Sunday, April 18, 2021

Utopia Probably Does Not Exist Anywhere

Utopia: A Brief Critique

By Bogumil Jarmulak

Posted by Theopolis, March 5, 2015

Utopia is often accompanied by such adjectives as impossible, unrealistic, and impractical. In spite of that, it is not so hard to find some renowned people who actually maintain that is a very helpful and practical concept. Zygmunt Bauman (Socialism: The Active Utopia) argues that “utopian thinking belongs in the same category as invention” and invention is what a society needs in time of crises when all traditional solutions fail tragically. It also needs hope to cope with the crises and utopia provides just that. But even in normal times utopia is very useful since it “is an integral element of the critical attitude,” something that the conservative mind lacks dramatically. Last but not least, utopias “exert enormous influence on the actual course of historical events,” as they not only motivate human action but also guide it.

Also Ruth Levitas (Being in Utopia) speaks favorably of utopia. According to her, “imagining ourselves otherwise is a central element of the utopian project.” And the utopian project serves to find “a better way of being and a better way of living.” So for her utopia is not so much a goal of human action but rather a method of reconstitution of society. “Hope and imagination are central to this.” Hope energizes the action and imagination “allows people to understand their own potential to change.” In this context, Levitas speaks about “prophetic identity,” which is “self-understanding in terms of who we might become.” This prophetic identity should be the goal of education.

But not only people whom we associate with the left political spectrum advocate utopian thinking. A libertarian philosopher, Robert Nozick (Anarchy, State, and Utopia), argues for meta-utopia, i.e. a minimal state and a society based on free market and voluntary associations. That’s the only possible utopia since utopia is “the best world imaginable, for each of us.”

It should not surprise that the vast majority of those who have a rather critical attitude towards utopia comes from the conservative corner. Thomas Molnar (Myth and Utopia) traces the roots of utopian thinking to ancient perception of reality. Ancient societies believed that they needed to re-immerse themselves in “the original outpouring of being” whenever they felt that they were not longer able to function. This re-immersion took form of rituals and festivals based of myths dealing with “the original outpouring of being.” Molnar notices that modern society has little room for myths, which however does not mean that they vanish. Instead, the myth “undergoes a succession of metamorphoses until the ‘true story’ turns into a ‘false story,’” where “false story” is a “second reality,” the only true reality. This “false story” is a pseudo-myth, a shadow of myth, also known as utopia.

According to Molnar this change from myth to utopia is connected with the rise of Christianity and its linear conception of history, which makes it impossible to return to “the original outpouring of being.” Nevertheless, modern societies are still caught by nostalgia. “The utopian designer . . . holds dear the images of Paradise, but only as fragments of a coarse myth: freedom from work, nudity, sexual gratification, the destruction of time and of rationality.” The linear perception of history makes him believe that utopia lies in an undefined future, which means that “the most important element of the myth, experience, is absent from utopia; in fact, its building blocks are the negation of the structure of the real.”

This last observation leads us to Karl Popper (The Open Society and Its Enemies) who, trying to explain utopia, also goes back to the ancients but this time to Plato and finds common elements in the Platonic approach and in utopian engineering. Both can be described as total approaches since both believe that for any action to be rational, one has to choose the end first. In this case the end is a blueprint for the Ideal State. And this is exactly the problem that Popper points at. “Social life is so complicated that few men, or none at all, could judge a blueprint for social engineering on the grand scale.” Another problem is that “the Utopian attempt to realize an ideal state, using a blueprint of society as a whole, is one which demands a strong centralized rule of a few, and which therefore is likely to lead to a dictatorship.” Moreover, utopian social engineers must face the question of succession: how to ensure that the next generation of leaders will be willing to pursue the same project as they do?

According to Popper these problems can be solved only if the “Platonic belief in one absolute and unchanging ideal” is true and “that there are rational methods to determine once and for all what this ideal is.” However, even Plato himself would admit that this is not possible. The chase after the impossible doesn’t pay off, because it stands in the way of what Popper calls “piecemeal engineering,” which adopts “the method of searching for, and fighting against, the greatest and most urgent evils of society, rather than searching for, and fighting for, its greatest ultimate good.”

Popper believes that the ultimate source of all utopian thinking can be found in aesthetic perception of reality. “Plato was an artist; and like many of the best artists, he tried to visualize a model, the ‘divine original’ of his work, and to ‘copy’ it faithfully.” This is why any “artist-politicians” who follow Plato has to start with “canvas-cleaning.” “He must eradicate the existing institutions and traditions. He must purify, purge, expel, banish, and kill.” But when doing this, the “artist-politician” has to get rid of himself, too, since he is a part of the canvas which has to be cleaned. To construct a fully other social order, one has to transcend it, but this is something that no man can actually do. “The political artist clamors, like Archimedes, for a place outside the social world on which he can take his stand, in order to lever it off its hinges. But such a place does not exist.”

Aurel Kolnai’s (The Utopian Mind) argument against utopia is similar to Popper’s. “Utopias are in fact intrinsically at war with the basic structure of reality.” First of all, utopian thinking ascribes to men divine like abilities, what should not surprise us, since any utopian project aims at constructing a new world: not so much with the materials acquired from the old one, but ex nihilo. This however contradicts our experience. “Real [human] power is always somehow divided, finite, and hampered. . . . It remains subject to mundane barriers and limitations. . . . It is part of history and depends on contingency.”

