Often mocked by classmates growing up, Tedford was born with brain damage that left him with the intellectual ability of afourth-grader. The damage also left him deaf, legally blind in one eye and with partial paralysis.

With the help of co-author Paul Smith, Tedford recently embarked on his latest book,Four Days With Kenny Tedford: Life Through the Eyes of a Child Trapped in a Partially Blind and Deaf Mans Body. The book, which will be released Nov. 26, discusses what its like to live with disabilities and explores Tedfords experiences with trauma.

I have spoken all around the United States, Canada and Norway. My audience varies, from preschoolers to senior citizens as old as 99 years. I have spoken to high school-age groups, college students, the Veterans Administration, churches and major state and international conventions of different occupations, such as interpreters, teachers, airlines, educators, etc. This experience has led me to write this book, Tedford, who has had to work to overcome difficulties speaking, wrote in an email to the Press.

Tedford who has worked as a counselor and former executive director of the TennesseeCouncil for the Deaf and Hard of Hearing recentlyreached out to the Press to tell us more about his book and himself, starting with some fast facts.

Tedford Briefly:

Hobbies: Hiking, horseback riding and kickboxing.

Favorite musicians: Bing Crosby, Frank Sinatra and Susan Boyle

Favorite food: Mexican food

Dogs or cats: Definitely dogs. Yellow Labs!

Ideal vacation: Being in a log cabin in the mountains with my hot tub and a cup of coffee. No one else is around, and spending time with God.

Can you tell our readers a bit more about your book?

My book is a collection of personal stories from my life. In it, I tell about my experiences with trauma and the loss of my family. The stories touch on how I overcame a broken neck, open-heart surgery, stroke, cancer, learning disabilities, and with the physical disability of deafness and depression. Overall, I hope the book will help others to see how they can also overcome any kind of trauma or obstacles. And I want to let them know that a person like me, labeled as mentally retarded, grew up to become a motivational speaker, actor, comedian, storyteller, and, now, a professor at East Tennessee State University. And, as a Christian, I give all the credit to God for everything and for who I am today.

What challenges did you experience growing up and how did you overcome them?

In addition to what I explained above, the biggest challenge that I had to overcome, even to this day, is communication with the hearing public. I overcome all challenges by my faith in God, and by living how Mom and Dad taught me, to just be myself, a loving son and loving to others.

What made you decide to write the book?

I have been told for the past 50 years, since high school, that I was a great storyteller, and a very funny comedian, and encouraged to write a book. It wasn't until I was performing my cancer story in Cincinnati, Ohio, at the National Storytelling Conference, when I was approached by a great writer, another motivational speaker, Paul Smith. He was very impressed and moved by my story, and was surprised that I didn't have a book with that story in it. He had already written four well-known books, and he decided to co-write a book with me. I hope that the audiences that I perform in front of will be able to have hope and never to give up, and to learn to love themselves as they are.

Do you think society is becoming more accommodating to people with disabilities?

I think, because of technology, that people are becoming more aware of those with disabilities around them. As for the deaf, I have seen some improvement, but there is still a long way to go. I say that because it is often difficult to get interpreters for hospital and doctor visits, etc. With technology, including video phones, text messaging, etc., we are being brought closer to access to hearing people. I pray that the book "Four Days with Kenny Tedford" will educate everyone, with or without a disability, and bring us together in unity with respect as individuals.

Who is your biggest inspiration in life?

I have two people that are famous who have been my inspirations, and role models, Abraham Lincoln, and Helen Keller.

Abraham Lincoln overcame a great deal of trauma and the death of loved ones in his life. Many people don't know this, but he was also depressed and suicidal. He ran for so many offices and lost, but this did not stop him from running again and becoming one of the greatest (to me) presidents of all. He shows me that one can overcome anything in life if you just persevere and not give up. He, too, had his personal faith in God that helped him. He was a very faithful man.

Helen Keller watching her life story in movies and reading about it in books, I see that she practically grew up as an animal with no education or any human behavior training. And yet, she became (to me), one of the most amazing motivational speakers I have ever seen, especially for a person who was deaf and blind. She also wrote many books.

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Motivational speaker talks overcoming disabilities in "Four Days with Kenny Tedford" - Johnson City Press (subscription)

Evolutionary scientists have been thinking about same-sex sexual behavior all wrong.

That's the implication of a new study on same-sex behavior in animals. Instead of asking why animals engage in same-sex behavior (SSB), researchers should be asking, "Why not?" the authors said.

If they're right, same-sex sex may not have evolved independently in different animals for adpative reasons. Instead, same-sex sex may have emerged very early in time and could persist simply because engaging in it doesn't cost animals much, evolutionarily speaking.

"Usually, when evolutionary biologists see a trait that's really widespread across evolutionary lineages, we at least consider the idea that the trait is ancestral and was preserved in all those lineages," said Julia Monk, a doctoral candidate at Yale University, who co-authored the new research. "So why hadn't people considered that hypothesis for SSB?"

Related: Alternative Lifestyles in the Wild

In evolutionary science, same-sex sexual behavior has long been viewed as a conundrum: Why would animals spend time and energy doing something sexual that won't pass along their genes to the next generation? And yet, same-sex sexual behavior has been observed in at least 1,500 species, ranging from lowly squash bugs to humans.

(To avoid anthropomorphizing, the researchers don't use the terms "homosexual," "heterosexual," "gay" or "straight" to refer to animal behavior.)

"We can't assign sexuality to animals we're trying our best to learn about them by observing their behaviors," Monk told Live Science. "And those behaviors shouldn't be mapped onto human cultural and societal contexts."

The assumption that there must be an evolutionary reason for all this same-sex sex has led researchers to search for possible benefits to same-sex behavior. For example, in humans, researchers have found that having a gay son or brother seems to be associated with a woman having more offspring in total. Other studies have posited that same-sex sexual behavior is a side effect of other genes that have reproductive benefits.

In evolutionary biology, the ability of an animal to reproduce given its environment is called fitness. It's entirely possible that in some species, same-sex sex could have fitness benefits, Monk and her colleagues wrote in their paper, published Nov. 18 in the journal Nature Ecology & Evolution. But these evolutionary benefits may not be required for same-sex sexual behavior to exist.

