New York City rentals (Credit: iStock)

Digital marketplace gets a boost

CRE digital marketplace CREXi nabbed $30 million in a Series B round led by Mitsubishi Estate Company, Industry Ventures, and Prudence Holdings. The new funds will help them build out a subscription service aimed at brokers and an analytics service that highlights trends in the industry. The company wants to become the go-to platform for every step in the CRE process, from marketing to sale.

Dude, wheres my tech-fueled hotel chain?

Ashton Kutchers Sound Ventures and travel-focused VC firm Thayer Ventures have gotten behind hospitality startup Life House, leading a $30 million Series B round. The company runs a boutique hotel chain as well as a management platform, which gives hotel owners access to AI-based pricing and automated financial accounting. Life House has over 800 rooms across cities such as Miami and Denver, with plans to expand to 25 hotels by next year.

Working from home

As the deadly Coronavirus virus outbreak becomes more serious with every hour, WeWork said it is temporarily closing 55 locations in China. The struggling co-working company encouraged employees at these sites to work from home or in private rooms to keep from catching the virus. Also this week, the startup closed a three-year deal to provide office space for 250 employees of gym membership company Gympass, per Reuters. WeWorks owner SoftBank is a minority investor in Gympass so it looks like Masa Sons using some parts of his portfolio to prop up others.

300,000

Thats how many listings rental platform/flatmate matcher Badi has across London, Berlin, Madrid, and Barcelona. Barcelona-based Badi claims to use machine-learning technology to match tenants and rooms. Badi plans on hopping across the pond to New York City within the year. Its an interesting market for the Barcelona-based company to enter. Though most people use a platform like StreetEasy to find an apartment with a traditional landlord, few established companies have cracked the sublet game without running afoul New York Citys rental laws. In effect, Badi would likely be competing with Facebook groups such as Gypsy Housing plus wanna-be-my-roommate startups like Roomi and SpareRoom. Badi is backed by Goodwater Capital, Target Global, Spark Capital and Mangrove Capital. The firm has raised over $45 million in VC funding since its founding in 2015.

Pink slips at Compass

Uh oh, yet another SoftBank-funded startup is laying off employees. Up to 40 employees of tech brokerage Compass in the IT, marketing and M&A departments will be getting the pink slip this week. Sources told E.B. Solomont that the nationwide cuts are a part of a reorganization to introduce a new Agent Experience Team that will take over onboarding and training new agents from former employees. Its a small number of cuts compared to the 18,000 employees Compass has across the U.S. but it isnt a great look in todays business climate.

Getting ready to move

As SoftBank-backed hospitality startup Oyo continues to cut back, their arch nemesis RedDoorz just launched a new co-living program in Indonesia. Theyre targeting young professionals and college students with the KoolKost service, dishing out shared units with flexible leases and free WiFi. Their main business, like Oyo, is running a network of budget hotels across Southeast Asia. Well see if co-living will help them avoid some of Oyos profitability problems.

Homes on Olympus

Its no secret that it can be a pain to figure out a place to live when work needs you to move to a new city for a bit. You can take your pick between bland corporate housing and Airbnbs designed for quick vacations. Thats where Zeus comes in (not with a thunderbolt but with a corporate housing platform.)

Zeus signs two-year minimum leases with landlords, furnishes the apartments with couches meant to look chic, and rents them out to employees for 30 days or more. They currently manage around 2,000 furnished homes with the goal of filling a newly added apartment within 10 days.

The corporate housing is a competitive space with startups like Domio and Sonder also trying to lure in business travelers. Youd think that Zeus would have to go one-on-one with Airbnb but the two companies actually have a partnership. The short-term rental giant lists Zeus properties on its platform and invested in the company as a part of a $55 million Series B round last month. Theyre trying to keep competition close.

Punch lists go digital

Home renovations platform Punch List just scored $4 million in a seed round led by early stage VC funds Bling Capital and Bedrock Capital, per Crunchbase. The platform lets homeowners track project progress and gives contractors a place to send digital invoices, all on a newly launched app. They want to make as much of the frustrating process of remodeling as digital as possible.

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Euro machine learning startup plans NYC rental platform, the punch list goes digital & other proptech news - The Real Deal

A multidisciplinary UB research team has received an $800,000 grant to develop a machine learning system that could eventually help caseworkers and human services agencies determine the best available services for the more than 20,000 youth who annually age out of foster care without rejoining their families.