Another headache with utopia is its perfectionism which leads to what Kolnai calls “the utopian contradiction.” Utopia is a “monistic conception,” and as such it is not only all embracing but also shielded from any external evaluation and reality. It is also not aimed at “getting rid of a definite evil” or “realizing a definite good” but instead “it hankers after deductive and necessary truths in preference to contingent facts and empirical discoveries.” The end result is that utopia disdains what people actually want and need.

Roger Scruton agrees with Kolnai that utopian thinking is stained with contradiction but, like Popper, adds that the most important criticism of utopia is that “it destroys the institutions that enable us to solve our conflicts one by one” (The Uses of Pessimism). The solutions to our problems do not exist as any blueprint but are discovered one by one, over generations, and are preserved in customs and traditions. But these are exactly what utopian mind abhors. It does so because it is intoxicated by “a prelapsarian bliss,” which “urges people to destroy the structures that stand in the way of a recovered innocence.”

All the above criticism of utopian thinking may be called commonsensical. But there is also a distinctively Christian criticism of utopia. As Lyman Tower Sargent (Utopianism: A Very Short Introduction) observes, the most common Christian objection against utopia is the Fall of Adam. “The theological argument against utopianism is much simpler than the one in favor of it because it is based on the common assumption that utopianism is rooted in the denial of original sin”. Since men are fallen, it should not be expected that they can build a utopia or be able to live in it.

But there are more arguments than that. It is true that there is what could be called a utopian element in Christian faith, namely the Kingdom of God. However, as Wolfhart Pannenberg (Basic Questions in Theology, Vol. 2) observed, the Kingdom of God is bestowed on us by God and is not something we can build on our own. This means that the Kingdom itself is not our ultimate hope, but God is, or rather our communion with Him. This also means that as Christian we are called not so much to build the Kingdom, as if we had some blueprint for it and all the needed resources, but to receive it with gratitude and obedience.

Hans Urs von Balthasar (Christianity as Utopia) adds that the Christian hope, as much as it is dissatisfied with the present earthly conditions, does not despise the world we live in, so the hope is not otherworldly. It is rather a hope for the redemption of the fallen world. Even if we are called to give up this life and this world, we do it so that we can regain it or, to be more precise, receive it anew. This way the Christian hope is based not so much on any longing for an entirely different reality, but rather on the promise to redeem the present reality and this promise is confirmed by the Cross and the Resurrection of Christ.

Jürgen Moltmann (Theology of Hope) speaks in the same manner. He admits that hope is a crucial element of the Christian faith and it has critical effects upon history, even to the point of shaping it. He also concurs that there are similarities between utopia and the Christian hope. Both share a deep dissatisfaction with the present reality and a deep longing for a better future. But what distinguishes the Christian hope from any utopian longing is the person and history of Jesus Christ. “Without faith’s knowledge of Christ, hope becomes a utopia and remains hanging in the air.”

Bogumil Jarmulak is pastor of Evangelical Reformed Church (CREC) in Poznan, Poland. His PhD is from Christian Theological Academy in Warsaw, Poland.

https://theopolisinstitute.com/utopia-a-brief-critique/#:~:text=The%20end%20result%20is%20that%20utopia%20disdains%20what,by%20one%E2%80%9D%20%28%20The%20Uses%20of%20Pessimism%20%29.

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Original Sin Lands on Humans When They Are Babies

Babies Prefer Individuals Who Harm
Those That Aren’t Like Them

Infants as young as nine months old prefer individuals who are nice to people like them and mean to people who aren’t like them, according to a new study published in Psychological Science, a journal of the Association for Psychological Science.

In our social lives, we tend to gravitate toward people who have things in common with us, whether it’s growing up in the same town, disliking the same foods, or even sharing the same birthday. And research suggests that babies evaluate people in much the same way, preferring people who like the same foods, clothes, and toys that they like.

This preference helps us to form social bonds, but it can also have a dark side. Disliking people who are different than us may lead us to mistreat them, and excuse — or even applaud — cases in which others mistreat people who are different than us.


Are the roots of such tendencies present in infancy?

To find out, psychological scientist Kiley Hamlin, now a professor at the University of British Columbia, conducted two studies as a graduate student at Yale University with her advisor Karen Wynn and colleagues.

The researchers had 9- and 14-month-old infants choose which food they preferred: graham crackers or green beans. The infants then watched a puppet show in which one puppet preferred graham crackers, while another preferred green beans. That is, one puppet demonstrated that its food preference was the same as the infant’s, while the other demonstrated that its food preference was different from the infant’s.

After the puppets chose their foods, infants then watched another puppet show, in which either the similar puppet or the dissimilar puppet dropped its ball and wanted it back. On alternating events, infants saw that one character always helped the ball-less puppet by returning the ball to him, while another character always harmed the ball-less puppet by stealing the ball away.

Finally, infants were given the chance to choose between the helper (giving) and harmer (stealing) puppets.

Unsurprisingly, infants’ choices revealed that almost all the infants in both the 9- and 14-month-old groups preferred the character who helped the similar puppet over the character who harmed the similar puppet. Previous research has shown that infants like people who are nice to totally unknown individuals, so it makes sense that they would also like people who are nice to individuals who are similar to them.

Far more surprising was that almost all the infants at both ages preferred the character who harmed the dissimilar puppet over the character who helped him. Infants’ preference for those who harmed dissimilar others was just as strong as their preference for those who helped similar ones.

According to Hamlin, these findings suggest that "like adults, infants incorporate information about not only what people do (e.g., acting nicely or meanly) but also whom they do it to (e.g., a person who is liked or disliked) when they make social evaluations."

The researchers confirmed these results in a second experiment, which included a neutral puppet that had demonstrated no food preference and no helpful or harmful behaviors.

