουσιν.]
Democritus (c. 460 BC – c. 370 BC) Greek philosopher
Fragment 64 (Diel) (190 N.) [tr. Freeman (1948)]

\#quotation #quote #academia #consultants #degree #dolts #education #intelligence #learning #wisdom
More notes and sourcing on WIST:
Fragment 64 (Diel) (190 N.) [tr. Freeman (1948)]
 
Many much-learned men have no intelligence.

[Πολλοὶ πολυμαθέες νοῦν οὐκ ἔχουσιν.]
Democritus (c. 460 BC – c. 370 BC) Greek philosopher
Fragment 64 (Diel) (190 N.) [tr. Freeman (1948)]

\#quotation #quote #academia #consultants #degree #dolts #education #intelligence #learning #wisdom
More notes and sourcing on WIST:
Fragment 64 (Diel) (190 N.) [tr. Freeman (1948)]
 
the best way to learn gimp ans g'mic is to use it and play with it. Trying out what happens. I can't remember much and I can't remember how I did things. #Sundaygimp was created to share this with many people. This is the best #tutorial for me to see what it can do and how it can be used in different ways. It's a weekly exercise that has less to do with #learning and more to do with #playing and I'm already looking forward to the many wonderful sundaygimps of 2021.
 
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𝓢𝓹𝓻𝓸𝓸𝓴𝓳𝓮𝓼𝓯𝓲𝓰𝓾𝓻𝓮𝓷

Fairy tale characters / Personnages de contes de fées / Märchenfiguren / Personaggi di fiabe

Bild/FotoLeer Nederlands ! / Learn Dutch ! / Apprenez le néerlandais ! / Lernen Sie Niederländisch ! / Imparate l’olandese !
  • Assepoester : Cinderella / Cendrillon / Aschenputtel / Cenerentola
  • Hans en Grietje : Hansel and Gretel / Hansel et Gretel; [parfois]Jeannot et Margot / Hänsel und Gretel / Hansel e Gretel
  • Pinokkio : Pinocchio / Pinocchio / Pinocchio / Pinocchio
  • de Gelaarsde Kat : Puss in Boots / le Chat botté; le Maître chat / der gestiefelte Kater / il gatto con gli stivali
  • Raponsje : Rapunzel / Raiponce / Rapunzel / Raperonzolo
  • Doornroosje : Sleeping Beauty / la Belle au bois dormant / Dornröschen / la bella addormentata nel bosco
  • Sprookjesprins; de prins op het witte paard : Prince Charming / le Prince Charmant / der Märchenprinz; der Prinz auf dem weißen Pferd / il principe azzurro
  • Sneeuwwitje en de zeven dwergen : Snow White and the Seven Dwarfs / Blanche-Neige et les sept nains / Schneewittchen und die sieben Zwerge / Biancaneve e i sette nani
  • Roodkapje : Little Red Riding Hood / le Petit Chaperon rouge / Rotkäppchen / Cappuccetto rosso
  • de boze wolf : the big bad wolf / le grand méchant loup / der böse Wolf / il grosso lupo cattivo; il grande lupo cattivo
  • de drie biggetjes : the three little pigs / les trois petits cochons / die drei (kleinen) Schweinchen / i tre porcellini
  • Klaas Vaak; het Zandmannetje : the Sandman / le marchand de sable / der Sandmann / l'omino del sonno; l'omino dei sogni; l'omino della sabbia; il mago sabbiolino
  • de goede fee : the fairy godmother / la bonne fée; la fée marraine / die gute Fee; die Märchenfee / la fata madrina
  • de boze heks : the wicked witch / la méchante sorcière / die böse Hexe / la strega cattiva; la strega malvagia
  • Klein Duimpje : Tom Thumb / le Petit Poucet / der kleine Däumling / Pollicino
🎄 1) Assepoester, Anton Pieck / 2) Hans en Grietje, Anton Pieck / 3) Pinokkio, Attilio Mussino / 4) De Gelaarsde Kat, Rie Cramer / 5) Raponsje, Marius de Schaar / 6) Doornroosje, Anton Pieck / 7) Sprookjesprins; De prins op het witte paard, Karel Willemen / 8) Sneeuwwitje en de zeven dwergen, Anton Pieck / 9) Roodkapje, Anton Pieck / 10) De boze wolf, Anton Pieck / 11) De drie biggetjes, Guillaume Le Baube / 12) Klaas Vaak; Het Zandmannetje, Pascal Schouten / 13) De goede fee, Rie Cramer / 14) De boze Heks, Hanna Kraan / 15) Klein Duimpje, Rie Cramer 🎄

WOORDENSCHAT
COLLECTIES : #woordenschat docnederlands #culturele aspecten docnl
#nederlands #dutch #néerlandais #hollandais #niederländisch #olandese #neerlandés #woordenschat #vocabulary #vocabulaire #wortschatz #vokabular #vocabolario #vocabulario #leren #learn #learning #apprendre #apprentissage #lernen #imparare #apprendimento #aprender #taal #talen #language #languages #langue #langues #sprache #sprachen #lingua #lingue #lenguas #nt2 #opleiding #educatie #education #éducation #bildung #schulung #educazione #illustratie #illustraties #afbeelding #afbeeldingen #illustration #illustrations #image #images #picture #pictures #bild #bilder #illustrazione #illustrazioni #ilustración #poster #posters #engels #english #frans #français #duits #deutsch #italiaans #italiano #tekening #tekeningen #dessin #dessins #drawing #drawings #zeichnung #zeichnungen #disegno #disegni #dibujo #tekenaar #dessinateur #sketcher #drawer #zeichner #disegnatore #anton pieck #rie cramer #pentekening #pentekeningen #sprookje #sprookjes #fairy-tale #fairy-tales #fairy tale #fairy tales #conte #contes #conte de fées #contes de fées #märchen #fiaba #fiabe #favola #favole
 
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𝓢𝓹𝓻𝓸𝓸𝓴𝓳𝓮𝓼𝓯𝓲𝓰𝓾𝓻𝓮𝓷

Fairy tale characters / Personnages de contes de fées / Märchenfiguren / Personaggi di fiabe

Bild/FotoLeer Nederlands ! / Learn Dutch ! / Apprenez le néerlandais ! / Lernen Sie Niederländisch ! / Imparate l’olandese !
  • Assepoester : Cinderella / Cendrillon / Aschenputtel / Cenerentola
  • Hans en Grietje : Hansel and Gretel / Hansel et Gretel; [parfois]Jeannot et Margot / Hänsel und Gretel / Hansel e Gretel
  • Pinokkio : Pinocchio / Pinocchio / Pinocchio / Pinocchio
  • de Gelaarsde Kat : Puss in Boots / le Chat botté; le Maître chat / der gestiefelte Kater / il gatto con gli stivali
  • Raponsje : Rapunzel / Raiponce / Rapunzel / Raperonzolo
  • Doornroosje : Sleeping Beauty / la Belle au bois dormant / Dornröschen / la bella addormentata nel bosco
  • Sprookjesprins; de prins op het witte paard : Prince Charming / le Prince Charmant / der Märchenprinz; der Prinz auf dem weißen Pferd / il principe azzurro
  • Sneeuwwitje en de zeven dwergen : Snow White and the Seven Dwarfs / Blanche-Neige et les sept nains / Schneewittchen und die sieben Zwerge / Biancaneve e i sette nani
  • Roodkapje : Little Red Riding Hood / le Petit Chaperon rouge / Rotkäppchen / Cappuccetto rosso
  • de boze wolf : the big bad wolf / le grand méchant loup / der böse Wolf / il grosso lupo cattivo; il grande lupo cattivo
  • de drie biggetjes : the three little pigs / les trois petits cochons / die drei (kleinen) Schweinchen / i tre porcellini
  • Klaas Vaak; het Zandmannetje : the Sandman / le marchand de sable / der Sandmann / l'omino del sonno; l'omino dei sogni; l'omino della sabbia; il mago sabbiolino
  • de goede fee : the fairy godmother / la bonne fée; la fée marraine / die gute Fee; die Märchenfee / la fata madrina
  • de boze heks : the wicked witch / la méchante sorcière / die böse Hexe / la strega cattiva; la strega malvagia
  • Klein Duimpje : Tom Thumb / le Petit Poucet / der kleine Däumling / Pollicino
🎄 1) Assepoester, Anton Pieck / 2) Hans en Grietje, Anton Pieck / 3) Pinokkio, Attilio Mussino / 4) De Gelaarsde Kat, Rie Cramer / 5) Raponsje, Marius de Schaar / 6) Doornroosje, Anton Pieck / 7) Sprookjesprins; De prins op het witte paard, Karel Willemen / 8) Sneeuwwitje en de zeven dwergen, Anton Pieck / 9) Roodkapje, Anton Pieck / 10) De boze wolf, Anton Pieck / 11) De drie biggetjes, Guillaume Le Baube / 12) Klaas Vaak; Het Zandmannetje, Pascal Schouten / 13) De goede fee, Rie Cramer / 14) De boze Heks, Hanna Kraan / 15) Klein Duimpje, Rie Cramer 🎄