Imagine, instead, that the earliest sexually reproducing animals simply tried to mate with any and all members of their species regardless of sex. This might have been a logical pathway for evolution, because all the bells and whistles that distinguish males from females are energetically costly to evolve. So any effort expended on mating with the same sex would be compensated for by not spending energy evolving and maintaining distinctive secondary sex characteristics, like differing colors, scents and behaviors. Those sex-distinguishing traits may have all come later in the evolutionary chain, the authors argued.

In this formulation, same-sex and different-sex sexual behavior would have started out on an equal footing, early in animal evolution. This could explain why same-sex sex is so common throughout the animal kingdom: It didn't evolve multiple times independently, but was instead part of the fabric of animal evolution from the start.

The new hypothesis undercuts old assumptions about same-sex behaviors, said Caitlin McDonough, a doctoral candidate at Syracuse University and a study co-author. Much of the research done on these sexual behaviors assumes that same-sex sex is costly for animals and that different-sex sex is not costly, she said.

"You really need to go through those assumptions and test the costs and benefits of both behaviors in a system," McDonough said.

If same-sex behaviors go back to the roots of animal evolution, the fact that these behaviors are so common today makes sense, Monk said.

"If you assume a trait like SSB is a new development and has high costs, it's going to be really hard to understand how it could become more and more common from those low initial frequencies," she said. "It would have to have really large fitness benefits, or be otherwise impervious to natural selection, for that outcome to be probable.

"On the other hand, if you assume a trait is ancestral and was originally common, and it has low costs, it's much more likely that it would remain widespread to this day, even if it doesn't seem to contribute much to fitness."

One piece of evidence supporting this hypothesis is that some echinoderms, including sea stars and sea urchins, engage in same-sex sexual behavior. Echinoderms evolved early in the history of life, likely in the Precambrian period more than 541 million years ago.

But other evidence is slim, largely because scientists haven't systematically studied same-sex sexual behavior in animals. Most observations have been accidental, and biologists have often viewed sex between two animals of the same sex as irrelevant or improper to note, Monk said. Sometimes, researchers automatically assume that same-sex behavior isn't really about sex but instead is about dominance or bonding. And often, if two animals are observed having sex, they're assumed to be male and female without any confirmatory evidence, McDonough said.

"The science that we do is really informed and influenced by cultural biases," she said.

Thinking of same-sex sexual behavior as a standard part of the animal repertoire would change how researchers approach the study of the evolution of these behaviors. The next step, Monk said, would be to gather more data on the prevalence of same-sex behavior in animals. Then, researchers could compare species from across the tree of life to determine if all linages show same-sex behavior. If so, it would strengthen the argument that same-sex sexuality was part of life for the ancestors of all of today's sexually reproducing animals.

Originally published on Live Science.

Read more from the original source:
Was Same-Sex Behavior Hardwired in Animals from the Beginning? - Livescience.com

The evolution of cooperative behavior does not necessarily require that animals develop a sense of fairness

A pair of blue-throated macaws (Ara glaucogularis) at Chester Zoo, England. These birds, like most ... [+] parrot species, form strong life-long monogamous pair bonds.(Credit: David Friel / CC BY 2.0)

As anyone who lives with parrots knows, they are very jealous and protective of their mates whether their mate is another parrot or a human. But parrots are not jealous about food. Basically, if one parrot is given a less desirable food reward than its partner gets, there are no temper tantrums which is the typical response to unequal resource distribution seen in great apes (and even in a large number of humans). Apparently, unlike most primates, parrot couples are quite tolerant of inequality.

This was the conclusion reached recently by a team of scientists from theMax Planck Institute for Animal Behaviour, a newly-independent research institution in Germany formerly known as the Max Planck Institute for Ornithology.

A key feature in the evolution of cooperation is a sense of fairness, where individuals are provided rewards that are fair and equitable to avoid future breakdown in cooperation (ref). Humans, even children, show a clear and consistent preference for equal over unequal outcomes (ref), a trait known as inequity aversion. For example, children as young as six years old refuse a reward that is less valuable than that given to a peer (disadvantageous inequity aversion), whereas older children typically refuse a reward that is more valuable than that provided to a peer (advantageous inequity aversion). Social scientists consider disadvantageous inequity aversion to be a universal feature of human behavior (PDF), whilst advantageous inequity aversion is probably influenced strongly by cultural norms.

Scientists think that sensitivity to inequity evolved in parallel with the ability for individuals to cooperate because it helps sustain benefitting from cooperation. Additionally, it has been proposed that species that rely on cooperating with group members may benefit from evaluating the equality of their cooperative payoffs to assess whether to stay with a certain partner, or mate, or to look for a new one to gain better outcomes.

The token-exchange paradigm is a classic tool in animal behavior studies that is used to study fairness. It tests the willingness of an animal to sacrifice its own material pay-offs for the sake of greater equality. In this test, when an experimenter does not equally distribute rewards of equal value between experimental subjects, the study animals may express their displeasure with a variety of responses, ranging from refusing to participate in further tests, throwing the reward at the experimenter or even with a temper tantrum.

A team of scientists trained four species of parrots held by the Max-Planck Comparative Cognition Research Station at Loro Parque in Spain, to trade tokens for food. The researchers were under the leadership of Anastasia Krasheninnikova, a postdoctoral researcher at the Institute whose main research interest is the evolution of cognitive skills, particularly identifying whether these skills may be influenced by different socio-ecological living conditions.

This study was designed to provide data on inequity aversion in species, such as parrots, that are distantly related to primates. It also was designed to provide more data on inequity aversion in long-term monogamous species by testing the reaction to inequity of four parrot species belonging to the Psittacoidea superfamily: blue-throated macaws, Ara glaucogularis; great green macaws, Ara ambiguus; blue-headed macaws, Primolius couloni; and African grey parrots, Psittacus erithacus. These species all form long-term monogamous pair bonds and live in family groups. Both parents provide food to their young and offspring stay with their parents for at least one breeding season.