The National Science Foundation and Amazon, the grants joint funders, have partnered on a program called Fairness in Artificial Intelligence (FAI) that aims to address bias and build trustworthy computational systems that can contribute to solving the biggest challenges facing modern societies.

Over the course of three years, the UB researchers will collaborate with the Hillside Family of Agencies in Rochester, one of the oldest family and youth nonprofit human services organizations in the country, and a youth advisory council made up of individuals who have recently aged out of foster care to develop the tool. They will also consult with national experts across specializations to inform this complex work.

Researchers will use data from the Administration on Children and Families (ACF) federally mandated National Youth in Transition Database (NYTD) and input from collaborators to inform their predictive model. Each state participates in NYTD to report the experiences and services used by youth in foster care.

The teams three-pronged goal is to use the experiences of youth, case workers and experts in the foster care system to identify the often hard-to-find biases in data used to train machine learning models, to obtain multiple perspectives on fairness with respect to decisions about services and to then build a system that can more equitably and efficiently deliver services.

Social scientists have long considered questions of fairness and justice in societies, but beginning in the early part of the 21st century, there was growing awareness of how computers might be using unfair algorithms, according to Kenneth Joseph, assistant professor in the Department of Computer Science and Engineering and one of the co-investigators of the project.

Joseph is an expert in machine learning who focuses much of his research on better understanding how biases work their way into computational models, and how to understand and address the social and technical processes responsible for doing so.

Machine learning is any computer program that can help extract patterns in data. Unsupervised learning identifies patterns, while supervised learning tries to predict something based on those patterns.

Our supervised problem is to take the information available about a particular child and make a prediction about how to allocate services, says Joseph. Our goal is to help social workers identify youth who might benefit from preventative services, while doing so in a manner that participants within the system feel is fair and equitable.

We also want our approach to have applications beyond foster care, so that eventually our approach can be used in other public service settings.

A machine learning models greatest asset, however, might also be its greatest liability. Machine learning algorithms learn from no other source other than the data theyre provided. If the original data is biased, Joseph says the algorithm will learn and echo those biases.

For instance, models for loan distribution derived from data that gives income- and geography-based preferences to applicants could be using information with inherent race, ethnicity and gender disparities.

There are many ways algorithms can be unfair, and very few of them have anything to do with math, says Joseph.

Finding and correcting those biases raises questions about using computers to make decisions affecting what is already a vulnerable population.

By age 19, 47% of foster care youth who have not been reunited with their families have not finished high school, 20% have experienced homelessness and 27% of males have been incarcerated, according to the AFCs Childrens Bureau.

But Melanie Sage, assistant professor in the School of Social Work and another of the grants co-principal investigators, says this project is about providing caseworkers with an additional tool to help inform not replace their decision-making.

We never want algorithms to replace the decisions made by trained professionals, but we do need information about how to make decisions based on likely outcomes and what the data tell us about pathways for children in foster care, she says.

Sage says their work on this grant is critical given the generational impact caseworkers and agencies have on the lives of foster youth.

When a determination is made that services should be provided for protection because kids are not better off with their families, those kids are deserving of the best services and interventions that the child welfare system can offer, she says. This research ideally gives us another tool that helps make that happen.

The projects other co-investigators are Varun Chandola, assistant professor of computer science and engineering; Huei-Yen Chen, assistant professor of industrial and systems engineering; and Atri Rudra, associate professor of computer science and engineering.

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UB receives $800,000 NSF/Amazon grant to improve AI fairness in foster care - UB Now: News and views for UB faculty and staff - University at Buffalo...

Machine learning models require human labor for data labeling

The hidden secret of artificial intelligence is that much of it is actually powered by humans. Well, to be specific, the supervised learning algorithms that have gained much of the attention recently are dependent on humans to provide well-labeled training data that can be used to train machine learning algorithms. Since machines have to first be taught, they cant teach themselves (yet), so it falls upon the capabilities of humans to do this training. This is the secret achilles heel of AI: the need for humans to teach machines the things that they are not yet able to do on their own.

Machine learning is what powers todays AI systems. Organizations are implementing one or more of the seven patterns of AI, including computer vision, natural language processing, predictive analytics, autonomous systems, pattern and anomaly detection, goal-driven systems, and hyperpersonalization across a wide range of applications. However, in order for these systems to be able to create accurate generalizations, these machine learning systems must be trained on data. The more advanced forms of machine learning, especially deep learning neural networks, require significant volumes of data to be able to create models with desired levels of accuracy. It goes without saying then, that the machine learning data needs to be clean, accurate, complete, and well-labeled so the resulting machine learning models are accurate. Whereas it has always been the case that garbage in is garbage out in computing, it is especially the case with regards to machine learning data.