WOORDENSCHAT
COLLECTIES : #woordenschat docnederlands #culturele aspecten docnl
#nederlands #dutch #néerlandais #hollandais #niederländisch #olandese #neerlandés #woordenschat #vocabulary #vocabulaire #wortschatz #vokabular #vocabolario #vocabulario #leren #learn #learning #apprendre #apprentissage #lernen #imparare #apprendimento #aprender #taal #talen #language #languages #langue #langues #sprache #sprachen #lingua #lingue #lenguas #nt2 #opleiding #educatie #education #éducation #bildung #schulung #educazione #illustratie #illustraties #afbeelding #afbeeldingen #illustration #illustrations #image #images #picture #pictures #bild #bilder #illustrazione #illustrazioni #ilustración #poster #posters #engels #english #frans #français #duits #deutsch #italiaans #italiano #tekening #tekeningen #dessin #dessins #drawing #drawings #zeichnung #zeichnungen #disegno #disegni #dibujo #tekenaar #dessinateur #sketcher #drawer #zeichner #disegnatore #anton pieck #rie cramer #pentekening #pentekeningen #sprookje #sprookjes #fairy-tale #fairy-tales #fairy tale #fairy tales #conte #contes #conte de fées #contes de fées #märchen #fiaba #fiabe #favola #favole
 
Building a better GoodReads with ActivityPub

Nilesh Trivedi

I have been building LearnAwesome.org as an ActivityPub compliant equivalent of GoodReads for learning. It’s both a repository of learning resources (books, but also blogs, courses, podcasts, newsletters, livestreams etc) and a social network of lifelong learners. It also supports #ActivityPub […]

VIDEO
https://conf.tube/videos/watch/c42604a8-d71d-4bd0-8081-d2c77210f206

Q&A SAT 15:30
remote questions:
https://socialhub.activitypub.rocks/t/learnawesome-org-building-a-better-goodreads-with-activitypub/946/1

#LearnAwesome #learning #software #apconf2020
 
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Your Professional Decline Is Coming (Much) Sooner Than You Think


Here’s how to make the most of it.

Story by Arthur C. Brooks
July 2019 Issue

“It’s not true that no one needs you anymore.”

These words came from an elderly woman sitting behind me on a late-night flight from Los Angeles to Washington, D.C. The plane was dark and quiet. A man I assumed to be her husband murmured almost inaudibly in response, something to the effect of “I wish I was dead.”

Again, the woman: “Oh, stop saying that.”

I didn’t mean to eavesdrop, but couldn’t help it. I listened with morbid fascination, forming an image of the man in my head as they talked. I imagined someone who had worked hard all his life in relative obscurity, someone with unfulfilled dreams—perhaps of the degree he never attained, the career he never pursued, the company he never started.

At the end of the flight, as the lights switched on, I finally got a look at the desolate man. I was shocked. I recognized him—he was, and still is, world-famous. Then in his mid‑80s, he was beloved as a hero for his courage, patriotism, and accomplishments many decades ago.

As he walked up the aisle of the plane behind me, other passengers greeted him with veneration. Standing at the door of the cockpit, the pilot stopped him and said, “Sir, I have admired you since I was a little boy.” The older man—apparently wishing for death just a few minutes earlier—beamed with pride at the recognition of his past glories.

For selfish reasons, I couldn’t get the cognitive dissonance of that scene out of my mind. It was the summer of 2015, shortly after my 51st birthday. I was not world-famous like the man on the plane, but my professional life was going very well. I was the president of a flourishing Washington think tank, the American Enterprise Institute. I had written some best-selling books. People came to my speeches. My columns were published in The New York Times.

But I had started to wonder: Can I really keep this going? I work like a maniac. But even if I stayed at it 12 hours a day, seven days a week, at some point my career would slow and stop. And when it did, what then? Would I one day be looking back wistfully and wishing I were dead? Was there anything I could do, starting now, to give myself a shot at avoiding misery—and maybe even achieve happiness—when the music inevitably stops?

Though these questions were personal, I decided to approach them as the social scientist I am, treating them as a research project. It felt unnatural—like a surgeon taking out his own appendix. But I plunged ahead, and for the past four years, I have been on a quest to figure out how to turn my eventual professional decline from a matter of dread into an opportunity for progress.

Here’s what I’ve found.

The field of “happiness studies” has boomed over the past two decades, and a consensus has developed about well-being as we advance through life. In The Happiness Curve: Why Life Gets Better After 50, Jonathan Rauch, a Brookings Institution scholar and an Atlantic contributing editor, reviews the strong evidence suggesting that the happiness of most adults declines through their 30s and 40s, then bottoms out in their early 50s. Nothing about this pattern is set in stone, of course. But the data seem eerily consistent with my experience: My 40s and early 50s were not an especially happy period of my life, notwithstanding my professional fortunes.

So what can people expect after that, based on the data? The news is mixed. Almost all studies of happiness over the life span show that, in wealthier countries, most people’s contentment starts to increase again in their 50s, until age 70 or so. That is where things get less predictable, however. After 70, some people stay steady in happiness; others get happier until death. Others—men in particular—see their happiness plummet. Indeed, depression and suicide rates for men increase after age 75.

This last group would seem to include the hero on the plane. A few researchers have looked at this cohort to understand what drives their unhappiness. It is, in a word, irrelevance. In 2007, a team of academic researchers at UCLA and Princeton analyzed data on more than 1,000 older adults. Their findings, published in the Journal of Gerontology, showed that senior citizens who rarely or never “felt useful” were nearly three times as likely as those who frequently felt useful to develop a mild disability, and were more than three times as likely to have died during the course of the study.

One might think that gifted and accomplished people, such as the man on the plane, would be less susceptible than others to this sense of irrelevance; after all, accomplishment is a well-documented source of happiness. If current accomplishment brings happiness, then shouldn’t the memory of that accomplishment provide some happiness as well?

Maybe not. Though the literature on this question is sparse, giftedness and achievements early in life do not appear to provide an insurance policy against suffering later on. In 1999, Carole Holahan and Charles Holahan, psychologists at the University of Texas, published an influential paper in The International Journal of Aging and Human Development that looked at hundreds of older adults who early in life had been identified as highly gifted. The Holahans’ conclusion: “Learning at a younger age of membership in a study of intellectual giftedness was related to … less favorable psychological well-being at age eighty.”

This study may simply be showing that it’s hard to live up to high expectations, and that telling your kid she is a genius is not necessarily good parenting. (The Holahans surmise that the children identified as gifted might have made intellectual ability more central to their self-appraisal, creating “unrealistic expectations for success” and causing them to fail to “take into account the many other life influences on success and recognition.”) However, abundant evidence suggests that the waning of ability in people of high accomplishment is especially brutal psychologically. Consider professional athletes, many of whom struggle profoundly after their sports career ends. Tragic examples abound, involving depression, addiction, or suicide; unhappiness in retired athletes may even be the norm, at least temporarily. A study published in the Journal of Applied Sport Psychology in 2003, which charted the life satisfaction of former Olympic athletes, found that they generally struggled with a low sense of personal control when they first stopped competing.

Recently, I asked Dominique Dawes, a former Olympic gold-medal gymnast, how normal life felt after competing and winning at the highest levels. She told me that she is happy, but that the adjustment wasn’t easy—and still isn’t, even though she won her last Olympic medal in 2000. “My Olympic self would ruin my marriage and leave my kids feeling inadequate,” she told me, because it is so demanding and hard-driving. “Living life as if every day is an Olympics only makes those around me miserable.”

Why might former elite performers have such a hard time? No academic research has yet proved this, but I strongly suspect that the memory of remarkable ability, if that is the source of one’s self-worth, might, for some, provide an invidious contrast to a later, less remarkable life. “Unhappy is he who depends on success to be happy,” Alex Dias Ribeiro, a former Formula 1 race-car driver, once wrote. “For such a person, the end of a successful career is the end of the line. His destiny is to die of bitterness or to search for more success in other careers and to go on living from success to success until he falls dead. In this case, there will not be life after success.”