After the study parrots had been trained to trade tokens (a steel washer) for a food reward (either a sunflower seed or a piece of a walnut), the team of researchers then conducted a series of experiments where two parrots were placed into adjacent compartments and each was asked, in turn, by the experimenter to exchange a token for food. Depending upon the experimental treatment, the parrots were either rewarded unequally (Figure 1A) or equally (Figure1B,C) for their efforts. During the tests, the parrots could easily see each other and the food rewards that each parrot got.

Figure 1. Experimental design showing the test and control conditions. Sunflower seeds are ... [+] low-quality rewards and walnut seeds are high-quality rewards.(Image courtesy of Anastasia Krasheninnikova and colleagues.)

Dr. Krasheninnikova and her collaborators observed how the study parrots reacted when they received a food reward with a differing quality for the same effort (unequal reward; Figure 1A) or when one parrot was expected to work harder than his or her partner for the same reward (unequal effort; Figure 1E). When the study parrots reactions were compared to what was observed for equal treatment for each species (Figure 1BC), Dr. Krasheninnikova and her collaborators saw no differences and they definitely did not see any temper tantrums (data video and Figure 2).

Of course, the study parrots had their own opinions about the study. For example, in the control test (when the neighboring chamber was empty), the two bigger macaw species refused to exchange tokens if a better reward was delivered to the empty enclosure (Figure 1D), whilst the smaller parrot species did not refuse to exchange tokens but took longer to do so (African grey parrots) or longer to accept rewards (blue-headed macaws).

Dr. Krasheninnikova and her collaborators found that the blue-throated macaws apparently became increasingly frustrated with the test procedure, whereas the African grey parrots showed the opposite pattern and escalated their number of exchanges in all conditions during the course of the study. This response might indicate that the grey parrots became so familiar with the exchange procedure that they stopped caring about the rewards and their distribution.

These distinct responses reveal strong species differences in their innate sensitivity to reward quality and their frustration (or motivation) with the token exchange task itself, making comparative studies very difficult to interpret.

Figure 2. (AD) Exchanges across test conditions separately for each species (all test sessions ... [+] combined; EQUL = equal low, EQUH = equal high, UNEQ = unequal, FC = food control, EC = effort control, UNEF = unequal effort). (Anastasia Krasheninnikova et al. | doi:10.1038/s41598-019-52780-8)

This study raises an important question: why arent parrots jealous when their partner gets a better reward for the same effort or when one parrot must work harder for the same reward? The answer, we think, can be summed up in just one word: monogamy. Unlike most mammals, most parrot species form long-term monogamous pair bonds and both parents care for their nestlings.

In contrast, it turns out that in long-term monogamous species that form pair bonds for life and show biparental care, such as parrots, there is a much higher tolerance of inequity, Dr. von Bayern said. In such species, individuals greatly depend on their functioning pair-bond and consequently, disrupting such a valuable bond in order to look for a fairer partner would simply be too costly.

But not all parrot species are monogamous. For example, Eclectus parrots, Eclectus species, and Vasa parrots, Coracopsis species, are polygynandrous, whilst a number of other species are socially cooperative breeders, including the golden parakeet, Guaruba guarouba, New Caledonian parakeet, Cyanoramphus saisseti, the horned parakeet, Eunymphicus cornutus, and the monk (quaker) parakeet, Myopsitta monachus. Testing these species using the token-exchange paradigm could provide important insights into whether inequity aversion is a general trait amongst all parrots or whether it is linked to social organization and species mating system.

This is an important finding because inequity aversion, also termed sense of fairness has been considered an important mechanism in the evolution of cooperative behaviour, Dr. Krasheninnikova said in a press release.

It enables individuals to detect when their partner cheats upon them, e.g. by not sharing food equally or by avoiding effort, and therefore allows them to decide when it pays off to switch to a new cooperation partner.

It is a good strategy only for animals living in societies in which one can switch between cooperation partners easily, such as those of most primates, agreed senior author, zoologist Auguste von Bayern, in a press release.

Social organization plays a strong role in whether an individual can easily switch to a new mate or cooperation partner but how strong is the effect of social organization upon the evolution of cooperation?

Our study adds to the very recent evidence, that inequity aversion is not a general prerequisite for the evolution of cooperation, Dr. von Bayern said in a press release.

Anastasia Krasheninnikova,Dsire Brucks,Nina Buffenoir,Dniel Rivas Blanco,Delphine Soulet&Auguste von Bayern(2019). Parrots do not show inequity aversion, Scientific Reports9:16416 | doi:10.1038/s41598-019-52780-8

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Parrots Aren't Jealous When Their Mate Gets A Better Reward - Forbes

Enlarge / But what does that car think that the spectator is thinking?

Imagine you're trying to make a left turn onto a busy road. Car after car rolls past, keeping you trapped as your frustration rises. Finally, a generous driver decelerates enough to create a gap. A check of the traffic from the opposite direction, a quick bit of acceleration, and you're successfully merged into traffic.

This same scene plays out across the world countless times a day. And it's a situation where inferring both the physics and the motives of your fellow drivers is difficult, as evidenced by the fact that the United States sees 1.4 million accidents each year from drivers in the process of turning. Now imagine throwing autonomous vehicles into the mix. These are typically limited to evaluating only the physics and to make conservative decisions in situations where information is ambiguous.

Now, a group of computer scientists has figured out how to improve autonomous vehicle (AV) performance in these circumstances. The scientists have essentially given their AVs a limited theory of mind, allowing the vehicles to better interpret what the behaviors of their nearby human drives are telling them.

Theory of mind comes so easily to us that it's difficult to recognize how rare it is outside of our species. We're easily able to recognize that our fellow humans have minds like our own, and we use that recognition to infer things like the state of their knowledge and their likely motivations. These inferences are essential to most of our social activities, driving included. While a friendly wave can make for an unambiguous signal that your fellow driver is offering you space in their lane, we can often make inferences based simply on the behavior of their car.

And, critically, autonomous vehicles aren't especially good at this. In many cases, their own behavior doesn't send signals back to other drivers. A study of accidents involving AVs in California indicated that over half of them involved the AV being rear-ended because a human driver couldn't figure out what in the world it was doing. (Volvo, among others, is working to change that.)