According to analyst firm Cognilytica, over 80% of AI project time is spent preparing and labeling data for use in machine learning projects:

Percentage of time allocated to machine learning tasks (Source: Cognilytica)

(Disclosure: Im a principal analyst at Cognilytica)

Fully one quarter of this time is spent providing the necessary labels on data so that supervised machine learning approaches will actually achieve their learning objectives. Customers have the data, but they dont have the resources to label large data sets, nor do they have a mechanism to insure accuracy and quality. Raw labor is easy to come by, but its much harder to guarantee any level of quality from a random, mostly transient labor force. Third party managed labeling solution providers address this gap by providing the labor force to do the labeling combined with the expertise in large-scale data labeling efforts and an infrastructure for managing labeling workloads and achieving desired quality levels.

According to a recent report from research firm Cognilytica, over 35 companies are currently engaged in providing human labor to add labels and annotation to data to power supervised learning algorithms. Some of these firms use general, crowdsourced approaches to data labeling, while others bring their own, managed and trained labor pools that can address a wide range of general and domain-specific data labeling needs.

As detailed in the Cognilytica report, the tasks for data labeling and annotation depend highly on the sort of data to be labeled for machine learning purposes and the specific learning task that is needed. The primary use cases for data labeling fall into the following major categories:

These labeling tasks are getting increasingly more complicated and domain-specific as machine learning models are developed that can handle more general use cases. For example, innovative medical technology companies are building machine learning models that can identify all manner of concerns within medical images, such as clots, fractures, tumors, obstructions, and other concerns. To build these models requires first training machine learning algorithms to identify those issues within images. To train the machine learning models requires lots of data that has been labeled with the specific areas of concern identified. To accomplish that labeling task requires some level of knowledge as to how to identify a particular issue and the knowledge of how to appropriately label it. This is not a task for the random, off-the-street individual. This requires some amount of domain expertise.

Consequently, labeling firms have evolved to provide more domain-specific capabilities and expanded the footprint of their offerings. As machine learning starts to be applied to ever more specific areas, the needs for this sort of domain-specific data labeling will only increase. According to the Cognilytica report, the demand for data labeling services from third parties will grow from $1.7 Billion (USD) in 2019 to over $4.1B by 2024. This is a significant market, much larger than most might be aware of.

Increasingly, machines are doing this work of data labeling as well. Data labeling providers are applying machine learning to their own labeling efforts to perform some of the work of labeling, perform quality control checks on human labor, and optimize the labeling process. These firms use machine learning inferencing to identify data types, things that dont match the structure of a data column, potential data quality or formatting issues, and provides recommendations to users for how they could clean the data. In this way, machine learning is helping the process of improving machine learning. AI applied to AI. Quite interesting.

For the foreseeable future, the need for human-based data labeling for machine learning will not diminish. If anything, the use of machine learning continues to grow into new domains that require new knowledge to be built and learned by systems. This in turn requires well-labeled data to learn in those new domains, and in turn, requires the services of the hidden army of human laborers making AI work as well as it does today.

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The Human-Powered Companies That Make AI Work - Forbes

LONDON--(BUSINESS WIRE)--The deep learning market is expected to grow by USD 7.2 billion during 2020-2024, according to the latest market research report by Technavio. Request a free sample report

Deep learning is popularly used in machine learning, which involves the use of artificial neural networks with several degrees of layers. Moreover, massive volumes of digital data that is produced at an unprecedented rate across industries is widening the application area of deep learning. In the healthcare industry, deep learning applications are used in drug research and development. Also, deep learning helps in training machines to understand the complexities associated with languages such as syntax and semantics and generating appropriate responses. Other application areas of deep learning are fraud detection, visual recognition, logistics, insurance, and agriculture. Thus, the growing applications of deep learning are expected to drive market growth during the forecast period.

To learn more about the global trends impacting the future of market research, download a free sample: https://www.technavio.com/talk-to-us?report=IRTNTR41147

As per Technavio, the growing emphasis on cloud-based deep learning will have a positive impact on the market and contribute to its growth significantly over the forecast period. This research report also analyzes other significant trends and market drivers that will influence market growth over 2020-2024.