Call it the Principle of Psychoprofessional Gravitation: the idea that the agony of professional oblivion is directly related to the height of professional prestige previously achieved, and to one’s emotional attachment to that prestige. Problems related to achieving professional success might appear to be a pretty good species of problem to have; even raising this issue risks seeming precious. But if you reach professional heights and are deeply invested in being high up, you can suffer mightily when you inevitably fall. That’s the man on the plane. Maybe that will be you, too. And, without significant intervention, I suspect it will be me.

The Principle of Psychoprofessional Gravitation can help explain the many cases of people who have done work of world-historical significance yet wind up feeling like failures. Take Charles Darwin, who was just 22 when he set out on his five-year voyage aboard the Beagle in 1831. Returning at 27, he was celebrated throughout Europe for his discoveries in botany and zoology, and for his early theories of evolution. Over the next 30 years, Darwin took enormous pride in sitting atop the celebrity-scientist pecking order, developing his theories and publishing them as books and essays—the most famous being On the Origin of Species, in 1859.

But as Darwin progressed into his 50s, he stagnated; he hit a wall in his research. At the same time an Austrian monk by the name of Gregor Mendel discovered what Darwin needed to continue his work: the theory of genetic inheritance. Unfortunately, Mendel’s work was published in an obscure academic journal and Darwin never saw it—and in any case, Darwin did not have the mathematical ability to understand it. From then on he made little progress. Depressed in his later years, he wrote to a close friend, “I have not the heart or strength at my age to begin any investigation lasting years, which is the only thing which I enjoy.”

Presumably, Darwin would be pleasantly surprised to learn how his fame grew after his death, in 1882. From what he could see when he was old, however, the world had passed him by, and he had become irrelevant. That could have been Darwin on the plane behind me that night.

It also could have been a younger version of me, because I have had precocious experience with professional decline.

As a child, I had just one goal: to be the world’s greatest French-horn player. I worked at it slavishly, practicing hours a day, seeking out the best teachers, and playing in any ensemble I could find. I had pictures of famous horn players on my bedroom wall for inspiration. And for a while, I thought my dream might come true. At 19, I left college to take a job playing professionally in a touring chamber-music ensemble. My plan was to keep rising through the classical-music ranks, joining a top symphony orchestra in a few years or maybe even becoming a soloist—the most exalted job a classical musician can hold.

But then, in my early 20s, a strange thing happened: I started getting worse. To this day, I have no idea why. My technique began to suffer, and I had no explanation for it. Nothing helped. I visited great teachers and practiced more, but I couldn’t get back to where I had been. Pieces that had been easy to play became hard; pieces that had been hard became impossible.

The data are shockingly clear that for most people, in most fields, professional decline starts earlier than almost anyone thinks.

Perhaps the worst moment in my young but flailing career came at age 22, when I was performing at Carnegie Hall. While delivering a short speech about the music I was about to play, I stepped forward, lost my footing, and fell off the stage into the audience. On the way home from the concert, I mused darkly that the experience was surely a message from God.

But I sputtered along for nine more years. I took a position in the City Orchestra of Barcelona, where I increased my practicing but my playing gradually deteriorated. Eventually I found a job teaching at a small music conservatory in Florida, hoping for a magical turnaround that never materialized. Realizing that maybe I ought to hedge my bets, I went back to college via distance learning, and earned my bachelor’s degree shortly before my 30th birthday. I secretly continued my studies at night, earning a master’s degree in economics a year later. Finally I had to admit defeat: I was never going to turn around my faltering musical career. So at 31 I gave up, abandoning my musical aspirations entirely, to pursue a doctorate in public policy.

Life goes on, right? Sort of. After finishing my studies, I became a university professor, a job I enjoyed. But I still thought every day about my beloved first vocation. Even now, I regularly dream that I am onstage, and wake to remember that my childhood aspirations are now only phantasms.

I am lucky to have accepted my decline at a young enough age that I could redirect my life into a new line of work. Still, to this day, the sting of that early decline makes these words difficult to write. I vowed to myself that it wouldn’t ever happen again.

Will it happen again? In some professions, early decline is inescapable. No one expects an Olympic athlete to remain competitive until age 60. But in many physically nondemanding occupations, we implicitly reject the inevitability of decline before very old age. Sure, our quads and hamstrings may weaken a little as we age. But as long as we retain our marbles, our quality of work as a writer, lawyer, executive, or entrepreneur should remain high up to the very end, right? Many people think so. I recently met a man a bit older than I am who told me he planned to “push it until the wheels came off.” In effect, he planned to stay at the very top of his game by any means necessary, and then keel over.

But the odds are he won’t be able to. The data are shockingly clear that for most people, in most fields, decline starts earlier than almost anyone thinks.

According to research by Dean Keith Simonton, a professor emeritus of psychology at UC Davis and one of the world’s leading experts on the trajectories of creative careers, success and productivity increase for the first 20 years after the inception of a career, on average. So if you start a career in earnest at 30, expect to do your best work around 50 and go into decline soon after that.

The specific timing of peak and decline vary somewhat depending on the field. Benjamin Jones, a professor of strategy and entrepreneurship at Northwestern University’s Kellogg School of Management, has spent years studying when people are most likely to make prizewinning scientific discoveries and develop key inventions. His findings can be summarized by this little ditty:

Age is, of course, a fever chill
that every physicist must fear.
He’s better dead than living still
when once he’s past his thirtieth year.

The author of those gloomy lines? Paul Dirac, a winner of the 1933 Nobel Prize in Physics.

Dirac overstates the point, but only a little. Looking at major inventors and Nobel winners going back more than a century, Jones has found that the most common age for producing a magnum opus is the late 30s. He has shown that the likelihood of a major discovery increases steadily through one’s 20s and 30s and then declines through one’s 40s, 50s, and 60s. Are there outliers? Of course. But the likelihood of producing a major innovation at age 70 is approximately what it was at age 20—almost nonexistent.

Much of literary achievement follows a similar pattern. Simonton has shown that poets peak in their early 40s. Novelists generally take a little longer. When Martin Hill Ortiz, a poet and novelist, collected data on New York Times fiction best sellers from 1960 to 2015, he found that authors were likeliest to reach the No. 1 spot in their 40s and 50s. Despite the famous productivity of a few novelists well into old age, Ortiz shows a steep drop-off in the chance of writing a best seller after the age of 70. (Some nonfiction writers—especially historians—peak later, as we shall see in a minute.)

Whole sections of bookstores are dedicated to becoming successful. There is no section marked “managing your professional decline.”

Entrepreneurs peak and decline earlier, on average. After earning fame and fortune in their 20s, many tech entrepreneurs are in creative decline by age 30. In 2014, the Harvard Business Review reported that founders of enterprises valued at $1 billion or more by venture capitalists tend to cluster in the 20-to-34 age range. Subsequent research has found that the clustering might be slightly later, but all studies in this area have found that the majority of successful start-ups have founders under age 50.

This research concerns people at the very top of professions that are atypical. But the basic finding appears to apply more broadly. Scholars at Boston College’s Center for Retirement Research studied a wide variety of jobs and found considerable susceptibility to age-related decline in fields ranging from policing to nursing. Other research has found that the best-performing home-plate umpires in Major League Baseball have 18 years less experience and are 23 years younger than the worst-performing umpires (who are 56.1 years old, on average). Among air traffic controllers, the age-related decline is so sharp—and the potential consequences of decline-related errors so dire—that the mandatory retirement age is 56.

In sum, if your profession requires mental processing speed or significant analytic capabilities—the kind of profession most college graduates occupy—noticeable decline is probably going to set in earlier than you imagine.

Sorry.

If decline not only is inevitable but also happens earlier than most of us expect, what should we do when it comes for us?

Whole sections of bookstores are dedicated to becoming successful. The shelves are packed with titles like The Science of Getting Rich and The 7 Habits of Highly Effective People. There is no section marked “Managing Your Professional Decline.”

But some people have managed their declines well. Consider the case of Johann Sebastian Bach. Born in 1685 to a long line of prominent musicians in central Germany, Bach quickly distinguished himself as a musical genius. In his 65 years, he published more than 1,000 compositions for all the available instrumentations of his day.

Early in his career, Bach was considered an astoundingly gifted organist and improviser. Commissions rolled in; royalty sought him out; young composers emulated his style. He enjoyed real prestige.

But it didn’t last—in no small part because his career was overtaken by musical trends ushered in by, among others, his own son, Carl Philipp Emanuel, known as C.P.E. to the generations that followed. The fifth of Bach’s 20 children, C.P.E. exhibited the musical gifts his father had. He mastered the baroque idiom, but he was more fascinated with a new “classical” style of music, which was taking Europe by storm. As classical music displaced baroque, C.P.E.’s prestige boomed while his father’s music became passé.