It's unrealistic to think that we'll give AVs a full-blown theory of mind any time soon. AIs are simply not that advanced, and it would be excessive for cars, which only have to deal with a limited range of human behaviors. But a group of researchers at MIT and Delft University of Technology has decided that putting an extremely limited theory of mind in place for certain driving decisions, including turns and merges, should be possible.

The idea behind the researchers' work, described in a new paper in PNAS, involves a concept called social value orientation, which is a way of measuring how selfish or community-oriented an individual's actions are. While there are undoubtedly detailed surveys that can provide a meticulous description of a person's social value orientation, autonomous vehicles generally won't have the time to be giving their fellow drivers surveys.

So the researchers distilled social value orientation into four categories: altruists, who try to maximize the enjoyment of their fellow drivers; prosocial drivers, who try to take actions that allow all other drivers to benefit (which may occasionally involve selfishly flooring it); individualists, who maximize their own driving experience; and competitive drivers, who only care about having a better driving experience than those around them.

The researchers developed a formula that would let them calculate the expected driving trajectory for each of these categories given the starting position of other cars. The autonomous vehicle was programmed to compare the trajectories of actual drivers to the calculated version and use that to determine which of the four categories the drivers were likely to be in. Given that classification, the vehicle could then project what their future actions would be. As the researchers wrote, "we extend the ability of AVs' reasoning by incorporating estimates of the other drivers' personality and driving style from social cues."

This is substantially different from some game-theory work that's been done in the area. That work has assumed that every driver is always maximizing their own gain; if altruism emerges, it's only incidental to this maximization. This new work, in contrast, bakes altruistic behavior into its calculations and recognizes that drivers are complicated and may change their tendencies as situations evolve. In fact, previous studies had indicated that in contexts other than driving, about half of the people tested showed prosocial behavior, with another 40% being selfish.

With the system in place, the researchers obtained data on vehicle locations and trajectories as drivers merged onto a highway, a situation that often requires the generosity of fellow drivers. With the social value orientation system in place, the autonomous driver was able make more accurate predictions of its fellow drivers' trajectories than it could withoutprediction errors dropped by 25%. The system also worked on lane changes on crowded freeways, as well as turns into traffic.

Using these evaluations, the researchers could also make some inferences using the traffic patterns they had. For example, they found that a highway driver may start out selfishly following the car in front of them, shift to altruistic as they decelerate to allow a driver to merge, then switch right back to a selfish approach. Similarly, drivers facing a merge onto a freeway typically ended up being competitivesomething you see every time a vehicle pulls out and slows down everyone who was stuck in the lane behind it.

While we're still a long way off from giving autonomous vehicles a general AI or a full theory of mind, the research shows that you can get significant benefits from giving AVs a very limited one. And it's a nice demonstration that if we want any autonomous system to integrate with something that's currently a social activity, then paying attention to what social scientists have figured out about those activities can be incredibly valuable.

PNAS, 2019. DOI: 10.1073/pnas.1820676116 (About DOIs).

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Improving autonomous autos by having them guess which humans are selfish - Ars Technica

Balancing out the benefits of artificial intelligence to prevent future pitfalls

Ethics surrounding artificial intelligence and the use of Big Data were among the topics discussed at the GRC Summit in London this week.

Organizations looking to implement data-driven tools and leverage the benefits of artificial intelligence (AI) must first understand the risks that these technologies can pose.

That was the consensus held by a panel of industry stakeholders, who cited transparency, standards, and explainability as factors for businesses to consider when creating AI products.

The panel, which took place in London on Monday (November 18), included Laura Turner of the UNs World Food Programme, and Anna Fellnder, co-founder of the AI Sustainability Center.

The reason why ethics is exploding is because AI is different from other data-driven technologies, AI moves faster, Fellnder said.

Theres no transparency and a lack of explainability models.

Machine learning (ML) data trained algorithms that facilitate the automation of tasks and AI human behavior learned in machines are increasingly seen on the marketplace amid confusion of their actual capability.

According to one survey, 40% of European startups are misusing the term AI in their products, The Verge reported in March leading to more funding from investors and a less efficient experience for consumers.

In the security sector, where ML and AI have the potential to identify cyber-threats far faster and more accurately, a quarter of organizations told the Ponemon Institute that they had been using some form of the technologies in their defense solutions.

Organizations supply this seductive technology into their business models and push down costs, nudging their customers to behaviors that could be unethical, Fellnder said.

Regardless of the amount of snake oil out there, the amount of data now available allows for the algorithmic maturity needed to build products and services and equally made ethics surrounding ML and AI a more pressing concern.

Global governments, most notably in the European Union, have even made calls to regulate the use of AI so to prevent potential societal issues such as bias within algorithmic decision-making, violations of user privacy, and dangers in line with cyber-offensive firepower.

When we enter AI you get no control of it, Fellnder said.

Its about having a goal to market readiness in your AI applications, so you dont lead to the [possible] pitfalls, and making sure your values are sustained.

Sir Nigel Shadbolt, chairman and co-founder of the Open Data Institute, who closed the first day of the conference, agreed that more literacy and communication was needed, not only around ML and AI, but the wider data ecosystem.

Were seeing people starting to really worry about the idea of the balance, the interests that they have, the rights that they have, in this data, is somehow way out of whack, he said.

Its not about owning the data, but having some agency on whats being done with it.

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Calls for AI regulation heard at London summit - The Daily Swig

You finish that last sip of morning coffee and stare at the empty paper cup in your hand. Should it go into the recycling bin, compost, or be landfilled or incinerated?

You are not alone. Most Americans are confused about recycling, and the crisis driven by Chinas decision to stop accepting most foreign scrap material is worsening the problem. At this point its hard to be sure that items put in the recycling bin are recycled.

Research shows that more often than not, Americans give up trying to sort their recyclables. Or they engage in wishful recycling, tossing nonrecyclables into the bin. Even so, most waste never gets that far. People feel intimidated by the task.

The average American generates about 4.5 pounds of waste each day. Only 1.5 pounds of it is recycled or composted. This means that over an average lifetime of 78.7 years, one American would send 67,000 pounds of waste to landfills. Thats more than twice the weight of a cruise ship anchor.