Deep Learning Market: Growing Emphasis On Cloud-Based Deep Learning

Cloud computing is considered an appropriate platform for deep learning as it provides support for scalability, visualization, and storage of vast amounts of structured and unstructured data. The use of cloud computing in deep learning allows the integration of large datasets for training algorithms. Moreover, cloud computing also allows deep learning models to scale efficiently and at a much lower cost. Thus, the popularity of cloud-based deep learning is increasing, which will have a positive impact on the growth of the market during the forecast period.

Increasing collaboration among vendors and the rising investments in deep learning will have a significant impact on the deep learning market growth during the forecast period, says a senior analyst at Technavio.

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This market research report segments the deep learning market by type (software, services, and hardware), and geographic segmentation (APAC, Europe, MEA, North America and South America).

North America region led the deep learning market in 2019, and the region is expected to register the highest incremental growth during the forecast period. This can be attributed due to factors such as the increasing use of deep learning in various industrial applications such as voice recognition and image recognition.

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Some of the key topics covered in the report include:

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Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions.

With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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Global Deep Learning Market 2020-2024 | Growing Application of Deep Learning to Boost Market Growth | Technavio - Business Wire

Everyone knows the theory behind the wisdom of crowds namely that pooling judgments from multiple individuals can lead to greater accuracy is often exhibited when you try to guess how many jelly beans are in the jar. Ask 500 people and no one gets it exactly right, but take the average of the 500, and the number tends to be spot on.

Wharton professor Barbara Mellers and doctoral student Ike Silver have some thoughts on what should be the appropriate composition of the crowd, as well as normalizing for its potential for overconfidence.

They say that if the leader of the group is truly knowledgeable, then collaboration works well, but if the person doing most of the talking has his head in a unique configuration, then the likelihood of getting it dramatically wrong is dramatically increased. On the other hand, if you let the group talk to each other first, without a leader, then their errors can become correlated, which is a fancy way of saying that in that case, groupthink tends to take over, amplified by conformity pressures.

The question that Mellers sets up to solve is under what conditions does discussion really help peoples judgment? They tried to understand the confidence calibration, which is a way of measuring the nexus between confidence and accuracy. Remember the famous phrase attributed to many CEOs, Often wrong, but never in doubt.

Silver says, Theres something about talking to other people that makes you really feel like youre getting smarter. Now lets be careful here. On the one hand, it is true that a great group thinking hard and true might find the diamond in the rough, but by the same token, the idea that you actually got smarter, that your IQ increased, is, of course, an illusion. But they argue that feeling smarter does allow you to be more inclusive of the other opinions being voiced.

However, just because the group is talking and coming to a confident consensus does not mean it got the right answer. Maybe the loudest dissident in the group truly is more knowledgeable. He might be disagreeable, but he also might be right. And the mitigating behavior he needs is to demonstrate is self-awareness and reasonable humility to balance the risk of potential arrogance and overconfidence.

The quality that I look for in a leader is the willingness (and to some extent eagerness) to change their mind to change not only when confronted with compelling alternate facts, but to be comfortable continually challenging themselves, seeing if they can find the hidden flaw in their reasoning, to continually test and tease the pieces of the puzzle and to not be afraid to look weak or small by juggling multiple possibilities. They cant all be right all the time, keep testing.

The challenge for the leader is to find a way to have an effective discussion and that starts with assembling the right group to do the talking and thinking. Within that subset is the requirement that confidence and conclusion do not walk in the door initially; rather they emerge and increase as the group gets smarter. The CEO can elect to stay out of the discussion, aggregate the input from several groups and then make the ultimate decision alone. Sort of like the jelly beans in the jar.

I have been thinking hard this year on good decision-making and several of my columns have touched on this theme. The good leader is equally skeptical and agile. Change, of course, is not a weakness if it is the right course. I am constantly asking myself, what did I miss, what is the unintended consequence that has been overlooked or discounted?

Mellers and Silver both stress that the one key critical component that must exist in the discussion model is the need for trust. The participants must trust each other and trust that the group will act in the best interests of solving the problem and that individuals will not act in their own personal best self-interests.

In the end, reaching the right decision always remains a balancing act: The wisdom of crowds, on the one hand, the loud domain expert with certainty, on the other. Imagine the possibilities if the CEO had a third hand.

Rule No. 645: You can always use your toes.

Neil Senturia, a serial entrepreneur who invests in early-stage technology companies, writes weekly about entrepreneurship in San Diego. Please email ideas to Neil at neil@blackbirdv.com.

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Can you improve decision-making with help from the crowd? - The San Diego Union-Tribune

Will you marry me?

I dont know. Whats in it for me?

Anything you want.