This time, the 14-month-olds — but not the 9-month-olds — preferred the character that harmed the dissimilar puppet over the neutral puppet, and the neutral puppet over the helper of the dissimilar puppet. These results suggest that when a dissimilar individual is in need, 14-month-olds generate both positive feelings toward those who harm that individual and negative feelings toward those who help him. The researchers suggest that between 9 and 14 months, infants develop reasoning abilities that lead to these more nuanced social evaluations.

These results highlight the fundamental mechanisms that underlie our interactions with similar and dissimilar people.

"The fact that infants show these social biases before they can even speak suggests that the biases aren’t solely the result of experiencing a divided social world, but are based in part on basic aspects of human social evaluation," says Hamlin.

But the exact reasons for infants’ biased evaluations are still unknown.

"Infants might experience something like schadenfreude [literally joy at the suffering of others, the German word for "resentment"] at the suffering of an individual they dislike," Hamlin notes. "Or perhaps they recognize the alliances that are implied by social interactions, identifying an ‘enemy of their enemy’ (i.e., the harmer of a dissimilar puppet) as their friend."

Hamlin emphasizes that even if these kinds of social biases are "basic," it doesn’t mean that more extreme outcomes, like xenophobia and intergroup conflict, are inevitable.

"Rather, this research points to the importance of socialization practices that recognize just how basic these social biases might be and confront them head-on," she concludes.

Co-authors on this research include Neha Mahajan of Temple University, Zoe Liberman of the University of Chicago, and Karen Wynn of Yale University.

This research was supported by National Science Foundation Grant BCS-0921515 and National Institutes of Health Grant R01-MH-081877 to Karen Wynn.

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Videos of the experimental procedure are available at: http://cic.psych.ubc.ca/Media_Videos.htmlFor more information about this study, please contact: Kiley Hamlin at kiley.hamlin@psych.ubc.ca or Karen Wynn at karen.wynn@yale.edu.

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Link for the entire above article: http://www.psychologicalscience.org/index.php/news/releases/babies-prefer-individuals-who-harm-those-that-arent-like-them.html


Saturday, April 17, 2021

Suicides at the Golden Gate Bridge

Between 1937 and 2012, an estimated 1,400 bodies were recovered of people who had jumped from the Golden Gate Bridge, located in the San Francisco Bay Area in the United States.

In 2013, 118 potential jumpers were talked down from their attempt and did not jump.  As of 2013, it is estimated that 34 people have survived after jumping.  Some die instantly from internal injuries, while others drown or die of hypothermia.

A number of measures are in place to discourage people from jumping, including telephone hotlines and patrols by emergency personnel and bridge workers. Although it had previously been considered impractical to build a suicide barrier, in 2014, the bridge's directors approved a proposal for a net below the bridge's deck, extending out either side, rather than side barriers at the railings as had long been proposed.

Background

The Golden Gate Bridge is referred to by Krista Tippett as a "suicide magnet."  The deck is about 245 feet (75 m) above the water.  After a fall of four seconds, jumpers hit the water at around 75 mph (120 km/h). Most of the jumpers die due to impact trauma.  About 5% of the jumpers survive the initial impact but generally drown or die of hypothermia in the cold water.

Most suicidal jumps from the bridge have occurred on the side facing the bay. The side facing the Pacific is closed to pedestrians.

An official suicide count was kept until the year 1995, sorted according to which of the bridge's 128 lamp posts the jumper was nearest when he or she jumped.  The official count ended on June 5, 1995 on the 997th jump; jumper No. 1000, Eric Atkinson (25), jumped on July 3, 1995.  Earlier in 1995, a local shock jock had offered a case of Snapple to the family of the 1000th suicide victim.  Consequently, Marin County coroner Ken Holmes asked local media to stop reporting the total number of jumpers.  By 2012 the unofficial count exceeded 1,600 (in which the body was recovered or someone saw the jump) and new suicides were occurring about once every two weeks, according to a San Francisco Chronicle analysis.  The most suicides in one month were in August 2013, when 10 jumped. The total count for the year 2013 was 46, with an additional 118 attempts prevented, making it the year with the highest tally so far.  The rate of incidence of attempts has risen to nearly one every other day.  The youngest known jumper is five-year-old Marilyn DeMont; in 1945, she was told to jump by her father who followed her.

For comparison, the Aokigahara Forest in Japan, has a record of 108 bodies, found within the forest in 2004, with an average of 30 a year.  There were 34 bridge-jump suicides in 2006 whose bodies were recovered, in addition to four jumps that were witnessed but whose bodies were never recovered, and several bodies recovered suspected to be from bridge jumps. The California Highway Patrol removed 70 apparently suicidal people from the bridge that year.

There is no accurate figure on the number of suicides or completed jumps since 1937, because many were not witnessed. People have been known to travel to San Francisco specifically to jump off the bridge, and may take a bus or cab to the site; police sometimes find abandoned rental cars in the parking lot. Currents beneath the bridge are strong and some jumpers have more than likely been washed out to sea without being seen.

The fatality rate of jumping is roughly 98%. As of July 2013, only 34 people are known to have survived the jump.  Those who do survive strike the water feet-first and at a slight angle, although individuals may still sustain broken bones or internal injuries. One young woman, Sarah Rutledge Birnbaum, survived, but returned to jump again and died the second time.  One young man survived a jump in 1979, swam to shore, and drove himself to a hospital. The impact cracked several of his vertebrae.