Although many communities and advocates have adopted regulations and action plans centered on moving toward a circular economy, major barriers still make it hard for individuals to reduce, reuse and recycle. Existing policies have been developed based on insights from engineering and economics, and give little consideration of how human behavior at the individual level fits into the system.

My colleagues and I use behavior science to foster goals ranging from energy conservation to community solidarity. In a recent paper, economist Marieke Huysentruyt, Ph.D. candidate Emma Barnosky and I uncovered promising solutions to the recycling crisis driven by personal benefits and social connections.

Why recycling is so hard

Why is getting Americans to recycle more so challenging? First, many of them dont understand waste problems and recycling strategies. Few are aware of the environmental problems waste causes, and most have a hard time connecting individual actions to those problems.

Most people dont know where their waste goes, whether it includes recyclables or what can be made from them. They may know what day to put out curbside trash and recycling, but are unsure which materials the companies accept. In a 2019 survey of 2,000 Americans, 53% erroneously believed greasy pizza boxes could be recycled, and 68% thought the same for used plastic utensils.

Another 39% of respondents cited inconvenience and poor access to recycling facilities as major barriers. California pays a 5- to 10-cent redemption fee for each beverage container, but the facilities often are inconvenient to reach. For example, the closest to my home in Los Angeles is eight miles away, which can involve driving for an hour or more. Thats not worth it for the few cans my family produces.

Most U.S. consumers are opposed to pollution, of course, but research shows that they seldom view themselves as significant contributors. As taxpayers, they hold local governments responsible for recycling. Many are not sure what happens next, or whether their actions make a difference.

Motivation matters

What can be done to address these barriers? Better messaging, such as emphasizing how waste can be transformed into new objects, can make a difference.

But as I argue in my 2018 book, The Green Bundle: Pairing the Market With the Planet, information alone cant drive sustainable behavior. People must feel motivated, and the best motivations bundle environmental benefits with personal benefits, such as economic rewards, increased status or social connections.

In a 2014 survey, 41% of respondents said that money or rewards were the most effective way to get them to recycle. Take-back systems, such as deposits on cans and bottles, have proven effective in some contexts. Such systems need to be more convenient, however.

Returning bottles directly to stores is one possibility, but novel strategies are being deployed across the country. Pay-as-you-throw policies charge customers based on how much solid waste they discard, thus incentivizing waste reduction, reuse and more sustainable purchasing behavior. Recyclebank, a New York company, rewards people for recycling with discounts and deals from local and national businesses.

Status and support

Social status also motivates people. The zero-waste lifestyle has become a sensation on social media, driving the rise of Instagram influencers such as Bea Johnson, Lauren Singer and Kathryn Kellogg, who are competing to leave behind the smallest quantity of waste. Visibility of conservation behavior matters, and could be a powerful component in pay-as-you-throw schemes.

Its also nice to have support. Mutual help organizations, or community-led groups, trigger behavioral change through social connections and face-to-face interactions. They have the potential to transfer empowering information and sustain long-term commitment.

One famous example is Alcoholics Anonymous, which relies on member expertise instead of instructions from health care specialists. Similarly, Weight Watchers focuses on open communication, group celebration of weight loss progress and supportive relationships among members.

French startup Yoyo, founded in 2017, is applying this strategy to recycling. Yoyo connects participants with coaches, who can be individuals or businesses, to help them sort recyclables into orange bags. Coaches train and encourage sorters, who earn points and rewards such as movie tickets for collecting and storing full Yoyo bags.

The process also confers status, giving sorters positive social visibility for work that is ordinarily considered thankless. And because rewards tend to be local, Yoyos infrastructure has the potential to improve members community connections, strengthening the perceived and actual social power of the group.

This system offers a convenient, social, incentive-based approach. In two years the community has grown to 450 coaches and 14,500 sorters and collected almost 4.3 million plastic bottles.

Such novel behavior-based programs alone cannot solve back-end aspects of the global waste crisis, such as recycling capacity and fluctuating scrap material prices. But our research has shown that by leveraging technology and human behavior, behavioral science can encourage people to recycle much more effectively than simplistic campaigns or slogans.

[ Insight, in your inbox each day. You can get it with The Conversations email newsletter. ]

Magali (Maggie) Delmas, Professor of Management Institute of the Environment & Sustainability, Anderson School of Management, University of California, Los Angeles

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image: Reuters

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Major Barriers Still Make It Hard for Individuals to Reduce, Reuse and Recycle - The National Interest Online

Since the election of President Trump in 2016 psychiatrists have come forward violating The Goldwater Rule, not personally interviewing the president, and diagnosing him as having a mental disorder. Given this, the first question an intelligent, free-thinking individual must ask himself/herself is, "Is psychiatry really science?"

The problem psychiatry suffers from is it relies on subjective views of psychology to make a diagnosis. There are 300 mental disorders outlined in the Psychiatric Association's Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders. These disorders are based on subjective opinions and none are based on objective data drawn from double-blind, placebo-controlled studies. There are no biological tests substantiating that these so-called conditions exist. Lacking independent diagnostic tests, testable hypotheses, and cures for mental disorder; psychiatrists cannot accurately and reliably predict dangerousness, violence, or any other type of human behavior. In the absence of biological test, psychiatrists have proclaimed themselves "expert witnesses" in this pseudo-science. Conspiring with the media, they push the myth of the "dangerous mental patient" stereotype.

Because they voted for the president, a psychiatrist has determined that 63 million voters have a mental disorder. Think about that for a moment; Americans are mentally disturbed for exercising their civic duty?

Earnest Rutherford, a renowned British physicist, stated that "all science is physics or stamp collecting. Psychiatry seems is at best a stamp-collecting activity. ... Any attempt to manipulate behavior seems about as far from applied science as breeding plants with no concept of genetics."

ELVIS DUNN, Jackson

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Letter to the Editor: The mental disorder theory (11/21/19) - Southeast Missourian

A more than 300 percent increase in enrollment in a recently created undergraduate neuroscience major could expose more students to smaller academic departments like speech, language and hearing sciences, faculty said.

Fifteen students majored in neuroscience last academic year, the first year the degree was offered, but more than 60 students are currently enrolled in the major, according to institutional data. Faculty in the hearing sciences department in the Columbian College of Arts and Sciences said more students are taking advantage of the departments course offerings as a result of the new major.