In a sequence in Dazed and Confused, incoming freshmen girls are hazed by rising seniors, forced to objectify themselves and pretend to offer up their newly-christened womanhood to sleazy, problematic men.

When a young girl unwittingly falls into the lewd trap, where a senior asks her if she spits or swallows, she walks away dejected. The senior chuckles its only a joke, right? and his friend shakes his head and tells him its degrading and terrible. But he does so between laughs, sociologically confirming the fact that the senior probably shouldnt have done that, but its not like its a big deal.

The nature of the so-called nice guy, the apex of allyship, is a sham. If youve ever claimed you werent like all men, distanced yourself from the uncomfortable yet necessary pushback against male hegemony, thought that anything could now constitute as sexual harassment or laughed off your bros lighthearted rape-culture-encouraging joke (because, hey, its not like you say that kind of stuff) this applies to you. You are also the problem.

There is a distinction to be made here: Nice guys, who are polite and seemingly conscientious, are not always good men. The typical male ally stands with and for women that is, until there arent any women around. In laid-back settings, when hes only surrounded by other men, this self-identifying ally isnt actually an ally at all, passively encouraging misogynistic behavior and language by remaining silent.

Jennifer Brown, a renowned diversity consultant, states that allyship occurs on a spectrum, ranging from men who are apathetic (clueless about gender issues) to those who are aware (having some knowledge on issues but not actively trying to solve them) to active (those who are well-informed and willing to have uncomfortable conversations) to advocate (who actually work to advance womens rights).

The reality is that most men fall into the former categories, proclaiming themselves feminists on the surface yet failing to contribute to or engage in honest, oftentimes uneasy, conversations about privilege a direct result of indoctrination into the cult of hypermasculinity.

Men who stand up for women in the workplace are perceived by both men and women as more submissive, less competent and, through their allyship with women, more feminine, according to a study published by the American Psychological Association. And while most men say they care about gender parity and are working to uplift women, a national survey conducted by Promundo, an organization that promotes gender equality, reveals otherwise. Although 48% of men surveyed said they have become more aware of sexism in the workplace in the past year, approximately 60% of women and men agreed that its rare to see men speak out against it.

This gap in allyship is incredibly harmful, actively undermining hard-fought achievements toward gender equity. When men give into toxic masculinity into bro culture they not only dehumanize themselves through facades of emotional detachment and nonchalance but they also encourage a world where linguistic violence toward women flourishes. As language then influences normative values, shaping sociological interactions, so then is actual violence toward women accepted.

From a sociological standpoint, people especially young people desire a sense of belonging to and acceptance from a community. Within male friend groups, masculinity acts as a toxic constraint on all the choices men make, even those that have seemingly nothing to do with gender. Loyalty is crucial, and this friendship is sealed by an adherence to misogyny, rejection of feminine behavior and passive participation in sexism if not overt aggressive behavior.

The What happens in Vegas, stays in Vegas mentality, the clear delineation between what can be said in public versus the locker room (and the fraternity house, military barracks, Wall Street business meetings, among other male-dominated environments) still governs male interactions, dictating what they can or cant say.

When it comes to oppression, marginalized groups are never given the space to advocate for themselves, never mind the fact that the burden of educating others out of their ignorance should not lie solely on their shoulders. Women have tried, time and time again, to make themselves heard, push themselves into spaces where they are unwanted, all the while meticulously performing gender expression gymnastics to avoid being called bitches.

It is absolutely vital that men begin educating other men.

Willfully engaging in dialogue about how you and your friends are a part of the problem is uncomfortable and takes a level of self-awareness and poise most men dont have. It means losing friends (ones you probably shouldnt want in the first place, though), and it means being feminized something our culture views as the worst thing you can be.

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Why you should be wary of nice guys - Daily Trojan Online

The serious moments take us by surprise. BoJack Horseman. Photograph: Netflix

BoJack Horseman, which came to an end last week, is a Netflix cartoon about talking animals. So why has it come, over the course of its six seasons, to inspire such love and debate? One of the answers to this question lies in the way the script is shifting around what is allowed to be profound. Our lives, lived online and deeply connected at all times, have made us simultaneously more attuned to the significance of the everyday, while also distancing us from things that might appear hokey or overly sincere. We use the therapists office as a structure for setting up Twitter jokes about the ways we are lacking as people. We pepper our everyday speech with words like dissociate and trauma. All this self-awareness is painful, and funny. But Ill be the first to admit that I didnt expect this subtle culture shift to manifest in the form of a talking cartoon horse.