Bach easily could have become embittered, like Darwin. Instead, he chose to redesign his life, moving from innovator to instructor. He spent a good deal of his last 10 years writing The Art of Fugue, not a famous or popular work in his time, but one intended to teach the techniques of the baroque to his children and students—and, as unlikely as it seemed at the time, to any future generations that might be interested. In his later years, he lived a quieter life as a teacher and a family man.

What’s the difference between Bach and Darwin? Both were preternaturally gifted and widely known early in life. Both attained permanent fame posthumously. Where they differed was in their approach to the midlife fade. When Darwin fell behind as an innovator, he became despondent and depressed; his life ended in sad inactivity. When Bach fell behind, he reinvented himself as a master instructor. He died beloved, fulfilled, and—though less famous than he once had been—respected.

The lesson for you and me, especially after 50: Be Johann Sebastian Bach, not Charles Darwin.

How does one do that?

A potential answer lies in the work of the British psychologist Raymond Cattell, who in the early 1940s introduced the concepts of fluid and crystallized intelligence. Cattell defined fluid intelligence as the ability to reason, analyze, and solve novel problems—what we commonly think of as raw intellectual horsepower. Innovators typically have an abundance of fluid intelligence. It is highest relatively early in adulthood and diminishes starting in one’s 30s and 40s. This is why tech entrepreneurs, for instance, do so well so early, and why older people have a much harder time innovating.

Crystallized intelligence, in contrast, is the ability to use knowledge gained in the past. Think of it as possessing a vast library and understanding how to use it. It is the essence of wisdom. Because crystallized intelligence relies on an accumulating stock of knowledge, it tends to increase through one’s 40s, and does not diminish until very late in life.

Careers that rely primarily on fluid intelligence tend to peak early, while those that use more crystallized intelligence peak later. For example, Dean Keith Simonton has found that poets—highly fluid in their creativity—tend to have produced half their lifetime creative output by age 40 or so. Historians—who rely on a crystallized stock of knowledge—don’t reach this milestone until about 60.

Here’s a practical lesson we can extract from all this: No matter what mix of intelligence your field requires, you can always endeavor to weight your career away from innovation and toward the strengths that persist, or even increase, later in life.

Like what? As Bach demonstrated, teaching is an ability that decays very late in life, a principal exception to the general pattern of professional decline over time. A study in The Journal of Higher Education showed that the oldest college professors in disciplines requiring a large store of fixed knowledge, specifically the humanities, tended to get evaluated most positively by students. This probably explains the professional longevity of college professors, three-quarters of whom plan to retire after age 65—more than half of them after 70, and some 15 percent of them after 80. (The average American retires at 61.) One day, during my first year as a professor, I asked a colleague in his late 60s whether he’d ever considered retiring. He laughed, and told me he was more likely to leave his office horizontally than vertically.

I need a reverse bucket list. My goal for each year of the rest of my life should be to throw out things, obligations, and relationships.

Our dean might have chuckled ruefully at this—college administrators complain that research productivity among tenured faculty drops off significantly in the last decades of their career. Older professors take up budget slots that could otherwise be used to hire young scholars hungry to do cutting-edge research. But perhaps therein lies an opportunity: If older faculty members can shift the balance of their work from research to teaching without loss of professional prestige, younger faculty members can take on more research.

Patterns like this match what I’ve seen as the head of a think tank full of scholars of all ages. There are many exceptions, but the most profound insights tend to come from those in their 30s and early 40s. The best synthesizers and explainers of complicated ideas—that is, the best teachers—tend to be in their mid-60s or older, some of them well into their 80s.

That older people, with their stores of wisdom, should be the most successful teachers seems almost cosmically right. No matter what our profession, as we age we can dedicate ourselves to sharing knowledge in some meaningful way.

A few years ago, I saw a cartoon of a man on his deathbed saying, “I wish I’d bought more crap.” It has always amazed me that many wealthy people keep working to increase their wealth, amassing far more money than they could possibly spend or even usefully bequeath. One day I asked a wealthy friend why this is so. Many people who have gotten rich know how to measure their self-worth only in pecuniary terms, he explained, so they stay on the hamster wheel, year after year. They believe that at some point, they will finally accumulate enough to feel truly successful, happy, and therefore ready to die.

This is a mistake, and not a benign one. Most Eastern philosophy warns that focusing on acquisition leads to attachment and vanity, which derail the search for happiness by obscuring one’s essential nature. As we grow older, we shouldn’t acquire more, but rather strip things away to find our true selves—and thus, peace.

At some point, writing one more book will not add to my life satisfaction; it will merely stave off the end of my book-writing career. The canvas of my life will have another brushstroke that, if I am being forthright, others will barely notice, and will certainly not appreciate very much. The same will be true for most other markers of my success.

What I need to do, in effect, is stop seeing my life as a canvas to fill, and start seeing it more as a block of marble to chip away at and shape something out of. I need a reverse bucket list. My goal for each year of the rest of my life should be to throw out things, obligations, and relationships until I can clearly see my refined self in its best form.

And that self is … who, exactly?

Last year, the search for an answer to this question took me deep into the South Indian countryside, to a town called Palakkad, near the border between the states of Kerala and Tamil Nadu. I was there to meet the guru Sri Nochur Venkataraman, known as Acharya (“Teacher”) to his disciples. Acharya is a quiet, humble man dedicated to helping people attain enlightenment; he has no interest in Western techies looking for fresh start-up ideas or burnouts trying to escape the religious traditions they were raised in. Satisfied that I was neither of those things, he agreed to talk with me.

I told him my conundrum: Many people of achievement suffer as they age, because they lose their abilities, gained over many years of hard work. Is this suffering inescapable, like a cosmic joke on the proud? Or is there a loophole somewhere—a way around the suffering?

Acharya answered elliptically, explaining an ancient Hindu teaching about the stages of life, or ashramas. The first is Brahmacharya, the period of youth and young adulthood dedicated to learning. The second is Grihastha, when a person builds a career, accumulates wealth, and creates a family. In this second stage, the philosophers find one of life’s most common traps: People become attached to earthly rewards—money, power, sex, prestige—and thus try to make this stage last a lifetime.

The antidote to these worldly temptations is Vanaprastha, the third ashrama, whose name comes from two Sanskrit words meaning “retiring” and “into the forest.” This is the stage, usually starting around age 50, in which we purposefully focus less on professional ambition, and become more and more devoted to spirituality, service, and wisdom. This doesn’t mean that you need to stop working when you turn 50—something few people can afford to do—only that your life goals should adjust.

Vanaprastha is a time for study and training for the last stage of life, Sannyasa, which should be totally dedicated to the fruits of enlightenment. In times past, some Hindu men would leave their family in old age, take holy vows, and spend the rest of their life at the feet of masters, praying and studying. Even if sitting in a cave at age 75 isn’t your ambition, the point should still be clear: As we age, we should resist the conventional lures of success in order to focus on more transcendentally important things.

I told Acharya the story about the man on the plane. He listened carefully, and thought for a minute. “He failed to leave Grihastha,” he told me. “He was addicted to the rewards of the world.” He explained that the man’s self-worth was probably still anchored in the memories of professional successes many years earlier, his ongoing recognition purely derivative of long-lost skills. Any glory today was a mere shadow of past glories. Meanwhile, he’d completely skipped the spiritual development of Vanaprastha, and was now missing out on the bliss of Sannyasa.

There is a message in this for those of us suffering from the Principle of Psychoprofessional Gravitation. Say you are a hard-charging, type-A lawyer, executive, entrepreneur, or—hypothetically, of course—president of a think tank. From early adulthood to middle age, your foot is on the gas, professionally. Living by your wits—by your fluid intelligence—you seek the material rewards of success, you attain a lot of them, and you are deeply attached to them. But the wisdom of Hindu philosophy—and indeed the wisdom of many philosophical traditions—suggests that you should be prepared to walk away from these rewards before you feel ready. Even if you’re at the height of your professional prestige, you probably need to scale back your career ambitions in order to scale up your metaphysical ones.

When the New York Times columnist David Brooks talks about the difference between “résumé virtues” and “eulogy virtues,” he’s effectively putting the ashramas in a practical context. Résumé virtues are professional and oriented toward earthly success. They require comparison with others. Eulogy virtues are ethical and spiritual, and require no comparison. Your eulogy virtues are what you would want people to talk about at your funeral. As in He was kind and deeply spiritual, not He made senior vice president at an astonishingly young age and had a lot of frequent-flier miles.

You won’t be around to hear the eulogy, but the point Brooks makes is that we live the most fulfilling life—especially once we reach midlife—by pursuing the virtues that are most meaningful to us.

I suspect that my own terror of professional decline is rooted in a fear of death—a fear that, even if it is not conscious, motivates me to act as if death will never come by denying any degradation in my résumé virtues. This denial is destructive, because it leads me to ignore the eulogy virtues that bring me the greatest joy.