Engineering professor Natalie Jeremijenko, as part of her "Bureau of Inverse Technology" art collective, created a "Despondency Index" by correlating the Dow Jones Industrial Average with the number of jumpers detected by "Suicide Boxes" containing motion-detecting cameras, which she claimed to have set up under the bridge.  The boxes purportedly recorded 17 jumps in three months, far greater than the official count. The Whitney Museum, although questioning whether Jeremijenko's suicide-detection technology actually existed, nevertheless included her project in its prestigious Whitney Biennial.

False Suicides and Survivors

Along with confirmed suicide deaths and suicide attempts at the bridge, there have been false suicides as well. The first documented case of "pseudocide" at the Golden Gate Bridge was in 1948. 47-year-old Chris J. Christensen was a well-known local jeweler who had been recently elected to the San Francisco Board of Supervisors.  Christensen's coat was found attached to a work box at the center span of the bridge with a note in the coat that read, "Loved Ones: My nerves are shot. Please forgive me. Chris." Believed to have jumped from the bridge, Christensen was declared dead and rumors abounded. Investigators concluded he was unable to cope with the pressures of being in public office. There were reports of Christensen having become friendly with a man described as "willowy ... almost too good-looking to be considered handsome." While Christensen introduced the man to friends and colleagues as his nephew, it was learned the man was not a relative; rather, he was a Navy sailor whom Christensen met in a Los Angeles bar. Over a year had passed when it was discovered Christensen was actually alive and selling Bibles in Houston, Texas.  Found living in a low-rent rooming house and having lost 40 pounds, Christensen explained that campaign contributors who supported his election had asked him to "do things he couldn't do". Christensen saw himself as a failure and never returned to San Francisco.

In 1985, 28-year-old Kenneth Baldwin jumped over and survived. Rescued by the US Coast Guard, he suffered a few broken ribs and a bruised lung.

On September 24, 2000, Kevin Hines was 19 years old, paranoid, and hallucinating when he jumped off the Golden Gate Bridge. Throwing himself headfirst over the bridge railing, he fell 220 feet into San Francisco Bay. During the fall, his body rotated so that when Hines hit the water he landed in a sitting position, taking the impact in his legs and up through his back. Three of his vertebrae were shattered, lacerating his lower internal organs. A United States Coast Guard vessel rescued him, and he was transported to a hospital in San Francisco where he received emergency surgery. Following further, experimental surgery, any physical evidence of his experience is almost non-existent, and Hines has full mobility. Regarding his thoughts after the jump, Hines stated, "There was a millisecond of free fall. In that instant, I thought, what have I just done? I don't want to die. God, please save me." Following his suicide attempt, Hines received some notoriety as a survivor, appearing in a documentary film, The Bridge (2006) and being interviewed on CNN by Larry King.  Additionally, he wrote a book about his experience before and after his suicide attempt, Cracked, Not Broken, and became a mental health advocate as well as a proponent for a bridge suicide barrier or net to prevent such incidents.

On March 10, 2011, 17-year-old Luhe "Otter" Vilagomez from Windsor High School in Windsor, California, survived a jump from the bridge, breaking his coccyx and puncturing one lung, though he said his attempt was for "fun" and not suicide. The teen was helped to shore by Frederic Lecouturier, 55, who was surfing under the bridge when he saw Vilagomez jump.  The California Highway Patrol recommended the San Francisco District Attorney's Office charge the student with misdemeanor trespassing (a charge that entails climbing any rail, cable, suspender rope, tower or superstructure not intended for public use), punishable by up to a year in the county jail and/or a fine up to $10,000, and that the teenager undergo a medical/psychiatric evaluation by medical professionals.

Prevention and Intervention

Various methods have been tried to reduce the number of suicides. The bridge is fitted with suicide-hotline telephones and staff patrol the bridge in carts, looking for people who appear to be planning to jump. The bridge is now closed to pedestrians at night. Cyclists are still permitted across at night, but can buzz themselves in and out through the remotely controlled security gates.  Attempts to introduce a suicide barrier have been thwarted by engineering difficulties, high costs, and public opposition.  One recurring proposal had been to build a barrier to replace or augment the low railing, a component of the bridge's original architectural design, as amended by the second designer in the final blueprint.  New barriers have eliminated suicides at other landmarks around the world, but were opposed for the Golden Gate Bridge for reasons of cost, aesthetics, and safety, as the load from a poorly designed barrier could significantly affect the bridge's structural integrity during a strong windstorm. Despite these concerns, on June 27, 2014, California approved a funding plan to install a suicide barrier.

A volunteer group called the Bridgewatch Angels was founded by Pleasanton Police Lieutenant, Mia Munayer, in 2011. During every major holiday and while off-duty, Munayer mobilizes hundreds of volunteers to patrol the bridge looking for anyone who may be contemplating suicide. Before embarking on their morning or afternoon shifts, Bridgewatch Angels volunteers receive training on the warning signs of someone in crisis, indirect and direct ways to engage with people walking alone on the bridge, and safety protocol when interacting with a suicidal person requiring police intervention. Each Bridgewatch event is dedicated to the memory of a person who jumped from the Golden Gate Bridge and their family joins the Bridgewatch Angels as they walk together to honor the memory of their loved one. The Bridgewatch Angels are credited with making dozens of interventions each year.

           https://en.wikipedia.org/wiki/Suicides_at_the_Golden_Gate_Bridge

Friday, April 16, 2021

The Whitest and Coolest Paint Ever

New paint formulation could help buildings rely less on air conditioning

From Perdue News Services

April 15, 2021 -- WEST LAFAYETTE, Ind. — In an effort to curb global warming, Purdue University engineers have created the whitest paint yet. Coating buildings with this paint may one day cool them off enough to reduce the need for air conditioning, the researchers say.