Somehow, the writers pull it off. Part of BoJacks charm is to do with the fact that is tightly scripted full of dynamic, hilarious and profound dialogue. But at the far limits of experience, language is inadequate. So its testament to the visual narrative and the careful pathos of the show that one of the best episodes is scriptless. Fish out of Water, a season 3 episode devoid of dialogue, follows BoJack through an underwater world, perfectly capturing the frustrations of being unable to navigate everyday spaces or communicate adequately with those around you. These frustrations are an inherent part of depression and addiction. Its dreamlike in the way that day-to-day life can be dreamlike with certain mental illnesses: unable to be part of the world, we float away to some other place on the periphery.

Comically, the episode also accurately mimics the frustrations of a life where everything repeatedly goes wrong BoJacks attempts to make things right are thwarted at every turn. Connection is just out of reach. Without giving the ending away, the entire episode also serves as the build-up to one of the funniest, purest punchlines in the shows history.

And thats the success of the show overall. It rarely allows itself to linger for so long on painful existentialism that it cant manage to do the simple job of making audiences laugh; whether thats through the use of unexpected plot twists, animal puns, or the slapstick humour of watching a hammerhead shark trying to hammer a nail using his actual head.

The form lends itself to this sort of comedy, and its this use of juxtaposition that means the serious moments take us by surprise, and cut deep. I feel like I was born with a leak, BoJack says. And any goodness I started with just slowly spilled out of me, and now its all gone. And Ill never get it back in me. Its too late. Life is a series of closing doors, isnt it? He says he doesnt know how people get up every day and live, and yet every episode there he is doing just that. Making jokes, having sex, behaving in ways that he is expected to behave and then being punished for it. Thats the beauty of the sitcom set-up. Life is like that, isnt it? A series of episodes of varying success.

There are still questions about how this show gained such an obsessively cultish following. Do we find solace in BoJacks self-obsession because it excuses our own? Are we desperate to see the cartoonish aspire to be profound? Perhaps! Probably! Isnt that what art is all about? When I make a pilgrimage to stand in a room full of paintings by Rothko and feel something, I do it with the self-importance that we tend to assign to highbrow pursuits. When I am watching the misadventures of a cartoon horse, and I am suddenly face to face with myself, that takes me by surprise, and I am moved deeply and without warning.

I know BoJack. I know where hes from, who brought him up, what he has done, the thoughts that plague him. Its all monumentally shitty. And yet, theres no other way his life might have gone, because he sees himself as a person (or rather, a horse-man) without qualities. He expects less than nothing from himself, and when good things happen, he waits for the other shoe to drop. He expects that he will fuck up, and he uses his past to justify his future. And through the ups and downs of knowing BoJack, I have also come to know these things about myself.

Eli Goldstone is the author of Strange Heart Beating

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BoJack Horseman is a cartoon about a horse, so why does it feel so profound? - The Guardian

A true thing: A teacher wrote The Third Rainbow Girl. A true thing: A student wrote it. A true thing: There was a double murder in a field in the United States National Radio Quiet Zone, where the government restricts Wi-Fi and cell towers so a giant satellite dish can track interstellar signals. A true thing: There are no true things.

In her debut work of nonfiction, Emma Copley Eisenberg recounts her time as an AmeriCorps VISTA volunteer teaching writing at a camp for teenage girls in West Virginia. An excerpt from her class notes, included in the book, reads: Does the story have more than one point/idea/theme? Could it be read in more than one way? Does the character have flaws and contradictions? Are all the words carefully chosen? Is every word necessary? Is it physical?

The Third Rainbow Girl ticks all of these boxes.

Its web of complexity stretches from themes of personal and shared experiences, silence in all its permutations, and misogynys place in the groundwater of every American city and every American town, to outsiderness and community, truth and its subjectivity. It can be read as a memoir, as a deeply researched true-crime report, as a work of philosophy. And the language isphysical and visceral in its description of both the corporeal and the psychological. By Eisenbergs own rubric, this book succeeds on many levels.

Eisenberg is a skilled researcher, a truth made clear by the troves of detail about the Rainbow Murders case, expertly laid out in engaging prose. On June 25, 1980, 26-year-old Vicki Durian and 19-year-old Nancy Santomero were murdered in an isolated clearing inside the federal Quiet Zone in Pocahontas County, West Virginia. They were shot at close range. They were hitchhiking to a Rainbow Gathering an annual, loosely knit convergence of a counter-culture group called the Rainbow Family that focused on peace, freedom and respect which in 1980 took place not far from where Nancy and Vicki were killed. The titular third woman is Elizabeth Johndrow, who had been traveling with Vicki and Nancy but decided to skip the gathering at the last minute.