The biggest mistake professionally successful people make is attempting to sustain peak accomplishment indefinitely.

How can I overcome this tendency? The Buddha recommends, of all things, corpse meditation: Many Theravada Buddhist monasteries in Thailand and Sri Lanka display photos of corpses in various states of decomposition for the monks to contemplate. “This body, too,” students are taught to say about their own body, “such is its nature, such is its future, such is its unavoidable fate.” At first this seems morbid. But its logic is grounded in psychological principles—and it’s not an exclusively Eastern idea. “To begin depriving death of its greatest advantage over us,” Michel de Montaigne wrote in the 16th century, “let us deprive death of its strangeness, let us frequent it, let us get used to it; let us have nothing more often in mind than death.”

Psychologists call this desensitization, in which repeated exposure to something repellent or frightening makes it seem ordinary, prosaic, not scary. And for death, it works. In 2017, a team of researchers at several American universities recruited volunteers to imagine they were terminally ill or on death row, and then to write blog posts about either their imagined feelings or their would-be final words. The researchers then compared these expressions with the writings and last words of people who were actually dying or facing capital punishment. The results, published in Psychological Science, were stark: The words of the people merely imagining their imminent death were three times as negative as those of the people actually facing death—suggesting that, counterintuitively, death is scarier when it is theoretical and remote than when it is a concrete reality closing in.

For most people, actively contemplating our demise so that it is present and real (rather than avoiding the thought of it via the mindless pursuit of worldly success) can make death less frightening; embracing death reminds us that everything is temporary, and can make each day of life more meaningful. “Death destroys a man,” E. M. Forster wrote, but “the idea of Death saves him.”

Decline is inevitable, and it occurs earlier than almost any of us wants to believe. But misery is not inevitable. Accepting the natural cadence of our abilities sets up the possibility of transcendence, because it allows the shifting of attention to higher spiritual and life priorities.

But such a shift demands more than mere platitudes. I embarked on my research with the goal of producing a tangible road map to guide me during the remaining years of my life. This has yielded four specific commitments.

JUMP

The biggest mistake professionally successful people make is attempting to sustain peak accomplishment indefinitely, trying to make use of the kind of fluid intelligence that begins fading relatively early in life. This is impossible. The key is to enjoy accomplishments for what they are in the moment, and to walk away perhaps before I am completely ready—but on my own terms.

So: I’ve resigned my job as president of the American Enterprise Institute, effective right about the time this essay is published. While I have not detected deterioration in my performance, it was only a matter of time. Like many executive positions, the job is heavily reliant on fluid intelligence. Also, I wanted freedom from the consuming responsibilities of that job, to have time for more spiritual pursuits. In truth, this decision wasn’t entirely about me. I love my institution and have seen many others like it suffer when a chief executive lingered too long.

Leaving something you love can feel a bit like a part of you is dying. In Tibetan Buddhism, there is a concept called bardo, which is a state of existence between death and rebirth—“like a moment when you step toward the edge of a precipice,” as a famous Buddhist teacher puts it. I am letting go of a professional life that answers the question Who am I?

I am extremely fortunate to have the means and opportunity to be able to walk away from a job. Many people cannot afford to do that. But you don’t necessarily have to quit your job; what’s important is striving to detach progressively from the most obvious earthly rewards—power, fame and status, money—even if you continue to work or advance a career. The real trick is walking into the next stage of life, Vanaprastha, to conduct the study and training that prepare us for fulfillment in life’s final stage.

SERVE

Time is limited, and professional ambition crowds out things that ultimately matter more. To move from résumé virtues to eulogy virtues is to move from activities focused on the self to activities focused on others. This is not easy for me; I am a naturally egotistical person. But I have to face the fact that the costs of catering to selfishness are ruinous—and I now work every day to fight this tendency.

Fortunately, an effort to serve others can play to our strengths as we age. Remember, people whose work focuses on teaching or mentorship, broadly defined, peak later in life. I am thus moving to a phase in my career in which I can dedicate myself fully to sharing ideas in service of others, primarily by teaching at a university. My hope is that my most fruitful years lie ahead.

WORSHIP

Because I’ve talked a lot about various religious and spiritual traditions—and emphasized the pitfalls of overinvestment in career success—readers might naturally conclude that I am making a Manichaean separation between the worlds of worship and work, and suggesting that the emphasis be on worship. That is not my intention. I do strongly recommend that each person explore his or her spiritual self—I plan to dedicate a good part of the rest of my life to the practice of my own faith, Roman Catholicism. But this is not incompatible with work; on the contrary, if we can detach ourselves from worldly attachments and redirect our efforts toward the enrichment and teaching of others, work itself can become a transcendental pursuit.

“The aim and final end of all music,” Bach once said, “should be none other than the glory of God and the refreshment of the soul.” Whatever your metaphysical convictions, refreshment of the soul can be the aim of your work, like Bach’s.

Bach finished each of his manuscripts with the words Soli Deo gloria — “Glory to God alone.” He failed, however, to write these words on his last manuscript, “Contrapunctus 14,” from The Art of Fugue, which abruptly stops mid-measure. His son C.P.E. added these words to the score: “Über dieser Fuge … ist der Verfasser gestorben” (“At this point in the fugue … the composer died”). Bach’s life and work merged with his prayers as he breathed his last breath. This is my aspiration.

CONNECT

Throughout this essay, I have focused on the effect that the waning of my work prowess will have on my happiness. But an abundance of research strongly suggests that happiness—not just in later years but across the life span—is tied directly to the health and plentifulness of one’s relationships. Pushing work out of its position of preeminence—sooner rather than later—to make space for deeper relationships can provide a bulwark against the angst of professional decline.

Dedicating more time to relationships, and less to work, is not inconsistent with continued achievement. “He is like a tree planted by streams of water,” the Book of Psalms says of the righteous person, “yielding its fruit in season, whose leaf does not wither, and who prospers in all he does.” Think of an aspen tree. To live a life of extraordinary accomplishment is—like the tree—to grow alone, reach majestic heights alone, and die alone. Right?

Wrong. The aspen tree is an excellent metaphor for a successful person—but not, it turns out, for its solitary majesty. Above the ground, it may appear solitary. Yet each individual tree is part of an enormous root system, which is together one plant. In fact, an aspen is one of the largest living organisms in the world; a single grove in Utah, called Pando, spans 106 acres and weighs an estimated 13 million pounds.

The secret to bearing my decline—to enjoying it—is to become more conscious of the roots linking me to others. If I have properly developed the bonds of love among my family and friends, my own withering will be more than offset by blooming in others.

When I talk about this personal research project I’ve been pursuing, people usually ask: Whatever happened to the hero on the plane?

I think about him a lot. He’s still famous, popping up in the news from time to time. Early on, when I saw a story about him, I would feel a flash of something like pity—which I now realize was really only a refracted sense of terror about my own future. Poor guy really meant I’m screwed.

But as my grasp of the principles laid out in this essay has deepened, my fear has declined proportionately. My feeling toward the man on the plane is now one of gratitude for what he taught me. I hope that he can find the peace and joy he is inadvertently helping me attain.

(c) Arthur C. Brooks is a contributing writer at The Atlantic, a professor of the practice of public leadership at the Harvard Kennedy School, and a senior fellow at the Harvard Business School.

LINK: https://www.theatlantic.com/magazine/archive/2019/07/work-peak-professional-decline/590650/

RU_ru: https://habr.com/ru/post/503530/

#life #work #decline #research #essay #news #happiness #social #literature #science #learning #study #mind #freedom #free #photo #lang ru #professional #carrer #depression #buddhism #enlightenment #religious #traditions #religion #psychology #story #success #progress #development
 

Scotland eyes outdoor learning as model for reopening of schools | UK news | The Guardian

Outdoor learning could offer a template for socially distanced schooling across Scotland, according to practitioners who believe the coronavirus pandemic could push parents and teachers to embrace the benefits of education in the outdoors.
What a great idea! For the little ones, at least. And it could probably work for most of the UK right up until October. But it probably won't happen because of staff shortages.

#education #learning #Covid-19 #CoronaVirus #Scotland
 

Scotland eyes outdoor learning as model for reopening of schools | UK news | The Guardian

Outdoor learning could offer a template for socially distanced schooling across Scotland, according to practitioners who believe the coronavirus pandemic could push parents and teachers to embrace the benefits of education in the outdoors.
What a great idea! For the little ones, at least. And it could probably work for most of the UK right up until October. But it probably won't happen because of staff shortages.