In October, the team created an ultra-white paint that pushed limits on how white paint can be. Now they’ve outdone that. The newer paint not only is whiter but also can keep surfaces cooler than the formulation that the researchers had previously demonstrated.

“If you were to use this paint to cover a roof area of about 1,000 square feet, we estimate that you could get a cooling power of 10 kilowatts. That’s more powerful than the central air conditioners used by most houses,” said Xiulin Ruan, a Purdue professor of mechanical engineering.

The researchers believe that this white may be the closest equivalent of the blackest black, “Vantablack,” which absorbs up to 99.9% of visible light. The new whitest paint formulation reflects up to 98.1% of sunlight – compared with the 95.5% of sunlight reflected by the researchers’ previous ultra-white paint – and sends infrared heat away from a surface at the same time.

Typical commercial white paint gets warmer rather than cooler. Paints on the market that are designed to reject heat reflect only 80%-90% of sunlight and can’t make surfaces cooler than their surroundings.

The team’s research paper showing how the paint works publishes Thursday (April 15) as the cover of the journal ACS Applied Materials & Interfaces.

What makes the whitest paint so white

Two features give the paint its extreme whiteness. One is the paint’s very high concentration of a chemical compound called barium sulfate, which is also used to make photo paper and cosmetics white.

“We looked at various commercial products, basically anything that’s white,” said Xiangyu Li, a postdoctoral researcher at the Massachusetts Institute of Technology, who worked on this project as a Purdue Ph.D. student in Ruan’s lab. “We found that using barium sulfate, you can theoretically make things really, really reflective, which means that they’re really, really white.”

The second feature is that the barium sulfate particles are all different sizes in the paint. How much each particle scatters light depends on its size, so a wider range of particle sizes allows the paint to scatter more of the light spectrum from the sun.

“A high concentration of particles that are also different sizes gives the paint the broadest spectral scattering, which contributes to the highest reflectance,” said Joseph Peoples, a Purdue Ph.D. student in mechanical engineering.

There is a little bit of room to make the paint whiter, but not much without compromising the paint.

“Although a higher particle concentration is better for making something white, you can’t increase the concentration too much. The higher the concentration, the easier it is for the paint to break or peel off,” Li said.

How the whitest paint is also the coolest

The paint’s whiteness also means that the paint is the coolest on record. Using high-accuracy temperature reading equipment called thermocouples, the researchers demonstrated outdoors that the paint can keep surfaces 19 degrees Fahrenheit cooler than their ambient surroundings at night. It can also cool surfaces 8 degrees Fahrenheit below their surroundings under strong sunlight during noon hours.

The paint's solar reflectance is so effective, it even worked in the middle of winter. During an outdoor test with an ambient temperature of 43 degrees Fahrenheit, the paint still managed to lower the sample temperature by 18 degrees Fahrenheit.

This white paint is the result of six years of research building on attempts going back to the 1970s to develop radiative cooling paint as a feasible alternative to traditional air conditioners.

Ruan’s lab had considered over 100 different materials, narrowed them down to 10 and tested about 50 different formulations for each material. Their previous ultra-white paint was a formulation made of calcium carbonate, an earth-abundant compound commonly found in rocks and seashells.

The researchers showed in their study that like commercial paint, their barium sulfate-based paint can potentially handle outdoor conditions. The technique that the researchers used to create the paint also is compatible with the commercial paint fabrication process.

Patent applications for this paint formulation have been filed through the Purdue Research Foundation Office of Technology Commercialization. This research was supported by the Cooling Technologies Research Center at Purdue University and the Air Force Office of Scientific Research through the Defense University Research Instrumentation Program (Grant No.427 FA9550-17-1-0368). The research was performed at Purdue’s FLEX Lab and Ray W. Herrick Laboratories and the Birck Nanotechnology Center of Purdue’s Discovery Park.

 

https://www.purdue.edu/newsroom/releases/2021/Q2/the-whitest-paint-is-here-and-its-the-coolest.-literally..html

Thursday, April 15, 2021

Is Artificial Intelligence coming after your job?

By David H Bailey, on January 30th, 2020, in the Mathematical Investor blog

Recent progress in artificial intelligence

It is no secret that artificial intelligence (AI) systems have made enormous strides in recent years, partly due to the adoption of Bayesian (probability-based) machine learning techniques rather than the rule-based techniques used until about 20 years ago. One highly publicized AI advance was the 2011 defeat of two champion contestants on the American quiz show “Jeopardy!” by an IBM-developed computer system named “Watson.” The Watson achievement was particularly impressive because it involved natural language understanding, i.e. the understanding of ordinary (and often tricky) English text. Legendary Jeopardy champ Ken Jennings conceded by writing on his tablet, “I for one welcome our new computer overlords.”

Then in 2016, a computer program named “AlphaGo,” developed by researchers at DeepMind, a subsidiary of Alphabet (Google’s parent company), defeated Lee Se-dol, a South Korean Go master, 4-1 in a 5-game tournament. Given the notoriously complicated nature of Go, with strategies that can only be described in vague, subjective terms, most observers had not expected Go-playing computer programs to beat the best human players for many years, if ever.

A year later, in October 2017, Deep Mind researchers announced results of a new program, called AlphaGo Zero, which was programmed only with the rules of Go and a simple reward function and then instructed to play games against itself. After just three days of training (4.9 million training games), the AlphaGo Zero program had advanced to the point that it defeated the earlier Alpha Go program 100 games to zero. After 40 days of training, AlphaGo Zero’s performance was as far ahead of champion human players as champion human players are ahead of amateurs. Additional details are available in an excellent New York Times analysis by mathematician Steven Strogatz.