The real third rainbow girl, however, is Eisenberg herself.

The search for justice in the Rainbow Murders case quickly ballooned into a complicated, shifty pursuit. Seven local men with reputations for rowdy drinking were accused in various capacities of having something to do with the crime, but it wasnt until 1993 that a local man, Jacob Beard, was convicted and sent to prison. Later, a serial killer (already imprisoned in a different state) confessed to the murders, and Beard was released. In reporting the many grim details of the case, Eisenberg explores the nature of truth and its connection to the idea of justice; she analyzes the case from the vantage point of storytelling archetypes, psychological theory and, most compellingly, her own shortcomings as an outsider.

Many outsiders narratives have been imposed on Appalachia, something Eisenberg readily acknowledges and grapples with. Despite her love of the place, her years living there and the community with which she shares a deep and complex relationship, she is not from the area. Perhaps in an attempt to reckon with this fact and to be as objective as possible, Eisenberg injects the book with two vital lifelines: her own memoir-esque chapters, and copious historical context. The narrative is expansive, but it doesnt get out of hand. It is engagingly written and well paced. Eisenbergs life in Pocahontas County was complicated by men her familial love of men, and harm experienced because of men.

Harm will always permeate a world with misogyny in the groundwater. I felt harmed and also that I had harmed others with my weakness and my silence and my actions, Eisenberg writes. Things kept returning to me and knocking, demanding to be heard for I was not just a witness but a part of all of it, a person who wanted oblivion for my own reasons.

Oblivion is sometimes preferable to knowledge, but it, too, is ultimately harmful. It is in the relentless pursuit of often unanswerable questions where the narrative becomes queer. While queerness is only explicit in the book a handful of times, the very bones and blood of it the ways in which it looks in all the corners, always asking why is where the authors queer lens shines. Ultimately, the book is about accepting multiplicity and the prismatic nature of truth and justice.

A book like The Third Rainbow Girl is a rare find. Its nuance and self-awareness propel the narrative forward into territory far beyond the black and white. In that sense, it is a rainbow in itself.

_____

The Third Rainbow Girl:The Long Life of a Double Murder in Appalachia byEmma Copley Eisenberg, Hachette, 336 pp., $27

Reading information:Eisenberg will read from Third Rainbow Girl at 7 p.m. on Monday, Feb. 10, at Elliott Bay Book Company,1521 10th Ave., Seattle;206-624-6600;elliottbaybook.com

Sarah Neilson Sarah Neilson is a freelance writer and book critic based in Seattle. Her work appears in Buzzfeed, LA Review of Books, LitHub, The Millions, and Electric Literature, among other outlets. She can be found on Twitter @sarahmariewrote, Instagram @readrunsea, and on her website, sarahneilsonwriter.com.

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The Third Rainbow Girl explores the complicated relationship between truth and justice - Seattle Times

BRANSON, Mo.-- More than 110 human trafficking cases were reported in Missouri in 2019.

Survivors of human trafficking say it's important to speak out about the abuse.

"It felt like I was dead inside like I didn't feel like a human being," said a survivor of human trafficking.

This woman, who has asked to remain anonymous, says she is a survivor of human trafficking. She was six years old when the abuse started. She says her own grandfather sold her and her sister to his brothers and friends for ten years.

"If we didn't come home with the agreed amount, we would be beaten for it," said the survivor.

She wants people to know there are signs to look out for when it comes to spotting victims.

"When you are going through something like that, you are really secluded you don't really talk to anybody because you're scared. there is a lot of physical abuse involved, it's not just sexual," said the survivor.

Tim Easton educates the community about human trafficking through his awareness and prevention course. He says this type of crime does happen more often in the Ozarks than one might think.

"We don't see a lot of it, because it's not obvious, a lot of it is done on-line," said Easton.

Human Trafficking is considered a form of modern slavery in which a person is sold for sex, work, or both. Easton says perpetrators don't discriminate when it comes to picking their victims.

"They are promised money, fancy clothes, nice stuff and it doesn't turn out like that," said Easton.

Easton says it's important to be self-aware, vigilant and to learn self-defense.

"Yell, scream, and call for help," said Easton.

And if you are a victim of this abuse, the survivor says the best way to get help is to find the courage to speak out.

For more information, click here.