Francys Subiaul, an associate professor of speech, language and hearing sciences, said several faculty in the department are involved with the major through teaching courses that could count toward the major or organizing events for neuroscience students and faculty through the GW Mind-Brain Institute, a research center designed for students and faculty members to study cognitive and behavioral neuroscience, according to its website.

These efforts in our department, the MBI and CCAS aim to provide students in GW diverse education and unique research opportunities in the neurosciences, Subiaul said in an email.

Officials announced the creation of two neuroscience majors in April 2018 in the biology and psychology departments to respond to rising interest in the field from students and faculty. Students in the speech, language and hearing sciences major can choose to concentrate in the neuroscience of language and communication, according to the department website.

Subiaul said students in the speech and hearing major who concentrate in neuroscience of language learn the specifics of language structure and how words and phrases can have different meanings based on the person speaking.

Majors in this concentration will prepare students for a variety of health fields including medicine, nursing and speech-language pathology, but also social work, education and business, Subiaul said.

He said the majority of the approximately 40 undergraduates majoring in the department are concentrating in communication sciences and disorders. Nine students majored in speech, language and hearing sciences in 2015, according to institutional data.

Language is among the most human of human behaviors and so the biological and neural basis of this fundamental behavior is inherently interesting to most, Subiaul said in an email.

He said faculty are advertising the departments most popular courses, like Autism and Brain and Language two courses that count toward the neuroscience major and designing social events to draw more undergraduates to major or minor in the department. Faculty are also creating social activities, like showing communication-themed films and inviting speakers to campus, throughout the year.

Shelley Brundage, the chair of the department, said the department changed its name from the Department of Speech and Hearing Sciences to better reflect the departments more comprehensive focus and direction.

She said the change recognized the language component of the department, represented by research like Subiauls work on social communication for people with autism.

Our name used to be Speech and Hearing Science, Brundage said in an email. We changed it to Speech, Language and Hearing Sciences to better reflect the variety of research and clinical topics in our discipline.

Course offerings for majors in the department include Language: Structure, Meaning and Use and Speech and Language Disorders, according to the departments website.

Brundage said members of the department are proud of its most recent class of masters students, who graduated from the program in the spring with a 100 percent completion rate. She said the department is currently accepting applications for its doctoral program that will be launched next fall.

The new doctoral program will include courses on psychology, neuroscience and physiology in addition to speech, language and hearing sciences, said James Mahshie, the departments chair in June 2018 when the program was approved.

Chris Dulla, an associate professor of neuroscience at Tufts University, said the most effective way for speech and hearing sciences department faculty to retain students who take classes in the department while pursuing the neuroscience major is to change its courses to focus on topics that students can identify with, like diseases, and provide access to related research opportunities.

University President Thomas LeBlanc has made research one of his biggest priorities since arriving on campus a few years ago. Research is one of the four pillars underpinning the Universitys next five-year strategic plan.

Most important, if the students can get lab-related research experiences, they will catch the neuroscience bug and will find that we as a field dont know the answers to many of their questions, Dulla said in an email.

This article appeared in the November 21, 2019 issue of the Hatchet.

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Speech and hearing department reaps benefits of new neuroscience major - GW Hatchet

The brains behind Mathematica, Wolfram|Alpha, and the Wolfram Language talks about how programming languages need to develop.

Image: Wolfram Research, Inc.

When it came to figuring out which computer scientist should help linguists decipher inscrutable alien texts, it was Stephen Wolfram who got the call.

Sure, these extraterrestrials may only have existed in the sci-fi movie Arrival, but if ET ever does drop out of orbit, Wolfram might well still be on the short list of people to contact.

The British-born computer scientist's life is littered with exceptional achievements -- completing a PhD in theoretical physics at Caltech at age 20, winning a MacArthur Genius Grant at 21, and creating the technical computing platform Mathematica (which is used by millions of mathematicians, scientists, and engineers worldwide), plus the Wolfram Language, and the Wolfram|Alpha knowledge engine.

His role advising for Arrival came out of the blue, when what he says was an interesting script crossed his desk with a request for help in consulting and creating some visuals for the soon-to-be-shot movie.

While Wolfram's involvement was mostly advising on some of the science and technical references in the script, his son Christopher was charged with devising a way in which linguists might decode these alien writings with next to no frame of reference, which meant the Wolfram Language also got some screen time.

At points during the film you can see Wolfram Language code being run as it deconstructs the alien logograms, slicing them up to help the on-screen linguists infer meaning from common patterns.

"The thing that was interesting is it's an alien first-contact story, and it's all about language and how we understand things," says Wolfram, explaining why he and his son took up the offer.

"Since I've spent much of my life as a computational language designer, I better be interested in how one can communicate thoughts with things like language."

SEE:How to build a successful developer career (free PDF)(TechRepublic)

For all his other achievements, Wolfram is probably best known for launching Wolfram|Alpha, the computational knowledge engine that underpins Apple's Siri digital assistant's ability to answer questions from "What's the tallest building in the US?" to "How many days until Christmas?".

Wolfram|Alpha has a grand mission: To make it possible to answer any question, immediately and automatically from accumulated knowledge of our entire civilization. An engine that doesn't simply direct users to a particular web page, but that comes to answers by computing them using models, built-in algorithms, and trillions of pieces of curated data.

While a search engine mostly serves up web pages as answers to questions, Wolfram|Alpha takes a different route, dynamically calculating the answer so that the answer to "Where is the International Space Station?" will be different each time, depending on where it actually is at that time.

Wolfram|Alpha can help with queries across a wide range of disciplines, from algebra to physics, food and nutrition to personal health. All of these capabilities involved building in the models needed to compute the problems, as well as gathering and curating the data needed to run these calculations.

Another way of looking at it: Google is, at its most basic, a magnifying glass for finding particular bits of text on the web, and giving you lots of options as to which might be the right one. Wolfram|Alpha is a Swiss Army knife, filled with tools aimed at helping you find the single answer to a question.

And yet, perhaps because we've been trained by years of googling to look at knowledge in particular ways, Wolfram|Alpha probably isn't for everyone. While it can work out the orbital path of the Hubble space telescope, or the number of pennies to cover two square miles, it has a harder time with questions like "Which are the best coffee shops in Shoreditch?".