Of course, AI systems are doing much more than defeating human opponents in games. Here are just a few of the current commercial developments:

  • Apple’s Siri and Amazon’s Alexa smartphone-based voice recognition systems are now significantly improved over the earlier versions, and speaker systems incorporating them are rapidly becoming a household staple.
  • Facial recognition has also come of age, for example with Apple’s 3-D facial recognition hardware and software, which is built into the latest iPhones and iPads as a security feature, eliminating the need to type passwords for many functions and websites.
  • Self-driving cars are already on the road, and 3.5 million truck driving jobs, just in the U.S., are at risk within the next ten years.
  • Numerous applications of AI have been fielded in the medical arena, including AI-powered surgical robots, AI-powered radiology (which now out-performs humans at some tasks), and AI-powered entry and analysis of medical data.
  • Other occupations likely to be impacted include package delivery drivers, construction workers, legal workers, accountants, report writers and salespeople.

For other examples and additional details see this Math Scholar blog.

What will be the impact of AI on employment?

Throughout history, fears have been raised about the employment impact of automation and technology. In the early 1800s, workers later called Luddites started breaking machines in British textile mills that they viewed as threats to their jobs. In the early 1900s, John Philip Sousa worried that the invention of the record player would render obsolete “the ennobling discipline of learning music” and put many professional musicians out of work (his fears were not realized — today there are more professional musicians than in Sousa’s day). Similarly, horse wranglers and blacksmiths feared that the new-fangled horseless carriages would put them out of work (they did, although millions are now employed in manufacturing, selling and servicing automobiles).

Even today, many have expressed concern about the impact of rapidly advancing technology in the workplace. An Oxford University report, for instance, notes that smart industrial robot installations have more than doubled since 2010, and that cumulative job losses due to smart robots have also more than doubled. A separate Oxford University study predicts that roughly 47% of current U.S. employment is at risk to computerization and automation, with heretofore uncomputerized occupations at the highest risk. A Century Foundation report warns that the pace of these changes may overwhelm the ability of workers to find new jobs and the capacity of social institutions to help.

Many have responded to these developments by observing that in our era, as in earlier eras, technology advances have led to productivity increases, which have advanced standards of living worldwide, and have opened the doors to new and, in most cases, more creative and fulfilling work than before. As Alex Tabarrok observes, if it were true that technology destroys jobs without replacing them, then “we would all be out of work because productivity has been increasing for two centuries.”

Yet some still worry that today’s situation is somehow fundamentally different. Are computers and AI becoming too smart, too fast? Will the requisite dislocations in the economy be too painful? Can governmental, educational and cultural institutions change fast enough? Will there any meaningful work for humans to do in the future?

How will AI and computer technology affect finance?

Until recently, many in the finance may have considered these developments not really applicable to their field — smart robots, for instance, may be useful in auto manufacture and medicine, but not in finance… True, no one has yet proposed a finance application for a smart robot. But machine learning, AI and the larger realm of computer-intensive technology is already having a significant impact in the field, and all signs point to a much greater role in the future.

According to a Bloomberg report, some specific areas that are prime for automation include:

  • Sell side credit markets: Natural-language processing, data collection and machine learning are being applied to automate subjective human decisions.
  • Sell side foreign exchange: Big data and machine learning are being used to anticipate variations in client demand and the resulting price swings.
  • Sell side commodities: Trader and salesperson conversations are being catalogued to create profiles of clients.
  • Sell side equities: Artificial intelligence is being applied to order execution.
  • Buy side equities: Predictive analytics is being applied to time stock purchases and assess risk based on market liquidity.
  • Buy side credit: Computer programs are being trained to scan and understand bond covenants, legal documents and court rulings.
  • Buy side macroeconomics: Natural-language processing is being used to analyze central bank commentary for clues on monetary policy. Other software is analyzing data such as oil-tanker shipments and satellite images (e.g., Chinese industrial sites, Walmart parking lots and more) to spot trends in the economy.

Other potential applications for machine learning, AI and big data in finance are highlighted in two previous Mathematical Investor blogs: Blog A and Blog B.

Overall, what are the prospects for employment in the finance field?

It is clear that numerous opportunities await professionals with solid research credentials in state-of-the-art machine learning and artificial intelligence techniques and their application to finance. But what about for others in the field? Do they have a future?

Here the picture is somewhat cloudy. In fact, this subject was the topic of a December 2019 hearing at the U.S. House of Representatives. Here are some comments from Marcos Lopez de Prado’s prepared statement:

Financial [machine learning] creates a number of challenges for the 6.14 million people employed in the finance and insurance industry, many of whom will lose their jobs, not necessarily because they are replaced by machines, but because they are not trained to work alongside algorithms. The retraining of these workers is an urgent and difficult task. But not everything is bad news. Minorities are currently underrepresented in finance. As technical skills become more important than personal connections or privileged upbringing, the wage gap between genders, ethnicities and other classifications should narrow. The key is to ensure equal access to technical education. In finance, too, math could be “the great equalizer.”

Retraining our existing workforce is of paramount importance, however it is not enough. We must make sure that America retains the talent it develops. The founders of the next Google, Amazon or Apple are this very morning attending an engineering or math course at one of our Universities. Unlike in the past, odds are that these future entrepreneurs are in our country on a student visa, and that they will have a very hard time remaining in the United States after their graduation. Unless we help them stay, they will return to their countries of origin with their fellow students, to compete against us in the near future, hindering our competitive advantage.