#education #learning #Covid-19 #CoronaVirus #Scotland
 

Kial mi lernas Esperanton? | Why am I learning Esperanto?


| esperanto | anglan |
|----|----|
|Ĉi-jare mi eklernis Esperanto. Mi komencis lerni ĝin pro scivolemo kaj mi tre ŝatas lerni ĝin. Mi lernas ĝin en la angla, kiu ne estas mia gepatra lingvo. Do mi lernas du lingvojn samtempe... La sekva citaĵo resonanta kun mia mondvido klarigas kial mi sentas, ke Esperanto estas mia vera lingvo: | This year I started learning Esperanto. I started learning it out of curiosity and I really like learning it. I learn it in English which is not my native language. So I learn two language at the same time... The following quote resonating with my worldview explains why I feel that Esperanto is my true language:|
| "La interna ideo de #Esperanto estas: sur neŭtrala lingva fundamento forigi la murojn inter la gentoj kaj alkutimigadi la homojn, ke ĉiu el ili vidu en sia proksimulo nur homon kaj fraton." -- L.L.Zamenhof, 1912 (lernu.net) | "The internal idea of #Esperanto is: the foundation of a neutral language will help break down barriers between peoples and help people get used to the idea that each one of them should see their neighbors only as a human being and a brother." -- L.L.Zamenhof, 1912 (lernu.net)|
Kelkaj bonaj rimedoj por lerni:
- https://learn.esperanto.com
- https://lernu.net/
- https://www.apertium.org
- http://www.reta-vortaro.de/revo/

#learning #international #world #language #humans #people #quote
 
Bild/Foto

Helianthus - new project for education


русский текст ниже

Helianthus -- is the multimedia library for making learning of the C language fun and interesting.

The C language is as close as possible to the machine language of the processor. And programming in C allows you to better understand how the computer works. Having understood the C language, you can easily learn any other programming language.

However, starting to learn C using only standard functions is boring. All we can work with (besides computing) is a text console where we can read and display text.

Connecting third-party libraries and building a project with graphics and sound is often not an easy task for a beginner.

This library provides to you a set of simple functions for working with graphics, sound and physics. So at the very beginning of learning the C language you can create interesting and beautiful programs.

Library released as public domain, but using of dependencies GTK3, SDL2, Cairo and FreeType may set an additional restriction to produced binaries (Zlib, #GPL or other libre license).

Online documentation (yet russian only):


https://coolbug.org/users/bw/helianthus-doc-ru/index.html

Repo (alpha version - may contain bugs):


https://coolbug.org/earthworm/user/bw/repo/helianthus
https://repo.coolbug.org/bw/helianthus

Examples repo (one example yet):


https://coolbug.org/earthworm/user/bw/repo/helianthusexamples
https://repo.coolbug.org/bw/helianthusexamples
теперь по-русски

Helianthus -- мультимедиа библиотека для того чтобы изучать язык Си было весело и интересно.

Язык Си максимально приближен к машинному языку процессора, и, программирование на Си позволяет вам лучше понять как устроен компьютер. Поняв язык Си вы с лёгкостью освоите любой другой язык программирования.

Однако начинать изучать язык Си опираясь только на стандартные функции достаточно скучно. Всё с чем мы можем работать (помимо вычислений) — это текстовый терминал где мы можем читать и выводить текст.

Подключать же сторонние библиотеки и собирать из них полноценный проект с графикой и звуком зачастую не такая уж и простая задача для новичка.

Данная библиотека предоставляет вам набор простых функций для работы с графикой, звуком и физикой, для того чтобы вы в самом начале изучения языка Си могли создавать интересные и наглядные примеры.

Библиотека распространяется как общественное достояние, однако для сборки трубуются библиотеки GTK3, SDL2, Cairo и FreeType. По этому на окончательный исполняемый файл могут быть наложены ограничения #GPL, Zlib или другой свободной лицензии.

Online документация (пока только на русском):


https://coolbug.org/users/bw/helianthus-doc-ru/index.html

Репозиторий библиотеки:


https://coolbug.org/earthworm/user/bw/repo/helianthus
https://repo.coolbug.org/bw/helianthus

в коде могут содержаться ошибки, указание на них или даже исправление приветсвуется

Репозиторий с примерами (пока с одним примером):


https://coolbug.org/earthworm/user/bw/repo/helianthusexamples
https://repo.coolbug.org/bw/helianthusexamples

#c #programming #coding #learn #learning #education
#си #программирование #образование #обучение
#opensource #cc0 #publicdomain
 
Bild/Foto

Helianthus - new project for education


русский текст ниже

Helianthus -- is the multimedia library for making learning of the C language fun and interesting.

The C language is as close as possible to the machine language of the processor. And programming in C allows you to better understand how the computer works. Having understood the C language, you can easily learn any other programming language.

However, starting to learn C using only standard functions is boring. All we can work with (besides computing) is a text console where we can read and display text.

Connecting third-party libraries and building a project with graphics and sound is often not an easy task for a beginner.

This library provides to you a set of simple functions for working with graphics, sound and physics. So at the very beginning of learning the C language you can create interesting and beautiful programs.

Library released as public domain, but using of dependencies GTK3, SDL2, Cairo and FreeType may set an additional restriction to produced binaries (Zlib, #GPL or other libre license).

Online documentation (yet russian only):


https://coolbug.org/users/bw/helianthus-doc-ru/index.html

Repo (alpha version - may contain bugs):


https://coolbug.org/earthworm/user/bw/repo/helianthus
https://repo.coolbug.org/bw/helianthus

Examples repo (one example yet):


https://coolbug.org/earthworm/user/bw/repo/helianthusexamples
https://repo.coolbug.org/bw/helianthusexamples
теперь по-русски

Helianthus -- мультимедиа библиотека для того чтобы изучать язык Си было весело и интересно.

Язык Си максимально приближен к машинному языку процессора, и, программирование на Си позволяет вам лучше понять как устроен компьютер. Поняв язык Си вы с лёгкостью освоите любой другой язык программирования.

Однако начинать изучать язык Си опираясь только на стандартные функции достаточно скучно. Всё с чем мы можем работать (помимо вычислений) — это текстовый терминал где мы можем читать и выводить текст.

Подключать же сторонние библиотеки и собирать из них полноценный проект с графикой и звуком зачастую не такая уж и простая задача для новичка.

Данная библиотека предоставляет вам набор простых функций для работы с графикой, звуком и физикой, для того чтобы вы в самом начале изучения языка Си могли создавать интересные и наглядные примеры.

Библиотека распространяется как общественное достояние, однако для сборки трубуются библиотеки GTK3, SDL2, Cairo и FreeType. По этому на окончательный исполняемый файл могут быть наложены ограничения #GPL, Zlib или другой свободной лицензии.

Online документация (пока только на русском):


https://coolbug.org/users/bw/helianthus-doc-ru/index.html

Репозиторий библиотеки:


https://coolbug.org/earthworm/user/bw/repo/helianthus
https://repo.coolbug.org/bw/helianthus

в коде могут содержаться ошибки, указание на них или даже исправление приветсвуется

Репозиторий с примерами (пока с одним примером):


https://coolbug.org/earthworm/user/bw/repo/helianthusexamples
https://repo.coolbug.org/bw/helianthusexamples

#c #programming #coding #learn #learning #education
#си #программирование #образование #обучение
#opensource #cc0 #publicdomain
 

Over 1,000 online photography courses are now available for free | TechRadar

Following in the footsteps of Cambridge University and JSTOR, the Professional Photographers of America association has announced that its catalogue of over a thousand online photography courses will be accessible for free for the next two weeks.
#photography #education #learning #tutorial #SocialIsolation
 

Over 1,000 online photography courses are now available for free | TechRadar

Following in the footsteps of Cambridge University and JSTOR, the Professional Photographers of America association has announced that its catalogue of over a thousand online photography courses will be accessible for free for the next two weeks.
#photography #education #learning #tutorial #SocialIsolation
 
 

Welcome to CSS Diner


It's a fun #game to #learn and #practice CSS #selectors.

http://flukeout.github.io/

#CSS #webdesign #coding #learning #lernen #spielen #gaming
 
Hi,
I made this summary for my personal use, but maybe you also find it useful.

--> sorry large image size

#art #mastoart #tutorial #rendering #learning
Bild/Foto
 
Bild/Foto
Another amazing strip by #ZenPencils. These guys never fail to deliver amazing #comic #art. And, a little bit of #travel and #learning #inspiration never hurts!
 
Bild/Foto
#public #english #deutsch #ai #artificial #intelligence #ki
I developed my first ai for my own purpose.
The ai found an approximation curve of my body weight.
Blue is my measured body weight and orange is the approximated curve.
It took 1/4 hour to calculate it with #tensorflow for #deep #machine #learning with my #artificial #neural #net.
This is quite similar with the mathematics of function approximation or non linear regression.