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Safety experts educate on human trafficking in the Ozarks - KY3

While it is guided by our internal values, our purpose should be outwardly directed and focused to what we can contribute to the world. A purpose bounds our lives, and boundaries bring freedom. Without boundaries we would never know where to begin and end.

We would have no idea how to direct our attention and actions, nor on what we should focus. When we live according to our purpose, we are taking all of your lifes energy and dedicating it to achieving a particular end. That is a powerful way to live.

People with a purpose are more resilient and are actually in better health than those without a purpose in life. Our lives need structure, and purpose gives it that structure.

Living by our values brings peace of mind. A purpose informs us of who we are, informs others of what we are about, and helps us ensure we are on the right path, doing the right work. Purpose allows us a new and deeper level of self-awareness and social awareness.

Self-awareness comes when we take the time to mindfully reflect on and exam our actions and honestly appraise whether they are in-line with our purpose, and living purposefully brings with it a social-awareness of how we fit in with and relate to the larger world. Are we trying to live sober but are surrounded by patiers?

Do we want to live a life devoted to justice, but work in a job that does not treat people, animals, and/or the environment in a just way?

No need exists for us to be something other than what we are when we live purposefully.

One invaluable component of living purposefully is mindfulness. Mindfulness can be defined as the purposeful, non-judgemental awareness of what we are experiencing in the present moment.

By living mindfully, we bring awareness to our current state and actions. This awareness allows us to know when we are not living according to our purpose and helps us return to our mission and true selves.

Some studies even suggest that meditators areless depressedand have a greater sense of purpose; that meditation actually helps them find, strengthen, and improve their lifes purpose. A mindful life is a purposeful life.

Respond Do Not React

Reaction is based on habit and is our first emotion about a situation. When we respond we are taking the time to consider the situation completely and give it a response that is appropriate.

When someone approaches us with a job offer for more money or an exciting opportunity, our initial reaction may be Yes! but before we answer we need to pause and consider the situation fully.

Will saying yes to this offer allow us to live more aligned with our purpose? Will this opportunity bring us closer to realizing our lifes vision? If the answer is not yes, then we should respond with a No.

Building a Purposeful Life

The formula for living a purposeful life is pretty simple:

Develop a purpose. Dictated by your values and principles, how do you want to make the world a better place?

Build a vision for your future based on your purpose. Based on your purpose, what is your end state? This should give you hope for the future.

Set and achieve goals that move you toward your future.

What work should you be doing now to move toward your desired future state? Your purpose is what gives you the motivation to get out of bed to work on the goals that move you toward your vision.

Stop to mindfully reflect on where you are going, what you are doing. Are you still heading in the direction of your purpose? Does your purpose still hold meaning for you?

The vision is the what and the purpose is the why. If your purpose and vision are not aligned, then you need to re-think one of the other. But simple is not the same as easy.

Many of us struggle with developing our purpose. First, we need to understand that our purpose and especially how we fulfill that purpose can change throughout our lives.

As we move through the stages of lives single man, husband, father, and grandfather how we find and live our purpose changes. What worked for us at 25 may no longer work at 45.

Second, we must never think our purpose is not good enough or special enough to guide our lives. Making sure that people have working cars or stores to shop in is needed as much as making sure that children and adults never go hungry.

Below are some ways to develop and build your purpose. These are difficult questions to answer, but they are important questions.

What are my values and principles? What do I give the greatest importance to?

What do those values say about me? What themes or ideas can I develop around those values?

How do I represent those values in my daily life? How could I express those values?

What am I really good at? What do I enjoy doing? How can I bind those into a larger purpose for my life?

What is missing in the world? What do I think needs to be done? What do I want to contribute to the world?

What do I want my legacy to be? If my purpose was written on my tombstone, what would it read?

What accomplishments am I most proud of in my life? What activities make me feel the most satisfied? Gives me energy

What activities get me into a state of flow? What do these activities have in common? What do they mean to and say about me?

If everything in my life were to work out perfectly, then what would I be doing in ten years?

Where am I now in relation to my future self? What would it take to get to that state?

Once you have a purpose a grand, overarching reason to be on this earth what can you do that helps you live your purpose and move you toward your vision? Think of these as missions that help you achieve your overall objective. If your purpose it to alleviate suffering in the world, then you can start volunteering at a hospital.

Test out these missions and take time to mindfully reflect on how they worked for you and if you want to continue with them or try another approach.

Living purposefully can also help us live mindfully. When we are fully engaged with meaningful work, we become present and stop wanting to be somewhere else, doing something else, as someone else.

Originally posted here:
How to Be More Mindful - Thrive Global