Numerous other trends in the industry point to storm clouds ahead in the field:

  1. The chronic under-performance of actively managed mutual funds. It is embarrassing but true that few actively managed mutual funds out-perform well-known market indices over a long-term time horizon. For example, only 8.3% of U.S. actively managed large-cap value and large-cap growth mutual funds survived and out-performed their equivalent index-based funds for the 10-year period ending February 2019. These statistics indicate that many actively managed funds do not add value, and thus are subject to declines and closure as investors head elsewhere.
  2. The chronic under-performance of actively managed hedge funds. The picture is somewhat better in the hedge fund world, although still arguably subpar. For example, the HFRI Fund Weighted Composite Index, scaled to 1.00 at 1990, by 2018 increased to 14.34. But the scaled S&P 500 index increased to 15.10 over the same time period. Some hedge funds consistently beat market averages — for example, Renaissance Technologies’ Medallion Fund has delivered annual returns averaging a whopping 39%, after fees, from 2011 to 2018, with similarly high returns extending back nearly 30 years. But successful funds such as the Medallion Fund are, almost exclusively, the realm of highly sophisticated, highly mathematical, highly big-data-intensive quantitative operations. Other hedge funds, by and large, do not do nearly as well, and are under heavy pressure from major investors to either streamline their operations (and reduce their high fees) or cease operations.
  3. The steady increase in the share of passively managed assets. Another measure of these trends is the rise in the fraction of total market assets that are passively managed, as opposed to actively managed. By one measure, passive assets have increased to 45% of the U.S. market, up from only 25% in 2010. Needless to say, passively managed funds require many fewer trained staff than actively managed funds, as evidenced by their much lower fee structures — as low as 0.05%, compared with 1.00% or higher for most actively managed funds.
  4. The failure of charting and technical analysis. For many years, a large sector of the investing community has relied on relatively unsophisticated approaches, such as charting and “technical analysis.” Tragically, these obsolete and statistically dubious techniques are even promoted by major financial news organizations and brokerages. Yet all available evidence indicates that these methods do not work in today’s market, which is dominated, as noted above, by mathematically sophisticated, machine-learning based, big-data intensive operations. As awareness of this fact increases, those sectors of the investment world that continue to rely on these outdated techniques are doomed to suffer declines in business and employment.
  5. The persistence of backtest overfitting and other statistically erroneous practices. In spite of years of effort by knowledgeable scholars to educate practitioners in the field about the dangers of backtest overfitting and other statistical errors, these practices still persist. But as more investors, institutional and individual, become aware of these difficulties, those investment organizations that cannot cite solid, independent certification that their products and services are free from these errors are doomed to see declines.
  6. A growing realization that only a big-data, machine-learning approach can hope to consistently achieve higher-than-market-average returns in today’s high-tech market. The consistent message of many indicators and trends in the field is that investors should Go with big data and machine learning, or leave finance to those who do.

What is the best training for finance professionals?

In short, a growing list of indicators and trends in the finance field suggest that major readjustments and realignments lie ahead. What can one do to ensure that one will be at the forefront of these developments, rather than be left in the dust?

One good technical reference here is the recently-published book Advances in Financial Machine Learning by Marcos Lopez de Prado. It explores commonly used data structures in finance, modeling techniques, backtesting techniques (and ways to avoid backtest overfitting), and other more advanced techniques based on a machine learning approach.

A solid graduate training program in the field would also help. But there is concern that university curricula are not keeping up with these developments. For example, in his Bloomberg column, Noah Smith wonders whether the current training for finance PhDs in particular is the best preparation for careers in the field. He suggests forming academic tracks that guide students to employment in the industry, possibly including apprentice-like research done in conjunction with advisers in the private sector, with dissertation research possibly done in team efforts rather than alone.

Marcos Lopez de Prado has also expressed concern about the typical preparation of researchers in finance careers. He notes, for example, that econometric models often employ statistical practices, such as multiple testing, that are not only considered ineffective but also downright unethical in other scientific research fields. And while most mathematical training for such persons is in areas such as linear algebra and calculus, topics such as graph theory, topology, discrete mathematics, information theory and signal processing are rising in importance.

In a Institutional Investor commentary, Lopez de Prado goes further, arguing that “The presence of financial academia is fading, something that was unthinkable 10 years ago.” He adds, “The [leading] edge is not yet another reincarnation of the capital asset pricing model,” but instead it is in analyzing heretofore untapped data sources. He adds that emerging technologies such as FinTech, machine learning and quantum computing are likely to render traditional academic education in finance even more irrelevant. Compounding the problem is that many academic journals in the finance field are mostly geared as “tenure-track vehicles” for aspiring professors, rather than venues for state-of-the-art research by practitioners. Similarly, books in the field are written by authors who, in many cases, have not actually attempted to field their techniques. As Lopez de Prado explains, “They contain extremely elegant mathematics that describe a world that does not exist.”

As David H. Bailey and Lopez de Prado further argued in a Forbes commentary, interviewed by Brett Steenbarger, rigorous training in statistics is typically not given its appropriate emphasis for prospective finance professionals, PhD or not. As a result, the finance field, as noted above, is replete with backtest overfitting and multiple-testing errors and, even more significantly, many in the field fail to appreciate how deeply these difficulties pervade modern finance, and the extent to which institutional customers and individual investors are potentially misled by inaccurate claims.

Some additional observations about training for finance professions are presented in a previous Mathematical Investor blog.

Nirvana or brave new world?

However these trends turns out, many worry about what the future holds for “average” workers, not only in finance but also in the broader economy. Will the future be a nirvana of creative, interesting and fulfilling work, or a brave new world where most humans are hopelessly relegated to secondary status by computational overlords?