Ich habe meine erste KI für meinen eigenen Zweck entwickelt.
Das KI fand eine Näherungskurve meines Körpergewichts.
Blau ist mein gemessenes Körpergewicht und Orange ist die angenäherte Kurve.
Es dauerte 1/4 Stunde, um es mit #tensorflow für #deep #machine #learning with my #artificial #neural #net zu berechnen.
Dies ist ähnlich mit der Mathematik der Funktionsannäherung oder der nichtlinearen Regression.

Source Code:
<br /># coding: utf-8 

# In[607]: 


from __future__ import absolute_import, division, print_function, unicode_literals 

import os 
import matplotlib.pyplot as plt 

import tensorflow as tf 
import numpy 
from sklearn.metrics import r2_score 
get_ipython().magic('matplotlib inline') 

tf.enable_eager_execution() 
&#35;tf.disable_eager_execution() 

print("TensorFlow version: {}".format(tf.__version__)) 
print("Eager execution: {}".format(tf.executing_eagerly())) 


# In[608]: 


&#35;train_dataset_url = "file:///home/alex/workspace-noneclipse/tensorflow/deep_learning_cookbook/data/gewicht2.csv" 
# 
&#35;train_dataset_fp = tf.keras.utils.get_file(fname=os.path.basename(train_dataset_url), 
#                                           origin=train_dataset_url) 
# 
&#35;print("Local copy of the dataset file: {}".format(train_dataset_fp)) 
# 


# In[609]: 


## column order in CSV file 
&#35;column_names = ['Zeitstempel','Gewicht'] 
# 
&#35;feature_names = column_names[0] 
&#35;label_name = column_names[1] 
# 
&#35;print("Features: {}".format(feature_names)) 
&#35;print("Label: {}".format(label_name)) 
&#35;print(str(train_dataset_fp)) 
# 


# In[610]: 


&#35;TrainingSet = numpy.genfromtxt("/home/alex/workspace-noneclipse/tensorflow/alx/data/weath1weight2.csv", delimiter=";", skip_header=True) 
TrainingSet = numpy.genfromtxt("/home/alex/workspace-noneclipse/tensorflow/alx/data/gewicht2.csv", delimiter=",", skip_header=True) 
&#35;ValidationSet = numpy.genfromtxt("/home/alex/workspace-noneclipse/tensorflow/alx/data/validation.csv", delimiter=",", skip_header=True) 


# In[611]: 


X1 = TrainingSet[:,0:1] 
Y1 = TrainingSet[:,1] 
# 
&#35;X2 = ValidationSet[:,0:1] 
&#35;Y2 = ValidationSet[:,1] 
X1 


# In[612]: 


# 
&#35;xa = X1[0][0] 
&#35;xb = X1[0][1] 
&#35;X3 = [] 
&#35;X3a = [] 
&#35;X3b = [9] 
&#35;for x in X1: 
#    X3.append([x[0]-xa]+[x[1]-xb]) 
&#35;X1 = X3 
# 
Y1 


# In[613]: 


&#35;ya = min(Y1) 
&#35;y3 =[] 
&#35;for y in Y1: 
#    y3.append(y-ya) 
&#35;Y1 = y3 
&#35;len(Y1) 


# In[614]: 


&#35;batch_size = 40 
# 
&#35;train_dataset = tf.contrib.data.make_csv_dataset( 
#    train_dataset_fp, 
#    batch_size, 
#    column_names=column_names, 
#    label_name=label_name, 
#    num_epochs=1) 
# 


# In[615]: 


&#35;features, labels = next(iter(train_dataset)) 
# 
&#35;features 


# In[616]: 


&#35;labels 


# In[617]: 


&#35;def pack_features_vector(features, labels): 
#  """Pack the features into a single array.""" 
#  features = tf.stack(list(features.values()), axis=1) 
#  return features, labels 
# 
&#35;train_dataset = train_dataset.map(pack_features_vector) 
# 
&#35;features, labels = next(iter(train_dataset)) 
&#35;print(features[:5]) 
# 
# 


# In[618]: 


&#35;print(labels[:5]) 


# In[619]: 


&#35;model = tf.keras.Sequential([ 
#  tf.keras.layers.Dense(40, activation=tf.nn.relu, input_shape=(1,1)),  # input shape required 
#  tf.keras.layers.Dense(40, activation=tf.nn.sigmoid), 
#  &#35;tf.keras.layers.Dense(20, activation=tf.nn.softmax), 
#  tf.keras.layers.Dense(1, activation=tf.keras.activations.linear) 
&#35;]) 
&#35;model = tf.keras.Sequential([ 
#  tf.keras.layers.Dense(40, activation=tf.nn.relu, input_shape=(1,1)),  # input shape required 
#  tf.keras.layers.Dense(40, activation=tf.nn.sigmoid), 
#  tf.keras.layers.Dense(40, activation=tf.nn.sigmoid), 
#  tf.keras.layers.Dense(40, activation=tf.nn.sigmoid), 
#  tf.keras.layers.Dense(40, activation=tf.nn.sigmoid), 
#  &#35;tf.keras.layers.Dense(20, activation=tf.nn.softmax), 
#  tf.keras.layers.Dense(1, activation=tf.keras.activations.linear) 
&#35;]) 
model = tf.keras.Sequential([ 
  tf.keras.layers.Dense(len(X1[0]), input_shape=(len(X1[0]),), kernel_initializer="uniform"), 
  tf.keras.layers.BatchNormalization(axis=-1, trainable=True), 
  tf.keras.layers.Dense(10000, activation=tf.nn.tanh, kernel_initializer="uniform"), 
  tf.keras.layers.Dense(10000, activation=tf.nn.tanh, kernel_initializer="uniform"), 
  &#35;tf.keras.layers.Dense(20, activation=tf.nn.softmax), 
  tf.keras.layers.Dense(1, activation=tf.keras.activations.linear, kernel_initializer="uniform")  
]) 

# 
# 
&#35;predictions = model(features) 
&#35;predictions 


# In[ ]: 





# In[620]: 


&#35;tf.nn.softmax(predictions[:5]) 
&#35;features 


# In[621]: 


&#35;print("Prediction: {}".format(tf.argmax(predictions, axis=1))) 
&#35;print("    Labels: {}".format(labels)) 


# In[622]: 



# Compile model 
model.compile(loss='mse', optimizer='adam', metrics=['accuracy']) 

# Fit the model 
model.fit(X1, Y1, epochs=30, batch_size=5,  verbose=2) 


# In[623]: 


&#35;def loss(model, x, y): 
#  y_ = model(x) 
#  &#35;print(str(x)) 
#  &#35;print(str(y)) 
#  &#35;print(str(y_)) 
#  return tf.reduce_mean(tf.squared_difference(y,y_[0])) 
#  &#35;return tf.reduce_sum(tf.square(y,name='error'),y_[0]) 
# 
#&#35;sparse_softmax_cross_entropy(labels=y, logits=y_) 
# 
# 
&#35;l = loss(model, features, labels) 
&#35;print("Loss test: {}".format(l)) 
# 


# In[624]: 


&#35;def grad(model, inputs, targets): 
#  with tf.GradientTape() as tape: 
#    loss_value = loss(model, inputs, targets) 
#  return loss_value, tape.gradient(loss_value, model.trainable_variables) 
# 


# In[625]: 


&#35;optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.005) 
# 
&#35;global_step = tf.Variable(0) 
# 


# In[626]: 




&#35;loss_value, grads = grad(model, features, labels) 

&#35;print("Step: {}, Initial Loss: {}".format(global_step.numpy(), 
#                                          loss_value.numpy())) 

&#35;optimizer.apply_gradients(zip(grads, model.trainable_variables), global_step) 

&#35;print("Step: {},         Loss: {}".format(global_step.numpy(), 
 #                                         loss(model, features, labels).numpy())) 


# In[627]: 


### Note: Rerunning this cell uses the same model variables 
# 
&#35;from tensorflow import contrib 
&#35;tfe = contrib.eager 
# 
## keep results for plotting 
&#35;train_loss_results = [] 
&#35;train_accuracy_results = [] 
# 
&#35;num_epochs = 5 
&#35;for epoch in range(num_epochs): 
#  epoch_loss_avg = tfe.metrics.Mean() 
#  epoch_accuracy = tfe.metrics.Accuracy() 
# 
#  # Training loop - using batches of 32 
#  for x, y in train_dataset: 
#    # Optimize the model 
#    loss_value, grads = grad(model, x, y) 
#    optimizer.apply_gradients(zip(grads, model.trainable_variables), 
#                              global_step) 
# 
#    # Track progress 
#    epoch_loss_avg(loss_value)  # add current batch loss 
#    # compare predicted label to actual label 
#    epoch_accuracy(model(x)[0], y[0]) 
#  # end epoch 
#  train_loss_results.append(epoch_loss_avg.result()) 
#  train_accuracy_results.append(epoch_accuracy.result()) 
# 
#  if epoch % 1 == 0: 
#    print("Epoch {:03d}: Loss: {:.3f}, Accuracy: {:.3%}".format(epoch, 
#                                                                epoch_loss_avg.result(), 
#                                                                epoch_accuracy.result())) 
#    &#35;print("Epoch {:03d}: Loss: {:.8f}".format(epoch,epoch_loss_avg.result())) 
# 