That's not to say it is entirely humourless; if asked, it will deny that it is Skynet, noting "Unlike Skynet I enjoy interacting with humans in ways that do not involve the launching of nuclear missiles," and will give you an estimate of the number of alien civilizations in the Milky Way (10).

Since its launch in May 2008, as well as fuelling Siri, Wolfram|Alpha has been added into chatbots, tutoring systems, and smart TVs. It was announced in January 2019 that Wolfram|Alpha would provide some of its intelligence to Amazon's Alexa, allowing that digital assistant to answer questions like "Alexa, how many cups does 12 tablespoons make?," or "Alexa, how far is the Voyager 1 satellite from Earth?".

As well as the public Wolfram|Alpha, there are enterprise versions that can answer questions using not only public data and knowledge, but also the internal data and knowledge from those organizations.

Wolfram|Alpha is in turn underpinned by Wolfram Language, a project that has been running through most of Wolfram's life. Wolfram Language effectively allows questions asked using natural language to be understood by a computer.

Wolfram|Alpha is now over a decade old. While it hasn't overtaken Google and still looks very complicated to the average new users, that hasn't dimmed Wolfram's ambition for it.

"What should Wolfram|Alpha know about? My goal has always been to have it eventually know about everything. But obviously one's got to start somewhere," he said earlier this year.

The path that led to Wolfram Language and Wolfram|Alpha is long and winding.

As a schoolboy his first love was physics, with Wolfram possessing a precocious talent that saw him publish his first scientific paper at age 15.

While he first saw a computer 50 years ago, at the age of 10, he wasn't enthralled straight away, initially seeing the machine as a useful tool for exploring his interest in physics.

"The first computer that I actually touched with my own hands was probably in 1972 or 1973, it was a thing called the Elliott 903, a British computer that's long extinct and rather exotic, the size of a large desk and programmed with paper tape," he says. "I always viewed it as being a tool for doing stuff that I was interested in, and I tried to simulate physics on the computer."

It was several years later that Wolfram began to develop an interest in computations and how computers worked, when studying particle physics at Caltech in 1979.

"I did a lot of programming computers to carry out some of the mathematical calculations you need for physics," he says.

"In 1979 I started building my first computer language, which was intended to be a language for doing computations you need in science. But I went back and tried to understand more about the nature of computation, in order to design the most general language. So that caused me to kind of go back and study mathematical logic and the origins of computing and so on," he says.

Wolfram co-designed a computer algebra system called SMP, a process he found useful when he started building Wolfram Language several years later.

At the same time Wolfram remained interested in how computers could simulate phenomenon such as the Big Bang and early galaxy formation, as well as neural nets, an idea that has taken off in the past decade thanks to advances in processing power and availability of training data.

It was studying how complex behavior could arise from simple rules that led Wolfram to what he considers one of his most significant discoveries, made while scrutinizing one-dimensional cellular automata.

Cellular automata offer a model for showing how simple rules determine the behavior of a system, with some rules resulting in complex and seemingly random outcomes. The importance of cellular automata hit home for Wolfram when he discovered "rule 30", which he calls "probably the single most surprising scientific discovery I had ever made".

The illustration below is created using rule 30 and begins with a grid of empty cells. Starting with a single black cell in the center of the top line in the grid, the rule stipulates whether cells in each subsequent line should be shaded black or left empty, depending on the color of the cells around them. From just four lines of instructions in rule 30, irregular and complex patterns emerged, a discovery that led Wolfram to argue "it is this basic phenomenon that is ultimately responsible for most of the complexity we see in nature".

This illustration is created using rule 30, which Stephen Wolfram calls "probably the single most surprising scientific discovery I had ever made".

Image: Stephen Wolfram, LLC

"I was studying these different examples of how you could make complex behavior, and I thought 'Let's try and make the simplest possible model that can capture the essence of what's going on in these different systems.'"

Wolfram set out his arguments that the complexity of the natural world -- even the formation of the universe itself -- could spring from these very simple rules in A New Kind of Science, a best-selling book he spent more than a decade working on, living "as something of a hermit", before publishing it in 2002.

The book, with its bold ambition to "transform science", proved divisive, with some praising it for being a "first-class intellectual thrill", while others felt it was too speculative and didn't properly acknowledge how it built on earlier discoveries.

"Some people were like: 'Oh great, a new thing, we're so excited,' and other people were like, 'Oh no, no, we don't want anything new. We're just fine doing science or whatever it is the way we've done it for the last few hundred years'," says Wolfram.

Stephen Wolfram's bookA New Kind of Science

Image: Wolfram Science

His recollection of the time and effort it took to write the book is aided by the trove of data he's captured on the minutiae of his life for more than three decades. The number of steps he's taken, how many emails he's sent and received, the meetings he's had, and every keystroke he's typed -- more than 100 million.

Doing so has allowed Wolfram to interrogate his past in unusual detail, and spot interesting patterns such as the dip in meetings when he took time out to write A New Kind of Science or how many new words are cropping up in his correspondence.

"Every so often there's something interesting that I want to look up about myself and then, as I passively collect tons of data because it's easy to do, very occasionally I'll want to answer some question, and then go and figure it out from that data," he says.

"I've realized that the main compensation for getting old is that you lived longer, so you know more stuff, you've experienced more things. The way that you really take advantage of that is to have good access to that whole history of yourself. At a meta level, that's the thing that I only really realized this comparatively recently."

Since A New Kind of Science was published, Wolfram says an increasing number of models of human behavior and physical systems are built around this idea of a "computational universe".

"It was interesting to me, the paradigm shift of thinking about things computationally, rather than mathematically," he says.

"In the last 15 years or so, if you look at new models that people make of things, whether they're of behavior of humans on the web or about plants -- whatever it is -- the vast majority of those new models are made in terms of programs, not in terms of mathematical equations."

To tap into the power of this computational universe, Wolfram says what's needed is what he calls a "computational language".

"It so happens that I've spent the last three at least decades working on building this computational language that we call Wolfram Language that is an effort to try to be able to express computationally anything about the world," he says.