Some of these questions were addressed by Yuval Noah Harari in his recent book Homo Deus: A Brief History of Tomorrow. He argues that future technology, and AI in particular, draws into question many of the bedrock systems that underpin modern society, and may eventually lead to a “post-human” world. But whatever happens in the far future, society faces the more immediate need to substantially improve the education new workers, to retrain many of those who are displaced, and to humanely deal others who find themselves in industries and occupations that are no longer economically valued.

So to a large extent, the future will be what we make it to be.

                 https://mathinvestor.org/2020/01/is-ai-coming-after-your-job/

 

Wednesday, April 14, 2021

Fast Growing Fed Financial Bubble

If economics was a stable and solid science, wouldn’t central bank steering of a G7 currency be rare and deftly done?  But it happens all the time and seems to weaken growth!

The Everything Bubble: The Endgame For Central Bank Policy 

By Graham Summers, MBA -- Paperback – Published October 22, 2017

Summary by Amazon.com

The Everything Bubble chronicles the creation and evolution of the US financial system, starting with the founding of the US Federal Reserve in 1913 and leading up to the present era of serial bubbles: the Tech Bubble of the ‘90s, the Housing Bubble of the early ‘00s and the current bubble in US sovereign bonds, which are also called Treasuries. Because these bonds serve as the foundation of our current financial system, when they are in a bubble, it means that all risk assets (truly EVERYTHING), are in a bubble, hence our title, The Everything Bubble. In this sense, the Everything Bubble represents the proverbial end game for central bank policy: the final speculative frenzy induced by Federal Reserve overreach. The Everything Bubble book is the result of over a decade of research and analysis of the financial markets and economy by noted investment analyst, Graham Summers, MBA. As such, this book is intended for anyone who wants to understand how the US financial system truly operates as well as those interested in the Federal Reserve’s future policy responses when the Everything Bubble bursts. To that end, The Everything Bubble is divided into two sections: How We Got Here and What’s to Come. Combined, these sections represent a blueprint for all things finance and money-related in the United States.This knowledge is now yours.

Review of the Book by a Reader

From Michael Wilson

4.0 out of 5 stars  I mean aren't they the smart guys? This book ties it all together”

Reviewed in the United States on January 12, 2018

Verified Purchase

Although I already knew the history of the Fed and had read many of the books referenced - although I have lived through the blowing of three bubbles, the implosion of two, and am now awaiting the implosion of the third - I never understood the ignorance of central bankers. I mean aren't they the smart guys?
This book ties it all together - why the fed did what they did and have then been forced to stay on the same path. I mean, after watching the mistakes of Japan and the BOJ, why repeat them?
I saw it happen and I did realize that the Fed was opting for Japan II over Great Depression II in 2009 - but why not a third option?
This book explains the trap central bankers are caught in. Unfortunately it also explains what is likely to happen when this, the third central bank bubble, also implodes.
Unfortunately it doesn't lay out a specific action plan as for what to do. That's why I only gave it 4 stars.

Tuesday, April 13, 2021

Genetic Modification for Malaria Mosquitoes

Altering a mosquito's gut genes to make them spread antimalarial genes to the next generation of their species shows promise as an approach to curb malaria, suggests a preliminary study published today in eLife.

April 13, 2021 -- The study is the latest in a series of steps toward using CRISPR-Cas9 gene-editing technology to make changes in mosquito genes that could reduce their ability to spread malaria. If further studies support this approach, it could provide a new way to reduce illnesses and deaths caused by malaria.

Growing mosquito resistance to pesticides, as well as malaria parasite resistance to antimalarial drugs, has created an urgent need for new ways to fight the disease. Gene drives are being tested as a new approach. They work by creating genetically modified mosquitoes that, when released into the environment, would spread genes that either reduce mosquito populations or make the insects less likely to spread the malaria parasite. But scientists must prove that this approach is safe and effective before releasing genetically modified mosquitoes into the wild.

"Gene drives are promising tools for malaria control," says first author Astrid Hoermann, Research Associate at Imperial College London, UK. "But we wanted a clear pathway for safely testing such tools in countries where the disease most commonly occurs."

In the study, Hoermann and colleagues genetically modified the malaria-transmitting mosquito Anopheles gambiae. They used the CRISPR-Cas9 technology to insert a gene that encodes an antimalarial protein amidst genes that are turned on after the mosquito eats a blood meal. The team did this in a manner that allowed the whole section of DNA to also function as a gene drive that could be passed on to most of the mosquitoes' offspring. They initially inserted the gene along with a fluorescent marker to help them track it in three different spots in the DNA, and then later removed the marker, leaving only a minor genetic modification behind.

Next, the team bred the mosquitoes to see if they were able to successfully reproduce and remain healthy. They also tested how well the malaria parasite developed in the mosquitoes' guts. Their experiments provide preliminary evidence that this approach to genetic modifications could create successful gene drives.

"These genetic modifications are passive, and could be tested in the field and undergo a stringent regulatory process to ensure they are safe and effective in blocking the parasite without raising concerns of accidental spread in the environment," explains senior author Nikolai Windbichler, Senior Lecturer at the Department of Life Sciences, Imperial College London. "However, once we combine them with other mosquitoes with an active gene drive, they turn into gene drives themselves without the need for any further changes. Our approach thus brings gene drives one step closer to being tested in the field as a malaria elimination strategy."


Story Source:  Materials provided by eLife.  Note: Content may be edited for style and length.

             https://www.sciencedaily.com/releases/2021/04/210413110620.htm