# In[628]: 


#&#35;import numpy as np 
#&#35;features=np.matrix([[1553774948, 1350809400, 1397637660, 1556192428, 1446639060,1556440621, 1328555460, 1477636620]]) 
#&#35;1574981355 
#&#35;features = tf.Variable([[1572981355]], dtype=tf.float32) 
&#35;features2 = tf.Variable([[1561810342]], dtype=tf.float32) 
&#35;predictions = model(features2) 
&#35;predictions = predictions.numpy().tolist() 
&#35;predictions# 


# In[ ]: 





# In[629]: 



# Calculate predictions 
PredTestSet = model.predict(X1) 
PredValSet = model.predict(X1) 


# In[630]: 



# Save predictions 
numpy.savetxt("/home/alex/workspace-noneclipse/tensorflow/alx/data/trainresults.csv", PredTestSet, delimiter=",") 
numpy.savetxt("/home/alex/workspace-noneclipse/tensorflow/alx/data/valresults.csv", PredValSet, delimiter=",") 


# In[631]: 


&#35;features.numpy().tolist() 


# In[632]: 



&#35;Plot actual vs predition for training set 
TestResults = numpy.genfromtxt("/home/alex/workspace-noneclipse/tensorflow/alx/data/trainresults.csv", delimiter=",") 
plt.plot(Y1,TestResults,'ro') 
plt.title('Training Set') 
plt.xlabel('Actual') 
plt.ylabel('Predicted') 

&#35;Compute R-Square value for training set 
TestR2Value = r2_score(Y1,TestResults) 
print("Training Set R-Square=", TestR2Value) 


# In[633]: 


# 
#&#35;Plot actual vs predition for validation set 
&#35;ValResults = numpy.genfromtxt("/home/alex/workspace-noneclipse/tensorflow/deep_learning_cookbook/data/valresults.csv", delimiter=",") 
&#35;plt.plot(Y2,ValResults,'ro') 
&#35;plt.title('Validation Set') 
&#35;plt.xlabel('Actual') 
&#35;plt.ylabel('Predicted') 
# 
#&#35;Compute R-Square value for validation set 
&#35;ValR2Value = r2_score(Y2,ValResults) 
&#35;print("Validation Set R-Square=",ValR2Value) 


# In[634]: 


# 
&#35;import matplotlib.pyplot as plt 
&#35;import datetime 
&#35;import numpy as np 
# 
## features['Zeitstempel'], labels.numpy() 
#&#35;gruene = labels.numpy()&#35;np.array([5.3, 8.0, 7.9, 9.5, 12.1, 7.7, 11.7, 24.2, 30.3]) 
&#35;origKg = labels.numpy()&#35;np.array([53.4, 51.9, 49.0, 39.6, 41.3, 44.8, 44.2, 39.0, 27.0]) 
# 
&#35;ts2=[] 
&#35;ts3=[] 
&#35;for ts in features.numpy().tolist(): 
#    ts2.append(datetime.datetime.fromtimestamp(int(ts[0]))) 
#    ts3.append(int(ts[0])) 
# 
&#35;predictions=[] 
&#35;for ts in ts3: 
#    features2 = tf.Variable([[ts]], dtype=tf.float32) 
#    prediction = model(features2)[0][0] 
#    predictions.append(prediction) 
#     
#&#35;predictions2 = [] 
#&#35;for prediction in predictions: 
##    predictions2.append(datetime.datetime.fromtimestamp(float(prediction))) 
# 
# 
# 
&#35;fig, ax = plt.subplots() 
#&#35;xlabels = features.numpy().tolist() 
# 
#&#35;plt.title("Regional Elections Baden-Wuerttemberg 1980-2016", size="x-large") 
#&#35;plt.ylabel("Votes in %", size="x-large") 
#&#35;plt.xlabel("Year", size="x-large") 
# 
## plot the data 
#&#35;print(str(features.numpy().tolist())) 
# 
#&#35;print(str(ts2)) 
#&#35;dates = matplotlib.dates.date2num(ts2) 
#&#35;print(str(predictions2)) 
&#35;plt.plot_date(ts2, origKg) 
&#35;plt.plot_date(ts2, predictions) 
#&#35;plt.plot(cdu, "r\*-", markersize=6, linewidth=1, color='black', label="CDU") 
#&#35;plt.plot(gruene, "r\*-", markersize=6, linewidth=1, color='g', label="Gruene") 
# 
## add legend 
#&#35;plt.legend(loc=(0.1, 0.3)) 
# 
## add x-labels 
#&#35;ax.set_xticks(range(len(xlabels))) 
#&#35;ax.set_xticklabels(xlabels, rotation='vertical') 
# 
&#35;plt.show() 


# In[635]: 


PredTestSet 


# In[636]: 



import matplotlib.pyplot as plt 
import datetime 
import numpy as np 

## features['Zeitstempel'], labels.numpy() 
#&#35;gruene = labels.numpy()&#35;np.array([5.3, 8.0, 7.9, 9.5, 12.1, 7.7, 11.7, 24.2, 30.3]) 
&#35;origKg = Y1 &#35;np.array([53.4, 51.9, 49.0, 39.6, 41.3, 44.8, 44.2, 39.0, 27.0]) 


ts2=[] 
ts3=[] 
predictions=[] 
origKg=[] 

for ts,y,pred in zip(X1,Y1,PredTestSet): 
    ts2.append(datetime.datetime.fromtimestamp(int(ts[0]))) 
    ts3.append(int(ts[0])) 
    predictions.append(pred[0]) 
    origKg.append(y) 

&#35;predictions=[] 
&#35;for ts in ts3: 
#    &#35;features2 = tf.Variable([[ts]], dtype=tf.float32) 
#    prediction = model(features2)[0][0] 
#    predictions.append(prediction) 

&#35;predictions2 = [] 
&#35;for prediction in predictions: 
#    predictions2.append(datetime.datetime.fromtimestamp(float(prediction))) 



fig, ax = plt.subplots() 
&#35;xlabels = features.numpy().tolist() 

&#35;plt.title("Regional Elections Baden-Wuerttemberg 1980-2016", size="x-large") 
&#35;plt.ylabel("Votes in %", size="x-large") 
&#35;plt.xlabel("Year", size="x-large") 

# plot the data 
&#35;print(str(features.numpy().tolist())) 

&#35;print(str(ts2)) 
&#35;dates = matplotlib.dates.date2num(ts2) 
&#35;print(str(predictions2)) 
plt.plot_date(ts2, origKg) 
plt.plot_date(ts2, predictions) 
&#35;plt.plot(cdu, "r\*-", markersize=6, linewidth=1, color='black', label="CDU") 
&#35;plt.plot(gruene, "r\*-", markersize=6, linewidth=1, color='g', label="Gruene") 

# add legend 
&#35;plt.legend(loc=(0.1, 0.3)) 

# add x-labels 
&#35;ax.set_xticks(range(len(xlabels))) 
&#35;ax.set_xticklabels(xlabels, rotation='vertical') 

plt.show()
 
That´s a good trick, turning Spam an commercial Shit into usable value.
#Learning a #language with no costs.

https://www.thelocal.fr/20190510/how-i-used-cold-callers-and-lovelorn-french-farmers-to-learn-the-language
  • Don’t hang up on the cold callers
This sounds really perverted but bear with. These people speak super fast much like they would in the UK because they don’t want you to figure out, quickly, that it’s a cold call trying to sell you something. So I keep these bad boys on the phone.

I get them to slow down, repeat what they’re saying and ask questions. They are naturally obliging because they want the sale. They have no idea they are in some perverse French lesson and then at the end you can just say non merci. And when they ask why, just say I don’t speak very good French. They will then hang up on you. A free five-minute French lesson.
Not hard to imagine that you could also call free hotlines yourself. But wouldn't that be fraud? :D
 

Jeder will etwas sein, aber niemand will etwas werden

Reminds me of a saying: everybody wants to be something, but nobody wants to become something.

Time to learn some new skills!

 
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