Wolfram Language draws upon much of the same underlying technologies as Mathematica and is the basis of Wolfram|Alpha.

Wolfram has described Wolfram Language as a "knowledge-based language" that has built into it "a vast amount of knowledge about how to do computations".

"So, right within the language there are primitives for processing images or laying out networks or looking up stock prices or creating interfaces or solving optimization problems," he said.

This broad sweep of built-in capabilities gives Wolfram Language abilities that aren't found in most other languages out of the gate; for example, typing currentImage[] captures the current image from the computer's camera. As such, the language can natively handle a wide range of data, everything from written language to geographic information, and visualize that data using relatively few lines of code.

But it was Wolfram Language's educational and mathematical focus that led to it being bundled with the official operating system for the $35 Raspberry Pi. The Raspberry Pi is designed to be a low-cost computer aimed at teaching kids about computers, and the Pi's official Raspbian OS bundles Wolfram Language alongside many other tools for learning about programming, ranging from Python to the drag-and-drop language Scratch.

SEE: Raspberry Pi: More must-read coverage (TechRepublic on Flipboard)

Wolfram Language has limitations, and has been described by some users as better suited to solving a wide range of predetermined tasks, rather than being used to build software. It also seems there is still a way to go for Wolfram Language it didn't, for example, feature in the IEEE's recent list of top programming languages.

Wolfram has said that Wolfram Language is not just a language for telling computers what to do, but a way for both computers and humans to represent computational ways of thinking about things.

Of late Wolfram has been more bold in how he talks about Wolfram Language, describing it as a "computational language" that could even help bridge the gulf between ourselves and future non-human intelligences, be they artificial intelligence (AI) or extraterrestrial.

As esoteric a pursuit as it might seem, Wolfram believes the need for this lingua franca is timely, as machine-learning systems increasingly make decisions about our lives -- whether that's screening loan applications today or maybe even choosing whether to kill people tomorrow.

"One of the places where that's important is in expressing the computational thoughts that might define the overall behavior of AI," he says, adding that Wolfram Language "gives one a language in which to express computational thoughts".

The focus on abstracting away much of the underlying technical detail in Wolfram Language -- the nitty-gritty of how a computer is instructed to check stock prices online -- also reflects Wolfram's view of what computing should be for most users.

He's skeptical of the recent push towards teaching more people to code for getting too bogged down in minutiae such as programming language syntax and control flow statements, the implementation details he feels aren't interesting to most users.

"We're now on about the fourth wave of attempts to teach programming/coding to kids," he says.

"The problem is that teaching raw programming, rather than computation about things, is ultimately rather boring to most people."

The majority would be better served by tools that allowed them to use computers to do whatever they're interested in, Wolfram believes.

"The interesting stuff tends to be the computational X, where X is whatever you might care about, whether it's journalism or literature or art history or whatever it is," he says."That's the place where most people are going to want to go."

Stephen Wolfram's new book Adventures of a Computational Explorer -- a series of essays in which he explores science, technology, AI, and language design -- is available now.

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Stephen Wolfram on the future of programming and why we live in a computational universe - TechRepublic

EL PASO, Texas El Pasos new FBI chief is worried about an old problem: advances in encryption technology that may allow criminals to plot or commit crimes with impunity.

Something that concerns not just the FBI but all law enforcement is what we call lawful access. Technology companies are deploying encryption software in which the customer can encrypt and only (they) and the end-user can access, said Luis M. Quesada, special agent in charge of the El Paso Field Office as of this month.

Encryption is useful when it comes to protecting private information like banking, he said, but unrestricted use of this technology could pose a threat to the public. It means we couldnt follow kidnappings, child pornography, terrorist acts the lone terrorist shooters which usually communicate through (digital) platforms, he said.

One example cited is the Sutherland Springs, Texas, shooting, in which a gunman killed 26 people and left 20 others injured at First Baptist Church. The shooters phone was encrypted and police didnt at the time have the technology to find out if he had co-conspirators.

We want to know if the shooter was communicating with somebody else, if he was being radicalized. It could lead us to somebody else to prevent the next event. Or if we arrest a child pornographer wed like to know who hes communicating with so we have a map of who hes (talking to) and save more kids, Quesada said. He suggested the problem could be addressed through legislation of these technologies.

Quesadas comments on Tuesday echoed concerns expressed in July by Attorney General William P. Barr and, more recently, the International Criminal Police Organization (INTERPOL). Some of it centers around Facebooks plan to provide state-of-the-art encryption on messages in all of its platforms, but concerns other companies applications as well.

At the July technology conference at Fordham University, Barr noted that one Mexican drug cartel was using WhatsApp as its privacy communication method to keep U.S. authorities from finding out when the next fentanyl shipment would be sent across the border.

Efforts to curb unfettered access by the general public to encrypted technology go back to the Obama administration and further. Back in 2015, then-FBI Director James B. Comey warned the Senate Judiciary Committee that malicious actors could take advantage of Web technology to plot violent crimes, steal private information or sexually abuse children. Back then the catchphrase wasnt lawful access, but instead going dark.

Former El Paso Border Patrol Sector Chief Victor M. Manjarrez said law-enforcement officials have been fighting criminals use of technology since the days of two-way handheld radios.

We came across encrypted radios used by drug traffickers in Southern Arizona in the early 2000s. You could hear them talking but couldnt (make out) the words, he said.

Manjarrez, now associate director of the Center for Law & Human Behavior at the University of Texas at El Paso, said even if Congress were actually cooperative with each other and restricted encrypted technology, organized criminals will eventually find a way to defeat it.

The problem is that technology changes so fast that transnational criminal organizations can overcome obstacles much quicker than we can change or legislate policy, he said.

Manjarrez said the only way law-enforcement agencies can prevent crimes shielded by technology is to be proactive.

Law-enforcement by nature is reactive. At some point we need to decide we have to be proactive. Just like the Department of Defense in terms of counterterrorism, they seek out the threats. At some point, I think, well have to accept that in law enforcement, he said.

Visit the BorderReport.com homepage for the latest exclusive stories and breaking news about issues along the United States-Mexico border.

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FBI worried about criminals access to encryption technology - fox5sandiego.com